References

This page lists publications that have used or cited NetLogo software and/or models.

This list is by no means complete or exhaustive. If you are using and/or citing NetLogo in your work, or you know of work that is not listed, please send the relevant citations to [email protected].

Google Scholar's database lists roughly 38,600 NetLogo citations. You can explore it here.

Bold = Publications authored by the CCL

2026202520242023202220212020201920182017201620152014201320122011201020092008200720062005200420032002200120001999

2026

  • Brichev, T. Y. (2026). Agent-based models of regional economic development: from conceptualization to software implementation . Modern Economy Success, (5), 156-162. doi: 10.58224/2500-3747-2026-5-156-162
  • Abdullah, Fatima, Z., Ather, M.A., & Rodríguez, J.L.O. (2026). A novel intelligent hybrid reinforcement learning framework for autonomous decision making in complex health cognitive systems. Scientific Reports, 16(14721). https://doi.org/10.1038/s41598-026-50418-0
  • Abdullah, Fatima, Z., Hafeez, N., Ather, M. A., Tellez, R. Q., Sidorov, G., Sanchez-Mejorada, C. G., & Torres Ruiz, M. J. (2026). Enhancing context-aware SARS disorder management: a proposed multi-agent simulation framework with machine learning and bio-sensor data integration. Frontiers in Medical Technology 8. https://doi.org/10.3389/fmedt.2026.1780837
  • Accolla, C., Glaum, P.R., Galic, N., & Vaugeois, M. (2026). Towards the development of standard models for ecological risk assessment: an agent-based model of fathead minnow. Ecological Modelling, 519. https://doi.org/10.1016/j.ecolmodel.2026.111656
  • Addo, L., Watz, J., Piccolo, J.J., Norrgård, J., Syjränen, J.T., Railsback, S.F., & Hajiesmaeili, M. (2026). Individual-based modeling of Atlantic salmon and brown trout outmigrants in a hydropeaking-regulated river: effects of minimum baseflow and gravel augmentation on smolt production. Global Ecology and Conservation. https://doi.org/10.1016/j.gecco.2026.e04111
  • Adebola, A., Janssen, M.A., Ayeni, A., & Soneye, A.S.O. (2026). How High Concentrations of Financially Enabled Households and Behavior Shape Solar Home Systems Expansion in Underserved Communities in Nigeria: Insights from a Stylised Spatial Agent-Based Expansion Model. SSRN. https://ssrn.com/abstract=6951258
  • Adhikari, P. (2026). Software development practices in simulation modeling: Insights from agent-based NetLogo repositories [Master's thesis, Miami University]. The Graduate School, Miami University.
  • Aini, Q.U.A., Nasri, S., Ahmad, N., Uddin, Z., & Ashfaq, I. (2026). Agent-Based Model for Real-Time Forest Fire, Early Warning and Alerting System. Proceedings of the 2026 International Conference on Robotics and Automation in Industry (ICRAI), IEEE, 1-7. 10.1109/ICRAI70912.2026.11551955.
  • Akbari, M., & Akbarpour Shirazi, M. (2026). A self-adjusting Decision Support System for strategic project portfolio prioritization using integrated AHP and reinforcement learning. Applied Operations and Analytics, 2(1), 1–19. https://doi.org/10.1080/29966892.2026.2694891
  • Al-Najar, A.A.M., Ibrahim, H.M., & Yousif, A.N. (2026). Modeling the Performance Metrics of Packet Scheduling Algorithms in Wireless Sensor Networks. National Journal of Antennas and Propagation, 8(2), 218-224. https://doi.org/10.31838/NJAP/08.02.18
  • Ale, S., Kelleher, J., Nguyen, T. N. Q., & Hunter, E. (2026). An ODD Protocol for an Agent-Based Model of Hepatitis C Virus Transmission with Inter- and Intra-Group Structural and Behavioural Heterogeneous Syringe-Sharing Networks. Technological University Dublin Arrow Repository Report. doi:10.21427/7ya7-xe60
  • Ale, S., Nguyen, Q., & Kelleher, J.D. (2026). An ODD Protocol for an Agent-Based Model of Hepatitis C Virus Transmission in a Two-Group Structural Heterogeneous Syringe-Syringe Sharing Network (M2). Technological University Dublin Arrow Repository. doi:10.21427/6r9q-4928
  • Ale, S., Nguyen, Q.T.N., & Kelleher, J.D. (2026). An ODD Protocol for an Agent-Based Model of Hepatitis C Virus Transmission in a Homogeneous Syringe-Sharing Network. Technological University Dublin Arrow Repository. https://arrow.tudublin.ie/scschcomrep
  • Ali, K.A.R., Somu, C.S., & Bang, S. (2026). Applying a Multi-Agent Simulation Model for Examining Restorative Justice-Based Intervention in the Criminal Justice System: A Legal and Technological Perspective. Proceedings of the 3rd International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS), IEEE. 10.1109/ICETEMS66917.2026.11469421
  • Alkaher, D., Taylor, E.J., Frenkel, M., & Bengo, Y. (2026). Winning the Tug of War in Hierarchical Military Organizations: Achieving Anti-Fragility Through the Institutionalization of Effective Innovation Management Systems. Systems 14(6). https://doi.org/10.3390/systems14060698
  • Alqumber, M.A. (2026). Probiotic-infused activated charcoal/ hydroxyapatite microbeads: a novel strategy to disinfection. PeerJ, 14(e20803). http://doi.org/10.7717/peerj.20803
  • Altaweel, M., & Forte, M. (2026). Archaeological Theory and AI: The Potential Past through Agentic Systems. AI and Society. https://discovery.ucl.ac.uk/id/eprint/10228635
  • Alves, R.B.O., Tomasiello, D.B., de Almeida, C.M., Rosalen, D.L., Pereira, L.H., da Silva, H.P., & Rodrigues, C.L. (2026). Multi-agent simulation of Sphenophorus levis infestation and natural biological control with Beauveria bassiana and Steinernema brazilense in sugarcane cultivation. Ecological Modelling, 518. https://doi.org/10.1016/j.ecolmodel.2026.111627
  • Amin, D., Salminen, J., Jansen, B. J., Kaate, I., & Akhtar, W. (2026). Large Language Models in Human–Computer Interaction: Using LLM-Generated Personas to Model Everything from Minority Views to Entire Ecosystems. In Artificial Intelligence and Large Language Models: A Scientific Perspective (pp. 240-276). CRC Press. https://doi.org/10.1201/9781003492252-10
  • Amoruso, P. (2026). Machine Learning Acceleration of Micro-Credentialing, Knowledge Gap Remediation, and Micro-Recruiting of STEM Assessments: from Learner Data to Hardware [Doctoral dissertation, University of Central Florida]. STARS University of Central Florida. https://stars.library.ucf.edu/gradstudies_etd_2026/13
  • An, G., & Cockrell, C. (2026). Dynamic knowledge representation of blood brain barrier activation, injury and restitution with a novel agent-based model. bioRxiv. https://doi.org/10.64898/2026.07.20.739661
  • Anisi, A. (2026). Using online text data to improve decision-making problems: From multiple generation products pricing and discernment of refurbished product features to supply chain risk exposure quantification using online text data [Doctoral dissertation, Iowa State University]. Iowa State University Digital Repository. https://dr.lib.iastate.edu/handle/20.500.12876/106559
  • Arbuzov, M.L., Bei, S., & Shvets, A. (2026). Estimated Dynamic Equilibrium Model: Supply and Demand as a Sample Path of a Stochastic Process. arXiv. https://doi.org/10.48550/arXiv.2605.15472
  • Assaad, L., Young, D.J., & Hahn, U. (2026). Evidential Dependence and Polarization in Social Networks. In G. Boleda, I. Dautriche, M. Maldonado, C. Saldana and T. Torrent (Eds.), Proceedings of the 48th Annual Conference of the Cognitive Science Society. Retrieved from https://escholarship.org/uc/item/7m26b941
  • Aya, C.F., Opeh, P.B., Ifon, H.T., Ndem, B.E., & Eremi, E.O. (2026). Social-ecological Modelling of Fisheries Extension Workers’ Resilience to Climate Change in Calabar Agricultural Zone, Nigeria. Journal of Agricultural Extension, 30(2). https://dx.doi.org/10.4314/jae.v30i2.5
  • Baccino, L. (2026). Media biases and two-sided news markets : audience, advertising, reputation and platform governance. Economics and Finance. Université Côte d'Azur, https://theses.hal.science/tel-05545290.
  • Badham, J., & Elsenbroich, C. (2026). Becoming an agent-based modeller: Theory and practice of building agent-based models in the Social Sciences. Routledge. https://doi.org/10.4324/9781032615622
  • Baer, M., Allhoff, K.T., Barnes, D.K.A., & Koch, F. (2026). Forecasting bryozoan assemblage dynamics under simulated climate change. Ecological Modelling, 517. https://doi.org/10.1016/j.ecolmodel.2026.111576
  • Bai, Z., Wen, J., Fang, Y., & Sun, X. (2026). Research on Spatial Vitality Mismatch Diagnosis and Targeted Renewal Strategies of Traditional Commercial Villages Based on Multi-Source Data: A Case Study of Lanche Village. Sustainability 18(15). https://doi.org/10.3390/su18157987
  • Bala, R., Manoharan, R., Ramya, G. (2026). Trust models for blockchain networks: a comprehensive review. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-025-05027-6
  • Ballinas-Hernández, A.L., Rangel-Galván, M., Rangel-Galván, V. (2026). Corridor geometry effects on the bidirectional pedestrian walking modeled as a multi-agent system. Physica A: Statistical Mechanics and its Applications 684(131207). https://doi.org/10.1016/j.physa.2025.131207
  • Bao, C., Chen, Y., Tahlyan, D., & Mahmassani, H. S. (2026). Understanding the trade-offs of Telework: An agent-based simulation of hybrid work models and employee behavioural dynamics. Travel Behaviour and Society, 43, 101207. https://doi.org/10.1016/j.tbs.2025.101207
  • Barhoumi, E.M., Charabi, Y., & Farhani, S. (2026). Agent-Based Modeling of Human-Nonhuman Interaction for Resilient Infrastructure Design and Environmental Conflict Management. Journal of Smart Infrastructure and Environmental Sustainability 3(2), 1-9.
  • Barnett, K.M., McMahon, T.A., Shepack, A.D., Buelow, H.N., Barkley, Z., Belsare, A.V., Risin, M., Milloway, O., Carozza, J., Beasley, J., Hobart, B., Moss, W.E., McDevitt-Galles, T., Detmering, S., Hilgendorff, B.A., Nordheim, C.L., Calhoun, D.M., Rohr, J.R., Johnson, P.T.J., & Civitello, D.J. (2026). From protection to amplification: Imperfect chytridiomycosis prophylaxis increases infections in wild amphibians. bioRxiv. https://doi.org/10.64898/2026.05.15.725113
  • Bashiri, H. (2026). Targeted Subsidies Plan: An Agent-Based Modeling Approach. Participatory Modelling and Simulation to Improve AI-based Public Social Services. Artificial Intelligence, Simulation and Society. Springer, Cham. https://doi.org/10.1007/978-3-032-15283-1_6
  • Becerra-Flores, C., Oruro, E. M., Medina-Saldivar, C., Pacheco-Otálora, L. F., & Pardo, G. E. (2026). Postnatal Development of Chloride Cotransporters in the Piriform Cortex and Its Modulation by Early Maternal Care. Developmental Psychology 68(3). https://doi.org/10.1002%2Fdev.70161
  • Beigh, M. J. (2026). Resilience Under Stress: Simulation-Based Evaluation of Disruption-Response in Critical Public-Good Supply Chains. [Doctoral Dissertation, George Washington University]. Available from ProQuest Dissertations & Theses Global. (3325958808).
  • Benisi, A., Psoma, V., Boikos, I., Katsiampoura, G., Toliou, A., Skordoulis, C., & Papanagiotou, A. T. (2026). An advanced form of NetLogo’s Forest Fire model: A teaching approach for Primary School students, regarding Complex Systems. Journal of Physics. Conference Series, 3203(1), 12002. https://doi.org/10.1088/1742-6596/3203/1/012002
  • Bhattacharjee, D., Zijlstra, T.W., Roth, T.S., Belli, E., Chova, P.E., Cousin, E., de Jong, J.A., de Laat, E.J.A.M., Guðjónsdóttir, A.R., Janmaat, K.R.L., Jeunink, E.J., Kluiver, C.E., Kuijer, P.E.N., Middleburg, E., Pflüger, L.S., Schroderus, V.I., van Dijk, E.S.J., Verspeek, J., Waasdorp, S., et al. (2026). Despotism promotes dyadic cooperation through enhanced interdependencies in non-human primate societies. Nature Communications, 7(3513). https://doi.org/10.1038/s41467-026-71168-7
  • Bhattacharya, M. (2026). Herring over the long haul: Exploring the impact of winter closures and reduced fishing caps on herring biomass and catch-related income in the central Baltic. [Master's Thesis, Stockholm University]. Available at Diva Portal 2068004.
  • Bischoff, R.J., & Padilla-Iglesias, C. (2026). Bridging Material Cultural Networks and Social Networks in Archaeology. OSF. https://doi.org/10.31235/osf.io/wjsyg_v2
  • Bisegna, F., Vespasiano, F., Pompei, L., Burattini, C., Belli, E., Bellucci, A. M., Di Vittorio, F., & Blaso, L. (2026). Towards the Decarbonization of Urban Communities: Evaluation of Smart and Green Strategies to Reduce Gas Carbon Emissions. Smart Cities 9(2). https://doi.org/10.3390/smartcities9020026
  • Blachford, A. (2026). Linking extrinsic with intrinsic factors: the effects of stress on reproduction may be adaptive. Canadian Journal of Zoology. https://doi.org/10.1139/cjz-2026-0018
  • Boman, B.M. (2026). Asymmetric Cell Division Interpretation Of Branching Morphogenesis According To Fibonacci P-Number Sequence. The Fibonacci Quarterly. https://doi.org/10.1080/00150517.2025.2591077
  • Boone, R. B., Samanamud, M. Z., Burkhart, S., Kiker, G. A., Senda-Ndlovu, T., & Warrier, R. (2026). Simulation as an approach to pastoral system analyses. In Routledge Handbook of Pastoralism (pp. 326-340). Routledge.
  • Böttcher, L. (2026). Control of dynamical systems with neural networks. Nonlinear Dyn 114(79). https://doi.org/10.1007/s11071-025-11937-z
  • Breen, M. J. (2026). An assessment of the unintended consequences of structural coastal flood protection. [Master's Thesis, Brunel University London]. Brunel University Research Archive. https://bura.brunel.ac.uk/handle/2438/33131
  • Brennan, R.W. (2026). An Agent-Based Model to Assess the Environmental Risks of Hydrogen Transportation. In: Trentesaux, D., Leitão, P., McFarlane, D., Rosin, F., Cardin, O. (Eds), Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2025. Studies in Computational Intelligence, vol 1274. Springer, Cham. https://doi.org/10.1007/978-3-032-18786-4_16
  • Briseño, M.A.H. (2026). Competencias digitales: Una propuesta a partir del uso del modelaje y la simulación educativa. RedCA Journal, 9 (25), 52-87. Retrieved from https://revistaredca.uaemex.mx/article/view/28395
  • Brooks, J.D., Belsare, A.V., Caudell, J.N., & Zollner, P.A. (2026). Harvest increase and culling as tools for managing chronic wasting disease in white-tailed deer. Journal of Wildlife Management. https://doi.org/10.1002/jwmg.70211
  • Caballero, R., Martínez, M.A., Wentzel, J.J., Akyildiz, A.C., & Peña, E. (2026). A personalized mechanobiology-driven multiscale model of atherosclerosis. Computers in Biology and Medicine, 210. https://doi.org/10.1016/j.compbiomed.2026.111715
  • Cabuya-Padilla, D., Díaz-López, D., & Castaneda-Marroquín, C. (2026). MARCIM-WG: A cyber wargame proposal based on math modeling applied in a naval scenario. arXiv. https://arxiv.org/abs/2606.12395
  • Caccin, A., & Stocco, A. (2026). WM-Classroom v1.0: A Didactic Multi-Species Agent-Based Model to Explore Predator–Prey–Harvest Dynamics. Wild, 3(1), 8. https://doi.org/10.3390/wild3010008
  • Calabrò, G., Inturri, G., & Le Pira, M. (2026). Integrating Passenger and Freight Transport in Demand-Responsive Systems: A Novel Agent-Based Modelling Approach. Transportation Research Procedia 95, 752-759. https://doi.org/10.1016/j.trpro.2026.02.095
  • Calero Valdez, A. (2026). Digital Maturity Understanding Human–AI Interaction in Online Social Media. In: Anastasiadis, M., Peters, Y., Thimm, C. (Eds) The Digital Society. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-49118-5_9
  • Calzado-Martínez, C. (2026). Social and Institutional Factors Influencing Restoration Decisions in Sagebrush Plant Communities in the Great Basin. [Doctoral Dissertation, Utah State University]. DigitalCommons@USU. https://digitalcommons.usu.edu/etd2023/730
  • Canessa, E., Chaigneau, S.E., & Lagos, R. (2026). Estimating sample size in conceptual property norms by standardizing coverage. Behavior Research Methods, 58(233). https://doi.org/10.3758/s13428-026-03110-0
  • Carter, D., Lungeanu, A., Pearman, J., Carter, N., Bauer, L., Pendergraft, J., Landon, L., Shuffler, M., DeChurch, L., & Contracto, N. (2026). Collective Attention in Virtual Teams: A Pathway forMitigating Communication Delays. Personnel Psychology. https://doi.org/10.1111/peps.70024open_in_new
  • Carvajal, A. S. B., Muñoz, G. A., & Noel, R. (2026). A Container-Based Software Architecture for Coupling Heterogeneous Agent-Based Evacuation Models using Machine Learning. In Anais do XXIX Congresso Ibero-Americano em Engenharia de Software, p. 264-278.
  • Chaabani, S., Olthof, G., Muller, E. B., Wagner, M., Jaspers, V. L. B., Einum, S. (2026). Extrapolating combined effects of PET microplastics and food availability from the individual to the population level of Daphnia magna using Dynamic Energy Budget Individual-Based Models. Available at SSRN. https://dx.doi.org/10.2139/ssrn.6753288
  • Chao, S., Tao, Y., Zhou, H., & Wang, L. (2026). Technological Progress and its Job Market Impacts: A Basic Simulation Framework for Human-technology Collaboration. Computational Economics. Springer. https://doi.org/10.1007/s10614-025-11273-9
  • Chappell, S.K. (2026). The complexity and dynamics of enhancing endurance performance [Doctoral dissertation, Colorado State University]. Mountain Scholar Digital Collections of Colorado. https://doi.org/10.25675/3.027270
  • Chaturvedi, V. (2026). Geospatial Analysis of Post-Conflict Urban Reconstruction and Recovery in Afghanistan and Iraq (Doctoral dissertation, Universität München).
  • Chen, W., Tang, X., Qi, J., & Liu, H. (2026). Dynamic pricing strategy design for manufacturing service providers in manufacturing platforms. Journal of Industrial and Management Optimization 22(2), 1140-1167.
  • Cheregi, V.I., & Silaghi, G.C. (2026). Agent-based modeling for the study of labor market dynamics. Journal of Economic Interaction and Coordination. https://doi.org/10.1007/s11403-026-00481-7
  • Chitnis, A. (2026). Exploring Self-Organization of Development in the Zebrafish Posterior Lateral Line Primordium with Computer Models. Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a041797
  • Chueca Del Cerro, C. (2026). The echoes from social media: modeling how filter bubbles and network structure shape echo chamber emergence. Information, Communication & Society, 1-22.
  • Cipriano, A.P., Bell, E., & Grüter, C. (2026). Drifting behaviour in social bees: using agent-based modelling to explore the effects of spatial layout on drifting patterns. Available at SSRN: https://dx.doi.org/10.2139/ssrn.6983299.
  • Clariño, M.A.A.D., & Mendoza, E.R. (2026). Agent-Based Model for Auxin-Driven Development of Parallel Leaf Venation Pattern. Proceedings of the International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA 2026). 10.1109/ACDSA67686.2026.11467791
  • Comolli, L. R., & Fassola, H. (2026). Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis. Frontiers in Agronomy 8. https://doi.org/10.3389/fagro.2026.1657465
  • Comolli, L.R., Fassola, H., & Mocenni, C. (2026). Interspecies nonlinear feedbacks drive emergent function in multispecies agroforestry. Preprint available on Research Square.https://doi.org/10.21203/rs.3.rs-9864636/v1.
  • Condie, S. A., Condie, C. M., Andreotta, M., McCrea, R., Melbourne-Thomas, J., & Spillias, S. (2026). Pathways towards social license for offshore wind energy: Modelling strategies to reduce conflict and increase acceptability. Available at SSRN: https://dx.doi.org/10.2139/ssrn.6788901.
  • Cricelli, L., Ianniello, S., Pragliola, P., & Strazzullo, S. (2026). Sustainability impacts of green hydrogen production technology: An agent-based model simulation. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2026.129910
  • Cristofaro, R., Duca, V., Ponsiglione, C., Primario, S., & Strazzullo, S. (2026). Addressing the Complexity of the Green Hydrogen Value Chain: An Agent-Based Modelling Approach. In G. Schiuma, & A. Lerro, IFKAD25 Knowledge Insights: Exploring Knowledge Futures (pp. 428 - 435). Springer.
  • Crouse, K.N., Lehman, C.L., & Wilson, M.L. (2026). Simulating the socioecology of virtual primates using B3GET: a generalized behavioral modeling tool. Individual-based Ecology 2. https://doi.org/10.3897/ibe.2.174844
  • Cui, L., Sun, Q., Yin, X., Yu, W., Cai, W., Zhao, N., Chen, L., Tang, S., Lin, H., Cui, L., Jin, X., Xie, Z., Cummings, S.R., Li, Z., Wang, L., Pang, Q., Chi, Y., Jiajue, R., Liu, W., ... Xia, W. (2026). Cost-effectiveness thresholds for initiating osteoporosis treatment in postmenopausal women in China: a microsimulation analysis based on real-world data. Archives of Osteoporosis 21(94). https://doi.org/10.1007/s11657-026-01722-9
  • Cushway, K.C., Foxfoot, I.R., Rosenberg, R.R., Schwalb, A.N., & Swannack, T.M. (2026). SAMPL: An agent-based model to evaluate spatial sampling strategies. SSRN. https://dx.doi.org/10.2139/ssrn.6890571
  • da Silva Júnior, J.S., & Alves, M.A. (2026). Do Nobels in Education/Teaching exist? A critical look at global prizes. Acta Scientiae, 28(2). 10.64856/acta.scientiae.8498
  • Da Silva, R., (2026). Digital Traceability and Agent-Based Modeling in the Beef Supply Chain in Brazil: Integration between Networks, Sustainability, and Governance of Livestock 4.0. UNIVERSIDADE ESTADUAL DE CAMPINAS (UNICAMP).
  • da Silva, R.F.M. (2026). Rastreabilidade Digital e Modelagem Baseada em Agentes da Cadeia de Comercialização de Bovinos no Brasil: Integração entre Redes, Sustentabilidade e Governança da Pecuária 4.0 [Doctoral thesis, University of Campinas]. UNICAMP. https://hdl.handle.net/20.500.12733/44530
  • Daems, D., & Boogers, S. (2026). Simulating resource exploitation strategies in Iron Age to Hellenistic communities in southwest Anatolia. In M. Gori, C. Von Rüden, & T. Stöllner (Eds.), Resources in Premodern Societies: New Approaches to Lifeworlds, Skills and Complexity (pp. 247-262). Leiden: Sidestone Press.
  • Dagnaw, G., & Muccini, H. (2026). A Cognitive Federated Digital Twin Framework with Self-Learning Agents for Energy-Aware and Sustainable Cultural Heritage Preservation. Available at SSRN. https://dx.doi.org/10.2139/ssrn.7235660
  • Dasoler, B.T., Teixeira, F.Z., Kindel, A., Ascensão, F., & Bastazini, V. (2026). Wildlife road crossings: a mechanistic trait- and landscape-based model. Ecological Modelling, 516. https://doi.org/10.1016/j.ecolmodel.2026.111579
  • Datta, D. J., Rao, R. P., & Ryder, E. F. (2026). Enteroscape: An agent-based model for simulating microbial dynamics and host responses in the gut ecosystem. bioRxiv, https://doi.org/10.64898/2026.01.27.701954.
  • De Cristofaro, R., Ponsiglione, C., & Primario, S. (2026). Green Hydrogen for Public Transportation: Insights From an ABM and From Palma de Mallorca Case Study. Business Strategy and the Environment, 1-26, https://doi.org/10.1002/bse.70761.
  • de Oliveira, L.A., da Silva, A.I.B., & Campos, R.F.D.A. (2026). Impactos do Programa de Aquisição de Alimentos (PAA) na redução da fome e da desigualdade: uma modelagem por agentes e redes sociais. Informe Econômico 52(1). https://doi.org/10.26694/2764-1392.6681
  • De Pretis, F., Pompa, L., & Tortoli, D. (2026). Quantifying the population exposure to employer branding strategies using a Maxwell–Boltzmann distribution-based evolutionary model. Journal of Economic Interaction and Coordination, 21(3), 555–580. https://doi.org/10.1007/s11403-026-00475-5
  • Degenhart, A-L., Burina, E., & Kazakova, M. (2026). Exploring Complexity: A Framework for Cross-Domain Complex System Mapping. Prepint available at ResearchSquare. https://doi.org/10.21203/rs.3.rs-10331875/v1
  • Del Cerro, C.C. (2026). What is Agent-Based Modeling?. GESIS Guides to Digital Behavioral Data, 21. DOI: 10.60762/ggdbd25021.1.0
  • DeLisi, J., Minocha, S., Perret, B., Peterson, K., Lee, I. A., Malyn-Smith, J. (2026). Integrating a New Computational Modeling Curriculum in High School Science Classrooms. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-026-10323-7
  • DeLisi, J., Minocha, S., Perret, B., Peterson, K., Lee, I.A., & Malyn-Smith, J. (2026). Integrating a New Computational Modeling Curriculum in High School Science Classrooms. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-026-10323-7
  • Demir, Damla. (2026). Modeling Retail Store Location Selection using Agent-Based Simulation: A Data-Driven Decision Support Approach. International Journal of Engineering Research and. 15. 1-12. 10.17577/IJERTV15IS070322. 
  • Deng, Y., Wang, S., & Zhang, Z. (2026). From structure to emergence: an agent-based simulation of multi-mechanism configurations in online charitable donation. Journal of Chinese Governance, 1–22. https://doi.org/10.1080/23812346.2026.2656054
  • Di Lernia, G., (2026). Dalla complessità alla sostenibilità: un modello basato su agenti per lo sviluppo e la governance delle Comunità Energetiche Rinnovabili. FACOLTA' DI INGEGNERIA CIVILE E INDUSTRIALE.
  • Diaconescu, A., Bellman, K., Landauer, C., Nelson, P.R., & King, D. (2026). Hybrid Clumping: a Self-integration Basis for Complex Structures and Collective Dynamics. Proceedings of the 7th IEEE International Conference on Autonomic Computing and Self-Organizing Systems.
  • Dimitrov, N., Gelmi-Candusso, T.A., Krkošek, M., & Fortin, M-J. (2026). The effects of fox movement and landscape heterogeneity on the spread of sarcoptic mange in urban settings. bioRxiv. https://doi.org/10.64898/2026.06.24.734291
  • Ding, Y., Chen, F., Plotnikova, M., Wang, & Wang, B. (2026). Policy Modeling for Urban Energy Resilience to Extreme Heat: A Multi-Agent Simulation Framework in Chongqing, China. Reliability Engineering and System Safety. https://doi.org/10.1016/j.ress.2026.112260.
  • do Nascimento, D.A., Alves, M.H.C., Zazeri, G., & Monteiro, F.F. (2026). Integrating agent-based modeling and flipped instruction to enhance a climate-physics learning: a Bloom's revised taxonomy analysis in Brazilian high schools. Frontiers in Education 11. https://doi.org/10.3389/feduc.2026.1869092
  • Dong, D. (2026). Toward Sustainable Edge Computing Infrastructures: A VEC-MEC-Cloud Collaborative Framework for Adaptive Resource Distribution. IEEE Transactions on Mobile Computing. DOI 10.1109/TMC.2026.3654928
  • Dong, J., Mi, J., Guan, J., & Crossan, M. (2026). When lacking knowledge is inevitable: ignorance distribution and the exploration-exploitation balance. Journal of Business Research 215. https://doi.org/10.1016/j.jbusres.2026.116361
  • Dong, T., & Tadi, M. (2026). Equitable Access to Urban Green Spaces Under Heat Stress: An Agent-Based Simulation (ABS) of Age-Differentiated Walkability Through a Behavioral Perspective. Smart Cities, 9(6), 97. https://doi.org/10.3390/smartcities9060097
  • Dressler, G., Robinson, L., & Müller, B. (2026). Unravelling vicious circles of land use competition and food insecurity in agropastoral rangelands. People and Nature. https://doi.org/10.1002/pan3.70280
  • Duprey, M. A., & Bobashev, G. V. (2026). Enhancing Computational Efficiency in NetLogo: Best Practices for Running Large-Scale Agent-Based Models on AWS and Cloud Infrastructures. arXiv:2602.15317
  • Duprey, M. A., Adam, J. W., Khan, S. S., & Bobashev, G. V. (2026). OPOSim: An Agent-Based Model for Evaluating Long-Term Effects of Opioid Policy Outcomes in North Carolina. Journal of Artificial Societies and Social Simulation 29(1).
  • Edmonds, B., Steiglechner, P., Lorenz, J., Kalvas, F., Batzke, M.C.L. (2026). Understanding Mutual Social Influence When People Prefer Coherent Beliefs. In: Keijzer, M.A., Lorenz, J., Bojanowski, M. (eds) Computational Social Science of Social Cohesion and Polarization. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-032-01373-6_9
  • Eidin, E., Bowers, J., & He, P. (2026). Modeling in the Digital Era: An Overview of Practical Studies on Computerized Model-Based Science Instruction. In Leite, L., Dourado, L., Afonso, A.S., Morgado, S., Carvalho, L. (Eds.), Practical Work in Science Education in the Digital Era, 203-220. Springer Nature. https://doi.org/10.1007/978-3-032-13788-3_15
  • Encinas, D., Jara, J., Bond, R., Rosatto, D., Krahn, G.G., Loreto, I.D., Zarate, E., Scholz, V., Gaudiani, A., & Morales, M. (2026). Análisis Basado en Simulación de Sistemas de Salud e Infraestructuras de HPC. In Workshop de Investigadores en Ciencias de la Computación WICC 2026 (pp. 797-802). Red de Universidades con Carreras en Informática. https://sedici.unlp.edu.ar/handle/10915/196370
  • Encinas, D.O. (2026). Modelización y simulación basada en agentes aplicada a la arquitectura de entrada/salida de los computadores paralelos [Doctoral thesis, Universidad Nacional de la Plata]. Repositorio Institucional de la UNLP. https://doi.org/10.35537/10915/194710
  • Espinosa, O., Ordóñez, A., & Puentes, G. (2026). Recent applications of chaos theory in medical sciences: progress and challenges. BMC Biomedical Engineering, 8(6), Springer. https://doi.org/10.1186/s42490-026-00111-0
  • Etienne, D. (2026). The Art of Being in Common : Walking Together to Better Explore. Cirad-Agritrop. https://agritrop.cirad.fr/617296/
  • Fachada, N., Fernandes, D., Fernandes, C.M., & Matos-Carvalho, J.P. (2026). Can Large Language Models Implement Agent-Based Models? An ODD-based Replication Study. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2602.10140
  • Fadhi, W.A., & Zein, R.O. (2026). Pengembangan Industri Kreatif Pengrajin Kayu Dengan Visualisasi Net Logo. Digital Nexus Systematic Journal, 2(1), 39–43.
  • Fauzi, M., Ferrianta, Y., Firdaus, M. (2026). Dynamic Integration in Agent-Based Modeling: Strategies for Optimizing Land-Use Change Policies in Peri-Urban Areas through Interactive Simulation. Research on World Agricultural Economy 7(1), 529-545. https://doi.org/10.36956/rwae.v7i1.2451
  • Fayad, P. (2026). Agent-based modelling and simulation of human mobility in the context of infectious disease spread: Development and application for the case of COVID-19 spread in Larnaca, Cyprus. [Doctoral Dissertation, Cyprus University of Technology].
  • Feng, W., Zhang, G., Li, B., Bai, C., Lyu, L., & Yang, L.E. (2026). Modeling interdisciplinary cooperation networks for innovation-focused university research projects: an agent-based approach. Humanities and Social Sciences Communications, 13(980). https://doi.org/10.1057/s41599-026-08025-x
  • Fernández-Vilas, E., Iglesias Carrera, M., & Coca, J. R. (2026). Modelling Mediated Vulnerability and Reversible Social Exclusion: An Exploratory Agent-Based Model Inspired by Deafblindness. Systems, 14(7), 781. https://doi.org/10.3390/systems14070781
  • Ferraciolli, E. C. (2026). Theories of Money and Communities of Practice: A Cross-Disciplinary Investigation on the Nature of Money and the Uses of Monetary Theory. [Doctoral Dissertation, Universidade de Lisboa].
  • Ferreira, D., de Figueiredo, R.F., de Almeida, J.P.D.E., Mendes, T.S.G., & Simões, S.J.C. (2026). Exploring the Role of Dynamic Graph-Based Modeling in Disaster Evacuation Route Simulation for Geologically and Hydrologically Hazardous Areas. Geographical Review, 1–23. https://doi.org/10.1080/00167428.2026.2664624
  • Finnemann, A., Waldorp, L., Borsboom, D., Marsman, M., & van der Maas, H. L. J. (2026). A theory-construction methodology for network theories in psychology. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000829
  • Fish, K.A. (2026). Modeling Preservation Bias to Improve Hominin Habitat Reconstructions [Doctoral dissertation, George Washington University] (No. 32579068). The George Washington University ProQuest Dissertations & Theses. https://www.proquest.com/dissertations-theses/modeling-preservation-bias-improve-hominin/docview/3345942177/se-2
  • Flandre, N.Y., & Giabbanelli, P.J. (2026). Distilling the Complexity of Agent-Based Simulations Into Textual Explanations via Large Language Models. Big Data Cogn. Comput, 10(4). https://doi.org/10.3390/bdcc10040121
  • Flores, F.J.G. (2026). Sistemas Multiagente y Modelos Predictivos para la Detección y Anticipación de Perfiles Profesionales en las Organizaciones [Master's thesis, Universidad de Málaga]. RiUMA. http://hdl.handle.net/10630/46630
  • Focardi, A., Bagnoli, F., Guazzini, A., Gronchi, G. (2026). Introducing the Physics of Complex Systems through Videogames. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2601.06230
  • Freeman, C. (2026). Organisational scale and fraud volatility: An exploratory agent-based simulation of occupational fraud dynamics. PLoS One 21(7). https://doi.org/10.1371/journal.pone.0342964
  • Freeman, C. (2026). Rethinking fraud control: Fraud dynamics, effective prevention, and the limits of reactive detection-led strategies. Journal of Economic Criminology 12. https://doi.org/10.1016/j.jeconc.2026.100216
  • Fuentes, N., Schmutzler, J., & Carréon-Vázquez, G. (2026). The Paradox of Inclusion: Modeling Systemic Dynamics of the Gender Finance Gap in Entrepreneurial Ecosystems. Entrepreneurship Theory and Practice. https://doi.org/10.1177/10422587261462834
  • Gamal, Y., Colasanti, R., Polhill, G., Mitomi, T., Suel, E., & Heppenstall, A. (2026). MATraM: A Multi-Activity Transport and Mobility Agent-Based Model for Activity Modifications. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2605.30547
  • Ganitis, K. (2026). BAM Engine: A vectorized Python framework for the Bottom-up Adaptive Macroeconomics model. Journal of Open Source Software.
  • Gao, B., Braun, R., Wu, D., & Ford, R.M. (2026). Pseudomonas putida Chemotactic Efficiency toward Naphthalene at a NAPL–Water Interface Decreased under Increasing Shear Flow. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.5c15041
  • Garrone, R. (2026). A Computational Social Simulation of Ageing and Care Accessibility in Italian Inner Areas. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2509.26496
  • Gerada, A., Zhong, Y., Brookefield, C., Howard, A., Reza, N., Rosato, C., Green, P.L., & Hope, W. (2026). Simulating interventions to prevent gastro-intestinal transmission of Clostridioides difficile and antimicrobial-resistant bacteria in healthcare settings: a model calibration study. Journal of Hospital Infection. https://doi.org/10.1016/j.jhin.2026.06.007
  • Ghasemi, A., Kulms, L., Lichtenstein, T., & Weske, M. (2026). Agent-Based Simulation of Flexible Process Interactions. In: Fuentes, L., Plebani, P., Combi, C., Reijers, H. (eds) Advanced Information Systems Engineering. CAiSE 2026. Lecture Notes in Computer Science, vol 16559. Springer, Cham. https://doi.org/10.1007/978-3-032-28117-3_5
  • Gomez, Y. Z. R. (2026). Generación de datos sintéticos Orientada a la detección del LA/FT Mediante simulación basada en agentes.
  • Goyal, S.B., Rajawat, A.S., Singh, A.V., & Singh, J. (2026). Self-healing infrastructure through Nano-AI swarms: Biomimetic approaches to urban sustainability. AIP Conference Proceedings, 3420(1). https://doi.org/10.1063/5.0329138
  • Grgičević, L., & Coates, E.M. (2026). PULSE: an algorithm for multi-agent centralised high-density maritime traffic management based on guidance games. Journal of Marine Science and Technology. https://doi.org/10.1007/s00773-026-01110-5
  • Guáqueta-Solórzano, V. E., Cantor, J. E. H., Sarmiento, V. P., Gomez, A., & Tello, M. I. (2026). Traditional ecological knowledge of the Arhuaco people on water management in the Sierra Nevada de Santa Marta. PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-8895046/v1]
  • Guerrero, L.M. (2026). Competencia Institucional Adaptativa y Selección Dinámica: Un Análisis Formal y Basado en Agentes de la Optocracia. SSRN. http://dx.doi.org/10.2139/ssrn.6583279
  • Gunputh, N. D., Kilikian, E., Miranda, C. A., Peirce, S. M., & Ford Versypt, A. N. (2026). Agent-Based Modeling of Idiopathic Lung Fibrosis and Mechanistic Treatments. bioRxiv. https://doi.org/10.64898/2026.03.22.713503
  • Haddad, Y., & El Azzaoui, M. (2026). A multi-agent model for simulating residential HVAC scenarios, forecasting heating demand and experimental validation. SSRN. https://dx.doi.org/10.2139/ssrn.6902955
  • Hafez, S., Farag, F.R., & Tawadros, A.S.N. (2026). From Homogeneous to Heterogeneous Adaptive Bounded-Confidence Opinion Dynamics on Networks. Northeast Journal of Complex Systems, 8(4). https://doi.org/10.63562/2577-8439.1169
  • He, Z. (2026). VISA: A Structured Description Protocol for Agent-Based Simulation Models Towards Machine Reproducibility. arXiv. https://doi.org/10.48550/arXiv.2607.28027
  • Herawatie, D., Siswanto, N., & Widodo, E. (2026). Evaluating Driver Strategies in Motorcycle-Based Ride-Hailing Services: An Agent-Based Modeling and Simulation Approach. JOIV : Int. J. Inform. Visualization, 10(1), 206-220.
  • Herawatie, D., Siswanto, N., Widodo, E., & Pramudita, R. . (2026). Design for evaluating online food delivery systems using agent-based model and GIS. In M. F. F. Mardianto & I. Syahzaqi (Eds.), Proceedings of the International Conference on Mathematics, Computational Sciences, and Statistics. https://doi.org/10.1063/5.0308994
  • Himani, Focker, M., Sadik, J. A., Samiotis, G., Hommels, N. M. C., Alteio, L., Jacxsens, L., Williams, P., Zwietering, M. H., & van der Fels-Klerx, H. J. (2026). Modelling Listeria monocytogenes contamination and application of the resilience concept in the soft cheese supply chain. Food Control 184. https://doi.org/10.1016/j.foodcont.2026.112032
  • Holman, M., Walker, G., & Lansdown, T. (2026). Networked Exploration of Complex Systems (NEXUS): Integrating Agent‐Based Modeling and Network Analysisfor Systems‐Based Human Reliability Assessment. Human Factors and Ergonomics in Manufacturing & Service Industries, 36(3). https://doi.org/10.1002/hfm.70045
  • Horn, C., Potter, R., Di Carlo, J.M., & Peternell, M. (2026). Carving miniature worlds – landscape, environments, and Nordic Bronze Age rock art carvers.World Archaeology. https://doi.org/10.1080/00438243.2025.2608638
  • Horn, S., Banisch, S., Batzdorfer, V., Reitenbach, A., Sartori, F., Schwabe, D., & Mäs, M. (2026). Success-Driven User Activity Contributes to Online Polarization. Journal of Artificial Societies and Social Simulation, 29 (2). 10.18564/jasss.5947
  • Hossain, R., & Rahman, A. (2026). Modeling Clinician Burnout and Attribution to Optimize Staffing Ratios and Improve Patient Safety Outcomes in Underfunded Safety-Net Hospitals. The Science Post 2(2). doi.org/10.5281/zenodo.12345678
  • Hotz, R., Brown, C., Zeng, Y., Schmitt, T., & Rounsevell, M. (2026). Modelling Socio-Psychological Drivers of Land Management Intensity. arXiv:2602.02347
  • Hotz, R., Schmitt, T., Brown, C., Zeng, Y., & Rounsevell, M. (2026). Empirical behavioural heterogeneity shapes the dynamics of an agent-based land use model. arXiv. https://doi.org/10.48550/arXiv.2608.03784
  • Huang, C., & Wang, S. (2026). Using a mixed opinion dynamics and innovation diffusion model to explore the ‘best game no one played’ phenomenon. PLoS One 21(5): e0349217. https://doi.org/10.1371/journal.pone.0349217
  • Huang, C., Feng, R., Cai, Y., & Sun, K. (2026). Research on the Entrepreneurship and Transmission Evolution Mechanism of Platform Enterprises Based on Complex Networks. ResearchSqaure. https://doi.org/10.21203/rs.3.rs-9409709/v1
  • Huang, R., Ma, Z., & He, N. (2026). Formal Verification of Trust in Multi-Agent Systems Under Generalized Possibility Theory. Mathematics 14(3), 456. https://doi.org/10.3390/math14030456
  • Huber, R., Barreiro-Hurlé, J., & Finger, R. (2026). Behavioural and social factors in farm-level agent-based models for European agricultural policy evaluations. European Review of Agricultural Economics, Vol 0 (0), 1-51. https://doi.org/10.1093/erae/jbag006
  • Hurtado, E.C., & Gómez, E.O. (2026). Simulación basada en agentes para el estudio de asentamientos informales: Análisis de enfoques, metodología y técnicas a partir de la revisión literaria [Trabajo de grado profesional, Universidad de Antioquia]. Universidad de Antioquia Repository. https://bibliotecadigital.udea.edu.co/server/api/core/bitstreams/f0ed1c27-6e3f-4c89-ae49-d2a930b957a5/content
  • Hussain, H., & de Vries, M. (2026). Evaluating and Extending the Meta Model for Enterprise Systems Dynamics. SN Computer Science, 7(577). https://doi.org/10.1007/s42979-026-05130-1
  • Ibrahim, Y., & Hassan, R. (2026). Intensified Media: When Could It Be Effective in Manipulating Crowd Behaviour?. Complexity, Governance & Networks, 9(1). https://doi.org/10.62799/cgn.v9i1.169
  • Ingram, F., Rudawska, A. (2026). Investigating Knowledge Sharing Under Diverse Working Modes Using Agent-Based Simulation. In M. Hernes et al. (Eds.), Proceedings of 28th European Conference on Artificial Intelligence ECAI 2025 - IMIS Workshop: Vol. 1. Emerging Challenges in Intelligent Management Information Systems (pp. 307 - 322). Springer. https://doi.org/10.1007/978-3-032-13869-9_24.
  • Inturri, G., Pluchino, A., & Di Grande, C.M. (2026). Capacity assessment of small airport passenger terminals using agent-based simulation: A case study of Comiso Airport. Case Studies on Transport Policy 25. https://doi.org/10.1016/j.cstp.2026.101896
  • Ioanăș, I., Ionescu, S., Delcea, C., & Marin, E. (2026). Modeling Skills Mismatch in the Labor Market Using an Agent-Based Approach. Proceedings of the International Conference on Business Excellence. https://doi.org/10.2478/picbe-2026-0292
  • Ioanăș, I., Sandu, A., Delcea, C., Cotfa, L.-A., Domenteanu, A., & Crișan, A.-G. (2026). A Multi-Method Framework for Exploring Skill Structures in the IT Job Market. Journal of Grey System, 38(2), 105–122.
  • Islam, S.M.S., & Demir, I. (2026). Modeling Flood Risk Communication and Evacuation Dynamics Using an Agent-Based Framework: A Cedar Rapids, Iowa Case Study. Earth arXiv. https://doi.org/10.31223/X5NZ1B
  • Ivanova, Y. (2026). SIMULATION ANALYSIS OF THE STRUCTURAL IMPACT OF AI AMPLIFIERS ON GLOBAL CYBER RISK MAPS. International Journal on Information Technologies & Security, 18(2), 95.
  • Jadiya, S., & Mishra, S. (2026). A Computational Framework for Intelligent Decision Making Using Behavioural and Creative Models. Proceedings of the 2026 IEEE International Conference on AI Engineering and Innovations (AIEI), IEEE. 10.1109/AIEI69164.2026.11497180
  • Jafar, K.H.M., Zakery, A. & Rasouli, M. (2026). The dynamics of knowledge interactions between returnee entrepreneurs and home-country firms, an ABM-simulation approach. Journal of Global Entrepreneurship Research 16(57). https://doi.org/10.1007/s40497-026-00531-1
  • Janjua, S., An-Vo, D., Reardon-Smith, K., & Mushtaq, S. (2026). Bargaining for Water: An Integrated Nash and Agent-Based Simulation for Sustainable Residential Water Demand Management in the Global South. Water Resources Management 40(335). https://doi.org/10.1007/s11269-026-04745-6
  • Jeong, G., Park, C., Kim, M. (2026). 베인브릿지 신앙 발생 모형의 재발견: 컴퓨터 시뮬레이션을 통한 종교사회학의 확장. (The Rediscovery of Bainbridge's Model of Faith Development: The Expansion of the Sociology of Religion Through Computer Simulation). Korean Society for the Sociology of Religion
  • Jia, F., Zhu, H., Jia, F., Shih, C., Yao, Z., Liu, S., & Chan, W.K. (2026). A Systematic Review and Prospect of Hybrid Simulation Methods and Applications from 1980 to 2024. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-026-10607-6
  • Jia, L., Song, F., Hei, W., Wang, J., & Xie, Y. (2026). Research on the Evolution of Oil and Gas Pipeline Network Attack and Defense Based on Reinforcement Learning and Game Theory. Journal of Internet Technology, 27(2), 251-262. https://doi.org/10.70003/1607926420260327020011
  • Jiang, W., Zhang, X., Zhai, J., Ma, S., Shen, C., & Liu, Y. (2026). False friends in the shell: Unveiling the emoticon semantic confusion in Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2601.07885
  • Jin, Z., Xiong, Y., Yu, C., Li, C., Jin, Z., Ye, X. (2026). Enhancing urban flood resilience through agent-based modeling of evacuation behaviors. Sustainable Cities and Society (107133). https://doi.org/10.1016/j.scs.2026.107133
  • Jo, A.A., Raj, E.D., & Srinivasalu, S. (2026. Agent-Based Modelling of the Indian Public Distribution System in AI FORA. In: Ahrweiler, P., Gilbert, N. (eds) Participatory Modelling and Simulation to Improve AI-based Public Social Services, 85-98. Artificial Intelligence, Simulation and Society. Springer, Cham. https://doi.org/10.1007/978-3-032-15283-1_7
  • Jo, H., Song, W., Jablonski, P. G., & Lee, S. (2026). Novelty-response behavior and size relationships in Camponotus japonicus foragers. Insectes Sociaux. https://doi.org/10.1007/s00040-026-01093-x
  • Johansson, E., Lorig, F., & Davidsson, P. (2026). Combination of agent-based social simulation models: An experimental evaluation using epidemic models. Simulation. https://doi.org/10.1177/00375497261453360
  • Johansson, E., Lorig, F., & Davidsson, P. (2026). Towards a Multi-Model Approach to Agent-Based Social Simulation for Policymaking. OpenReview. https://openreview.net/forum?id=A992hzkn1P
  • Johnson, N.F., & Huo, F.Y. (2026). Fusion-fission forecasts when AI will shift to undesirable behavior. arXiv. https://doi.org/10.48550/arXiv.2605.14218
  • Joshi, C.H., & Dornhaus, A. (2026). Frequent errors are the worst: robustness to individual failures in collective foraging. bioRxiv. https://doi.org/10.64898/2026.07.12.738017
  • Junhong, C. (2026). AI-Based Modeling and Simulation for Enhancing Systems Thinking in High School Ecosystem Education: An Empirical Study. 15th International Conference on Educational and Information Technology (ICEIT), Xi'an, China, pp. 887-892, doi: 10.1109/ICEIT68991.2026.11521439
  • Kahalimoghadam, M., & Thompson, R.G. (2026). Assessing the viability of construction and demolition waste recycling through agent-based modeling. International Journal of Construction Management, 1–26. https://doi.org/10.1080/15623599.2026.2653085
  • Kahana, O. (2026). Digital Birders: Exploring the Link between Students’ Eye Tracking Patterns and Learning About Complex Systems through Observing an Agent-Based Model. [Master's Thesis, University of Haifa]. ProQuest Dissertations & Theses.
  • Kalluri, R. (2026). Agent-Based Simulation of Trust Development in Human-Robot Teams: An Empirically Validated Decision-Support Framework. Central European Journal of Operations Research. https://doi.org/10.1007/s10100-026-01028-8
  • Kang, J., Hong, H., Lee, M., Hwang, H., Yang, J., Lee, S., & Jeong, H. (2026). Enhancing GIS Education through Geosimulation: A Focus on Agent-Based Modeling. Journal of the Korean Geographical Society, 61(1). https://doi.org/10.22776/kgs.2026.61.1.69
  • Kangkelidis, A., Gavidia-Calderon, C., Nuseibeh, B., Abioye, A., & Bennaceur, A. (2026). Yanapay: A Simulation Toolkit for Autonomous Agents Reasoning about Human Behaviour in Emergency Evacuations. AAMAS 2026: Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems, 4062-4064. https://doi.org/10.65109/YQLN3900
  • Kapetanović, S., Dželalija, M., Bijedić, N., Gašpar, D., & Tipurić-Spužević, S. (2026). A Rule-Based Agent-Based Neural Model with Explicit Signal Transport and Environment-Mediated Feedback: The LANA Model. Sci 8(7). https://doi.org/10.3390/sci8070159
  • Kapetanovic, S., Dželalija, M., Bijedic, N., Gašpar, D., & Tipuric-Spuževic, S. (2026). Systematic Verification and Validation of the LANA Agent-Based Spiking Neural Network Model. Sci, 8(5). DOI:10.3390/sci8050107
  • Kareva, I. (2026). Adaptive receptor expression and the emergence of disease as loss of signaling homeostasis. bioRxiv. https://doi.org/10.64898/2026.03.21.713376
  • Karthika, J. (2026). Socio-Technical Conflict and Adaptive Governance: Applying Agent-Based Modelling to Inter-Civilizational and Environmental Scenarios. Journal of Smart Infrastructure and Environmental Sustainability, 3(2), 18-25. https://aasrresearch.com/index.php/JSIES/article/view/173
  • Kaviari, F., Kim, K., & Yamashita, E. (2026). Simulating the spatial spread and containment of a pandemic in a multimodal tourism district using agent-based modeling. Transportation Research Interdisciplinary Perspectives. https://doi.org/10.1016/j.trip.2026.102111
  • Kay, N.R., & Schaller, M. (2026). Adaptive management of kin recognition errors: Agent-based models of kinship estimation biases and their fitness implications in different ecological contexts. Evolution and Human Behavior, 47(4). https://doi.org/10.1016/j.evolhumbehav.2026.106865
  • Kelly, S. (2026). Creative Destruction or Structural Scarring? An Agent-Based Model of the AI Transition. SSRN. http://dx.doi.org/10.2139/ssrn.6776778
  • Kennedy, J.R.M., Oliva, F., Degani, A., & Bar-Sinai, K.L. (2026). Designing like Beavers: A Multi-Agent, Data-Coupled Approach to Process-First Landscape Design for Resilience. Journal of Digital Landscape Architecture. doi:10.14627/537770066
  • Keshavarz, K., Jaca, C., Alvarez, M.J., & Elkady, S. (2026). What Drives Sustainable Food Choices? Testing Policies with Agent-Based Modelling. Journal of Policy Modeling. https://doi.org/10.1016/j.jpolmod.2026.107042
  • Khaluf, Y., Wurpts, B., Hovestadt, T., Zöller, N., Hofstede, G.J. (2026). Modeling Social Cohesion: The Influence of Memory and Learning in Simulated Networks. In: Keijzer, M.A., Lorenz, J., Bojanowski, M. (Eds) Computational Social Science of Social Cohesion and Polarization. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-032-01373-6_11
  • Klagkou, E., & Lika, K. (2026). A DEB-IBM approach on modeling population dynamics of Bactrocera oleae, under different global warming scenarios. Ecological Modelling 521. https://doi.org/10.1016/j.ecolmodel.2026.111769
  • Knox, C. E. (2026). Building Bridges for Troubled Water: Assessing the Possibility of Cooperative Adaptation in Boston Harbor With Agent-Based Models of Coalition Formation (Publication No. 32441988)[Doctoral Dissertation, Tufts University]. ProQuest Dissertations & Theses Global. http://turing.library.northwestern.edu/login?url=https://www.proquest.com/dissertations-theses/building-bridges-troubled-water-assessing/docview/3302407990/se-2
  • Knox, C., Kirshen, P., Lamontagne, J., & Islam, S. (2026). More than the climate: an exploration of coordinated governance for sea level rise adaptation in Boston. Regional Environmental Change, 26(78). https://doi.org/10.1007/s10113-026-02573-3
  • Kos, J., Singh, R., & Goel, A. (2026). T2MM: An LLM Supported Architecture For Inquiry-Based Modeling. Cornell University, arXiv. https://doi.org/10.48550/arXiv.2606.11210
  • Kosak, O., Kastenmuller, P., Malke, V., Schwaiger, F., & Reif, W. (2026). A Swarm Algorithm for Following Formations applied to Crazyflie Drones. Paper Accepted to the 8th International Workshop on Robotics Software Engineering.
  • Kosak, O., Kastenmüller, P., Reif, W. (2026). Let's do the swarm flight again: unleashing the potential of PROTEASE 2.0 for drone formation flight. International Journal on Software Tools for Technology Transfer. https://doi.org/10.1007/s10009-025-00834-w.
  • Kulichkina, A., Waldherr, A., & Wijermans, N. (2026). Online Protest and Repression in Authoritarian Settings. Journal of Artificial Societies and Social Simulation, 29(2). 10.18564/jasss.5960
  • Kumar, D., Sood, S. K., Rawat, K. S., & Prajapat, S. (2026). A digital-twin-enabled cyber-physical framework for predictive infection-risk monitoring and secure health-data surveillance. IEEE Transactions on Computational Social Systems. Advance online publication. https://doi.org/10.1109/TCSS.2026.3703478
  • Kurahashi, S., Mukai, T., Kumada, F., & Nagai, H. (2026). A Simulation Model for Evaluating Walking Promotion Policies to Support Health-Promoting Cities. Proceedings of the 18th International Conference on Agents and Artificial Intelligence (ICAART 2026), 1, 347-353. 10.5220/0014309400004052
  • Kurahashi, S., Mukai, T., Kumada, F., & Nagai, H. (2026). Digital Social Experiment Simulation to Support Smart Wellness Cities. In H. Verhagen, E. Chappin, & G. Scholz (Eds.), Advances in Social Simulation: Proceedings of the 20th Social Simulation Conference (pp. 310 - 321). Springer.
  • Kürschner, T., Radchuk, V., Planillo, A., Scherer, C., Blaum, N., Staubach, C., Thulke, H.H., & Kramer-Schadt, S. (2026). Reconciling dynamic epidemiological models with long-term outbreak data: The case of classical swine fever in Germany. Ecosphere, 17(2). https://doi.org/10.1002/ecs2.70539open_in_new
  • Kuzma, O. (2026). Modification and Application of Diagrammatic Languages (BPMN, UML) for the Description and Modeling of Biological Systems [Master's thesis, Univerzita Karlova]. Charles University Digital Repository. http://hdl.handle.net/20.500.11956/207147
  • Laassami, F., Ledmi, A., & Souidi, M.E.H. (2026). CSO: A Novel Crow Swarm Optimization Algorithm for Collaborative Path Planning with Collision Avoidance. Journal of Intelligent and Fuzzy Systems, 1-26. https://doi.org/10.1177/18758967261422625
  • Lai, P.K. (2026). Enhancing learning of nanoscale phenomena: the role of agent-based models and embodied reasoning. Instr Sci, 54(22). https://doi.org/10.1007/s11251-025-09776-x
  • Lammers, D., Requier, F., Focks, A., & Groeneveld, J. (2026). Food for thought: could the queen's egg-laying rate in the BEEHAVE honey bee model emerge from the effects of brood pheromones, weather conditions, and pollen availability?. Individual-based Ecology 2(e185721). https://doi.org/10.3897/ibe.2.185721
  • Landin, L. (2026). An Agent-Based Simulation Framework for Analyzing Traffic Effects in Ice Hockey. [Bachelor's Thesis, Umeå University].
  • Lara-Lopez, L.C., Trejos-Zelaya, I., Nunez-Corrales, S., & Helo-Guzman, J. (2026). Agent-Based Modeling and Simulation - Feature Spaces, Macroscopic Statistical Analysis, and Specification Fidelity. CLEI Electronic Journal, 29(4). https://doi.org/10.19153/cleiej.29.4.7
  • Le, N.H. (2026). From controlled degrowth to controlled staying: Rethinking overtourism through climate immobilities. Humanities and Social Sciences Communication. https://doi.org/10.1057/s41599-026-07958-7
  • Leal, D.F. (2026). Locating Cultural Holes Brokers in Diffusion Dynamics Across Bright Symbolic Boundaries. Sociological Methods & Research, 55(2), 754-792. https://doi.org/10.1177/00491241251322517open_in_new
  • Ledesma González, V.J. (2026). Simulacion Basada en Agentes de Estrategias de Economia Circular para la Reduccion de Residuos Electronicos de Smartphones. Available at ResearchGate.
  • Lee, Y., & Xu, X. (2026). Use of aggregated relational data in agent-based modeling. Social Networks, 84, 164-179. https://doi.org/10.1016/j.socnet.2025.09.004
  • Leonard-Duke, J., Csordas, D.J., Hannan, R.T., Sano, C., Hossainian, D., Batavia, M., Andrews, R., Ambrosone, M., Eggertsen, T.G., Velez, T.E., Sturek, J.M., Sperling, A., Abebayehu, D., Barker, T.H., Bonham, C.A., Saucerman, J.J., & Peirce, S.M. (2026). Computational Analysis of Fibroblast Subpopulation Dynamics as a Driver of Fibrotic Foci Formation. bioRxiv. https://doi.org/10.64898/2026.08.03.742640.
  • Lewis, E., Parker, B., & Johnson, A. (2026). Quantifying the Stability of Distributed Enterprise Architectures Through Multi-Agent System Simulations. Journal of Innovation in Governance and Business Practices 2(1).
  • Li, J., & Kennedy, W.G. (2026). Adaptive Self-Preservation in Flocking Systems: How Dynamic Risk Thresholds Shape Collective Safety. OpenReview. https://openreview.net/forum?id=ujkFP8ynAx
  • Li, S., Hao, J., Chai, Y., Feng, T., Liu, Y., & Zhu, X. (2026). Mapping Emotional Pathways to Social Identity in Hybrid Work: A Computational Model for Organizational Cohesion. Behavioral Science 16(2). https://doi.org/10.3390/bs16020303
  • Li, T., Giardini, F. (2026). Unraveling the Role of Vulnerability and Norm Compliance in Societal Preparedness in Health Crises. In: Verhagen, H., Chappin, E., Scholz, G. (eds) Advances in Social Simulation. SSC 2025 2025. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-032-31712-4_26
  • Li, W., Zhang, Y., Yang, Q., Yang, G., & Ding, S. (2026). Research on the decision-making of green and high-quality agricultural product production at the front end of the industrial chain: From the perspective of cooperation between cooperatives and farmers. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2026.148433
  • Li, X., Liu, S., & Zou, Y. (2026). Agent-Based Modeling of the Greenwashing Behavior of Building Material Enterprises Under Public Opinion Influence. Buildings, 16(9). https://doi.org/10.3390/buildings16091791
  • Little, C. J., Banville, P. E., Ford, A. T., & Germain, R. M. (2026). Spatial signature of resource distribution is mediated by consumer body size and habitat preference bioRxiv. https://doi.org/10.1101/2024.06.24.600507
  • Liu, F., Guo, Z., Xie, Z., Chen, D., & Yang, Y. (2026). Unpacking Innovation Incentives in Rural Tourism: A Simulation of Policy Impacts and Regional Heterogeneity. Journal of Travel Research, 19. https://doi.org/10.1177/00472875251410562
  • Liu, Y., Zhang, G., & Liu, Y. (2026). What are the key factors affecting AI algorithmic price discrimination in China? Evidence from complex adaptive systems modelling. Systems Research and Behavioral Science. https://doi.org/10.1002/sres.70041open_in_new
  • Lomasto, L., Camoia, A., Guarino, A., Lettieri, N., Malandrino, D., & Zaccagnino, R. (2026). Integration of Deep Reinforcement Learning and Agent-based Simulation to Explore Strategies Counteracting Information Disorder. Social and Information Networks, arXiv. https://doi.org/10.48550/arXiv.2604.13047
  • Long, W., Zheng, B., Wu, Z., Qian, H., Zou, Y., Tan, J., & Wang, Y. (2026). Survey-calibrated agent-based modeling of peer targeting for promoting physical activity among adolescents. Frontiers in Public Health, 14. https://doi.org/10.3389/fpubh.2026.1807576
  • Lowe, W. U. A., Carroll, P., Lades, L., & Martinez-Pastor, B. (2026). Using agent-based modelling for investigating the impact of travel behaviour interventions on mode shift: A travel behaviour simulation in Dublin. Available at SSRN. http://dx.doi.org/10.2139/ssrn.6202729
  • Lowe, W.U.A., Lades, L., & Carroll, P. (2026). Evaluating soft travel behaviour interventions using behavioural stages of change within an agent-based modelling framework. Transportation. https://doi.org/10.1007/s11116-026-10784-0
  • Lu, J., & Lin, C. (2026). Stereotypes as Byproducts of Hierarchical Bayesian Inference. Available at ResearchGate.
  • Lučić, K., & Gorica, G.V. (2026). Izazovi provedbe fakultativne nastave Umjetna inteligencija: od koncepta do primjene u Gimnaziji Velika Gorica. In Umjetna inteligencija – saveznik u suvremenom odgoju (pp. 110-121). BrAIn. https://brain.hr/wp-content/uploads/2026/05/BrAIn_Zbornik-primjera-dobre-prakse-u-provedbi-kurikula.pdf#page=58
  • Lunde, H.B., Xanthopoulou, T.D., & Prinz, A. (2026). Replicating DomWorld. In H. Verhagen, E. Chappin, & G. Scholz (Eds.), Advances in Social Simulation: Proceedings of the 20th Social Simulation Conference (pp. 16-38). Springer.
  • Ma, K., Li, Y., & Gubar, E. (2026). The Impact of Transportation Flows on the SEIR Epidemic Model: A Case Study. Mathematics, 14(11), 1820. https://doi.org/10.3390/math14111820
  • Ma, X., Yan, J., Gao, P., Li, Q., Yao, X., Li, J., & Wang, W. (2026). Modeling grassland landscape degradation and policy response in the Gannan Plateau: a couple ERI-BRT-ABM framework. Ecological Indicators 185. https://doi.org/10.1016/j.ecolind.2026.114766.
  • Madsen, J.K. (2026). The Supply Side: Are Biased Information Sources Necessary for Belief Polarisation?. Proceedings of the Annual Meeting of the Cognitive Science Society, 48(0). https://escholarship.org/uc/item/4833c3fg
  • Mandl, C., Chaturvedi, S., & van Gerven, M. (2026). Tuning Agent-Based Predator-Prey Models Toward Lotka-Volterra Dynamics. Cornell University, arXiv. https://doi.org/10.48550/arXiv.2606.13639
  • Manzi, F., Brans, V., Dacek, M., Jacob, S., & Schtickzelle, N. (2026). Tracking cell movement in two dimensional, fragmented microcosms reveals dispersal syndromes and strategies in Tetrahymena thermophila. Ecology and Evolution, 16(2), e73092. https://doi.org/10.1002/ece3.73092
  • Maqour, Z., El Bakkali, H., Benhaddou, D., & Benbrahim, H. (2026). RBCrowd: A Reliable Blockchain-Based Reputation Management Framework for Privacy Preservation in Mobile Crowdsensing. Future Internet 18(1). https://doi.org/10.3390/fi18010065
  • Marisetty, V. (2026). Common Technology Providers, Common Risk: AI and Securities Markets. Common Risk: AI and Securities Markets (July 01, 2026).
  • Masanawa, S. A., Doro, I. S., Deborah, B. (2026). Accurate Derivation and Efficient Implementation of Higher Order Logic Programming in Hopfield Network. Journal of Basics and Applied Sciences Research 4(2). https://dx.doi.org/10.4314/jobasr.v4i2.40
  • Matsui, K., & Hashimoto, T. (2026). Identifying Factors Contributing to the Acceptance of Behavior-Change Policies: The Case of Stay-atHome Orders During the COVID-19 Pandemic in the United States. ResearchSquare. https://doi.org/10.21203/rs.3.rs-9696939/v1
  • McCall, G.S., & Rehan, M. (2026). E-lorgesailie: An Agent-Based Simulation Model of Acheulean Lithic Raw Material Procurement and Handaxe Deposition Patterns. Journal of Archaeological Method and Theory, 33(49). https://doi.org/10.1007/s10816-026-09788-y
  • McEneaney, J. E. (2026). Modeling Strategy-based Reading Comprehension Instruction. Preprint available at ResearchGate. DOI:10.35542/osf.io/wxnfa_v1
  • Medina-Labrador, M., Marroquín-Ciendúa, F., Alvarado-Perdomo, L.C., Caballero-Villalobos, J., Hernández-Martínez, J.A., & Vargas-Méndez, J.C. (2026). Chatbots and Socio-Emotional Cues in Corporate E-Learning: Evidence on Learning and Dropout. Revista Academia & Negocios, 12(2), 1-13. https://doi.org/10.29393/RAN12-17CSMM60017
  • Mena-Mejía, S. A., Muyulema-Allaica, J. C., & Balón-Ramos, I. D. R. (2026). An Agent-based Model for Establishing a Symbiotic System in Manufacturing Companies. Computer and Decision Making: An International Journal, 3, 996–1016. https://doi.org/10.59543/comdem.v3i.18322
  • Meng, Q., Zhao, J., Li, Z., & Chong, H. (2026). Navigating unsafe behavioral decision-making in workplaces: An agent-based simulation for intervention and management. WORK: A Journal of Prevention, Assessment, & Rehabilitation. https://doi.org/10.1177/10519815251409139
  • Mensink, E. (2026). The human and the desired path: On the inclusion of pedestrian movement and behaviour in agent-based modelling, explaining the emergence of desire paths [Master's thesis, Wageningen University and Research]. Wageningen University and Research. https://edepot.wur.nl/714114
  • Mertes, P., Kurchyna, V., Bae, Y.E., Berndt, J.O., & Timm, I. (2026). An agent-based exploration of the influence of needs on health protection motivation and intentions. Front. Psychol. 17, 1581156. 10.3389/fpsyg.2026.1581156
  • Mesas, F., Taboada, M., Epelde, F., Cabrera, E., Wong, A., & Rexachs, D. (2026). From Formal Specifications to Executable Simulations: A Computation-Driven Metasystem for Agent-Based Modeling. In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (Eds.), Computational Science – ICCS 2026 Workshops. ICCS 2026. Lecture Notes in Computer Science, vol 16788. Springer, Cham. https://doi.org/10.1007/978-3-032-29915-4_3
  • Mhade, S., Bhosekar, U., Hill, M.D., Sinclair, S., Agrawal, S., Guerrini, J., Pletz, K., Zou, L., Koebcke, A., Kummer, A.G., Ventura, P.C., Del Valle, S.Y., Chinazzi, M., Litvinova, M., Vespignani, A., & Ajelli, M. (2026). Mapping the Landscape of Individual-Based Models for Respiratory Pathogen Transmission in the Pandemic and Post-Pandemic Era (2020–2024): A Systematic Review. Epidemics. https://doi.org/10.1016/j.epidem.2026.100924
  • Michna, M., Kořínek, M., Štekerová, K. (2026). Agent-Based Simulation of Repeated Parking Problem Inspired by El Farol Bar Model. In: Silhavy, R., Silhavy, P. (Eds) Proceedings of the 9th Computational Methods in Systems and Software. Springer. https://doi.org/10.1007/978-3-032-20749-4_29
  • Mishra, K., & Setty, B. (2026). Agentic AI for ESG-Aligned Supply Chain Resilience in Apparel SMEs: A Multi-Agent Framework and Illustrative Simulation. IUP Journal of Management Research, 6(2), 101–118. https://doi.org/10.71329/IUPJMR/2026.25.2.101-118
  • Montero-Moguel, L., Vargas-Alejo, V., & Carmona, G. (2026). Model Development Sequences for Advancing Mathematical Learning of Adults Returning to Higher Education. Education Sciences, 16(4), 587. https://doi.org/10.3390/educsci16040587
  • Moosazadeh, A., Eshrati, B., & Babaee, E. (2026). Assessing the Impact of Rotavirus Vaccination inIranian Children Under Five: An Agent‐BasedModelling Approach. Health Science Reports, 9(6). https://doi.org/10.1002/hsr2.72564
  • Morofuji, H., & Kurahashi, S. (2026). Government Official-Initiated Bid-Rigging: A Simulation-Based Approach Using Gaming and Agent-Based Methods. New Generation Computing 44(14). https://doi.org/10.1007/s00354-026-00316-w
  • Mukherjee, A., Hilzendeger, M., Rinvelt, A., Fatma, S., Schupp, M., Nogare, D.D., & Chitnis, A.B. (2026). Mechanical interactions dynamically influence rosette morphogenesis in the migrating zebrafish Posterior Lateral Line primordium. Development. https://doi.org/10.1242/dev.204973
  • Mustillo, T. (2026). Building a FAIR, linked, and open data ecosystem: innovation cascades in political science. European Political Science, 24(4), 810-825. https://doi.org/10.1057/s41304-025-00520-0
  • Nagura, T., & Akiyama, E. (2026). Effect of Network Homophily and Partisanship on Social Media to "Oil Spill" Polarizations. Journal of Artificial Societies and Social Simulation 29(1).
  • Narayan, P.S. (2026). Simple Agent Based Simulation for Quick Evaluation of System of Systems Architectures. [Master's Thesis, Linköping University].
  • Natalini, D., Sharpe, S., Jones, A., & Barbrook-Johnson, P. (2026). A Dynamic Comparison of the Cost-Effectiveness of Carbon Pricing Policies. Sustainability 18. https://doi.org/10.3390/su18115677
  • Navarrete Velásquez, J. E. (2026). Análisis de la transferencia tecnológica universitaria: una mirada desde la relación universidad–empresa en universidades colombianas. [Doctoral Thesis, Universidad Pontificia Bolivariana]. http://hdl.handle.net/20.500.11912/13708
  • Navarrete, J. E. (2026). Análisis de la transferencia tecnológica universitaria: una mirada desde la relación universidad– empresa en universidades colombianas. Recovered from: http://hdl.handle.net/20.500.11912/13708
  • Németh, D. (2026). Modeling agile SW delivery in NetLogo [Bachelor's thesis, Masaryk University]. Masaryk University Information System. https://is.muni.cz/th/kqv2h/
  • Nguyen, T.T., Olunusi, B.O., Badawy, A., & Bell, A.R. (2026). Explore Aspirations and Capabilities theory in migration in Moroccan Oases under drought: An Agent-Based Approach. In: Comes, T., Dijkman, R. M., Jörin, J., Mühlhäuser, M., Pregnolato, M., Reuter, C., & Schaffner, C. (Eds.). International Conference on Resilient Systems (ICRS) 2026, Delft, The Netherlands: Book of Abstracts, 433-435. Eindhoven University of Technology. https://doi.org/10.6100/qp5f-nb93
  • Nicart, M.R.O., Refuerzo, C.P.L., Gomez, Z.J.A., Galvo, J.J.A., Segundo, K.C., Celis, C.R.G., & Ocena, C. (2026). Simulation-Based Optimization of Drone-Assisted Search and Rescue Operations in a Flood-Prone Area. Kafrelsheikh Journal of Information Sciences, 6(1), 2537-0677.
  • Nicholson, J., & McCusker, S. (2026). It’s in the wind: An opportunity for understanding epidemiological evidence. Proceedings of IASE 2025 Satellite Conference - Statistics and Data Science Education in STEAM. https://doi.org/10.52041/iase25.153
  • Nikulina, A., Forbes, H.F., & Lawrence, D. (2026). Scales, Sources, and Simulations: Reviewing Studies on Human–Environment Interactions in the Mediterranean from 1000 BCE to 1000 CE. Journal of Archaeological Method and Theory, 33 (66). https://doi.org/10.1007/s10816-026-09805-0
  • Ning, H., & Ding, J. (2026). Data-Environment Coverage and the Path from Narrow to General AI: A Cyber-Physical-Social-ThinkingSurvey.
  • Njila, R. C. N., Mostafavi, M. A., Brodeur, J., & Rivest, S. (2026). Monitoring Spatiotemporal Evolution of Dynamic Fields of via Sensor Network Datastream: A Decentralized Event-Driven Approach. International Journal of Geo-Information. https://doi.org/10.3390/ijgi15050194
  • Ntankouo Njila, R.C., & Mostafavi, M.A. (2026). Tracking topological relationships and spatiotemporal changes occurring in vague shape phenomena monitored by sensor network: a distributed fuzzy reasoning approach. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XI(1), 209-216. https://doi.org/10.5194/isprs-annals-XI-1-2026-209-2026, 2026.
  • Nugroho, A. S., Yonanda, B. C. (2026). Analisis Pemerataan Ekonomi Di Kalimantan Selatan Dengan Menggunakan Simulasi Berbasis Agen. Digitial Nexus Systematic Journal 2(1), 1-10.
  • Nwokoye, C. (2026). A Survey of SIR-Based Differential Epidemic Models for Control and Security Against Malware Propagation in Computer Networks. Security and Privacy 9(1). https://doi.org/10.1002/spy2.70138
  • Nwokoye, C.H., Iloka, B.O., Waldron, S., & Ezzeh, P. (2026). Machine Learning Epidemic Predictions Using Mathematical & Agent-Based Wireless Sensor Network Models of Malware Propagation and Control. Proceedings of the International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA 2026), IEEE. 10.1109/ACDSA67686.2026.11468113
  • Obiako, I.V., Walker, S.B., Lippert, K.J., Cloutier, R.J. (2026). Systematic Learning and Pedagogy Through a Modular, Progressive-Based Simulation Modeling Framework for Complex Systems. Systems Engineering. https://doi.org/10.1002/sys.70047Digital Object Identifier
  • Olaru, A., & Hodoroagă, I. (2026). Reconciling Simulation and Distributed Deployment in a MAS Development Framework [Article in preparation]. National University of Science and Technology POLITEHNICA Bucharest.
  • Oruro, E.M., Pardo, G.E., Rasia-Filho, A.A., & Garcia-Cairasco, N. (2026). New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach. Frontiers in Computational Neuroscience, 20(1800523). 10.3389/fncom.2026.1800523
  • Pamungkas, I., Kuncaraningrat, Y.E., & Triani, M. (2026). A Conceptual Framework for Integrating Blue Carbon Ecosystems into Climate Mitigation and Coastal Flood Risk Reduction for Power Plants. In L. R. Sugiarti et al. (eds.), Proceedings of the 2nd International Conference on Social Environment Diversity (ICOSEND 2025), Advances in Social Science, Education and Humanities Research 1011. https://doi.org/10.2991/978-2-38476-565-2_7
  • Paolillo, R., Accordino, F., & Pecoraro, F. (2026). Childbirth Mobilities: A Geo-Spatial Simulation Approach. In M. Giacomini et al. (Eds.), Opening the Personal Gate between Technology and Health Care, 1776-1770. 10.3233/SHTI260528
  • Pary, A.C., Ccori, M.E.G., Aguilar, M.C., Cuevas, L.B., Oruro, E.M., Pacheco-Otalora, L.F., & Pardo, G.E. (2026). Strain-dependent differences in maternal care and early developmental trajectories in laboratory rats. Behavioural Brain, 512. https://doi.org/10.1016/j.bbr.2026.116291
  • Paul, B. K., Kwiatkowski, M., Zhantibiyeva, F., Singh, S., Nguyen, C., Singari, S. V. D., Perumal, C. D., Anandan, I., Adke, A. P., Sadler, K. E., Carrasquillo, Y., Neilan, R. M., & Kolber, B. J. (2026). Predicting Pain Using a Data-Driven Agent-Based Model of the Bilateral Central Amygdala. bioRxiv. https://doi.org/10.64898/2026.04.24.720694
  • Pecoraro, F., & Prosperi, M. (2026). Integrating Causal Inference and Agent-Based Modelling to Assess the Impact of Clinicians' Guideline Adherence in Older Adults Hospitalized with Pneumonia. Studies in health technology and informatics, 336, 373–377. https://doi.org/10.3233/SHTI260180
  • Pellizzari, P., & Parpinel, F. (2026). Illusions and Perceived Wealth: An Agent-based model of Madoff's Ponzi scheme. Working Paper available at https://www.unive.it/dec-workingpapers
  • Pereira, A.P., Laureano, R.M.S., de Lima Neto, F.B. (2026). Simulation of Human Behavior in Adherence to Preventive Health Programmes -- A Methodological Proposal and an Example of Its Applications. A. Rocha et al. (Eds.), Proceedings of 19th Iberian Conference on Information Systems and Technologies (CISTI 2024) (pp. 100-112). Springer. https://doi.org/10.1007/978-3-032-12882-9_9
  • Perry, G.L.W., Simpkins, C.E., Reihana, K., Brock, J.M.R., Bellingham, P.J. (2026). Regeneration failure, fire, topography, and climate interact to drive temperate wet forest landscapes into fire traps. Ecography e08550. https://doi.org/10.1002/ecog.08550
  • Ping, L. (2026). Impacts of standards integration on the effectiveness of green certification: an agent-based model. Environment, Development, and Sustainability. https://doi.org/10.1007/s10668-025-07213-0
  • Pires, R., Torres, P., Valente, N.A., Solteiro Pires, E.J., Reis, A., de Mourna Oliveria, P.B., & Barroso J. (2026). Agent-Based Simulation of Forest Fire Spread with NetLogo. In M. Antona and C. Stephanidis (Eds.), 27th International Conference on Human-Computer Interaction: Vol.16335. HCI International 2025 - Late Breaking Papers (pp. 212-224). Springer. https://doi.org/10.1007/978-3-032-12781-5_14.
  • Pokorny, L. (2026). A Mixed-Methods Investigation into the Economic Impacts of Offshore Wind Development on Commercial Fishing Industries in the Mid-Atlantic Region, with a Specific Focus on New Jersey. SSRN. 10.5281/zenodo.17993777
  • Polhill, G., Heppenstall, A., Batty, M., Hare, M., Colasanti, R., Salt, D., ... & Gamalaldin, Y. (2025, August). A Proposed Nomenclature for Models of Complex Systems. In Social Simulation Conference (pp. 464-474). Cham: Springer Nature Switzerland.
  • Pratama, A., & Pambudi, A. G. (2026). Permodelan dan Simulasi Kebijakan Asta Cita dalam mendorong Kewirausahaan menggunakan Agent-Based Modeling Berbasis NetLogo. Digital Nexus Systematic Journal 2(1), 11-14.
  • Purnomo, H., Aminuddin A. P., R. A., Immawan, T., & Firdaus, F. (2026). Socio-technical transformation of Indonesia’s national food system: An integrated approach based on circular supply chain management, human factors, and agent-based modeling. Challenges in Sustainability 14(1), 157-168. https://doi.org/10.56578/cis140110.
  • Pyke, G.H., Kepert, A.G., & Chivers, W.J. (2026). Lévy foraging hypothesis is a model artefact. Ecological Modelling, 519(111647). https://doi.org/10.1016/j.ecolmodel.2026.111647
  • Qi, H., Guo, Y., Long, F., Zheng, X., & Lu, X. (2026). The Differential Effects of Green Finance Instruments on Forestry Enterprises: An Agent-Based Analysis from China. Forests, 17(2), 233. https://doi.org/10.3390/f17020233
  • Qiu, L., Feng, W., Li, F., Wang, H., & Wu, X. (2026). Shaping social norms to promote individual response behavior in public crises: An agent-based modeling approach. Journal of Cleaner Production, 554, 148014. https://doi.org/10.1016/j.jclepro.2026.148014
  • Qiu, Q., Chen, C., Idziorek, K., & Wong, S.D. (2026). The dynamics of bicycles for disaster recovery and relief. Sci Rep, 16(3903). https://doi.org/10.1038/s41598-025-31962-7
  • Qiu, Y., Peng, L., Chen, X., & Liang, S. (2026). Unveiling dynamics of livelihood transitions and well-being evolution under ecological compensation policies in rural China: A hybrid agent-based modeling approach. Applied Geography, 194(104109). https://doi.org/10.1016/j.apgeog.2026.104109
  • Radović, O., & Radojičić, J. (2026). The Impact of AI on the Fear & Greed Index on Limit Order Book Microstructure and Execution Costs: An Agent-Based Simulation of Crypto Markets. International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management, 61-68. https://doi.org/10.46541/978-86-7233-439-5_531
  • Radović, O., Radojičić, J., & Marinković, S. (2026). Agent-Based Modeling in Economics and Finance: A Simple Cryptocurrency Market. Proceedings of EKONBIZ 2026, 198-206.
  • Railsback, S.F., Bliven, J., Grimm, V., & Kelter, J. (2026). Testing NetLogo model code. Ecological Modeling, 520. https://doi.org/10.1016/j.ecolmodel.2026.111669
  • Railsback, S.F., Harvey, B.C., & Ayllon, D. (2026). InSTREAM 7 User Manual: Model Description, Software Guide, and Application Guide Version 7.4. USDA Forest Service. 10.2737/psw-gtr-276
  • Rajagopal, S. (2026). Exploring the functions and consequences of recruitment behaviours for collective performance in the rock ant temnothorax rugatulus. (Order No. 32442905, University of Georgia). ProQuest Dissertations and Theses, , 107. Retrieved from http://turing.library.northwestern.edu/login?url=https://www.proquest.com/dissertations-theses/exploring-functions-consequences-recruitment/docview/3352546733/se-2
  • Rajagopal, S., Sasaki, T., & Hall, R.J. (2026). Sublethal costs of infection reduce performance of collective tasks in social insects: a modelling study. Research Sqaure. https://doi.org/10.21203/rs.3.rs-8747129/v1
  • Rakotobe, M. H., & Courdier, R. (2026). Modelling organisational sensitivity in sports clubs: A neuro-symbolic agent-based analysis of engagement dynamics. Available at ResearchGate.
  • Rakotobe, M. H., & Courdier, R. (2026). Neuro-symbolic multi-agent simulation of engagement dynamics: Comparison of resilient and fragile organisational profiles. The 11th International Conference on Information and Communication Technology for Intelligent Systems, Apr 2026, Bankok, Thailand. ⟨hal-05578661⟩
  • Ramírez-Mejía, A.-I., Medina-Marín, J., Hernández-Romero, N., Cendejas-Castro, E.-A., & Barceló-Alonso, G. (2026). A Hybrid EBM–ABM Framework for COVID-19 Modeling via Sequential SEIRD Calibration with Particle Swarm Optimization in Mexican Cities. Systems, 14(8), 925. https://doi.org/10.3390/systems14080925
  • Rath, M., & Mellacher, P. (2026). Exploring the effects of Covid-19-policies on intra-household care work division. Graz Economic Papers. Available at uniPUB. URN: urn:nbn:at:at-ubg:3-27986.
  • Rebelato, J. G. B. (2026). Modelagem e framework multiagente para otimização e simulação de cenários de remanejamento de equipes de campo para o restabelecimento de energia em eventos climáticos extremos (Master's thesis, Universidade Tecnológica Federal do Paraná).
  • Ries, B., Guelfi, N., & Sousa, T. (2026). Towards Model Compliance Using Generative Agents: A NetLogo to Sequence Diagrams Experiment. In Proceedings of the 14th International Conference on Model-Based Software and Systems Engineering. SCITEPRESS. https://hdl.handle.net/10993/67998
  • Rojas, C., Otalora, M., Riascos Ochoa, J., Longo, M., & Scott, C. (2026). Institutional network connectivity outweighs land-use zoning in shaping watershed sustainability: a participatory agent-based model in the Colombian Andes. Preprint available at Research Square. https://doi.org/10.21203/rs.3.rs-10395667/v1
  • Rombardo, R. (2026). Navigating Dangerous Terrain: Intraspecific Competition and Movement in Aspen Leaf Miner Larvae. [Master's Thesis, University of Alaska Fairbanks]. ProQuest Dissertations & Theses. http://turing.library.northwestern.edu/login?
  • Rossi, T. J. A., de Carvalho, A. M., & Sarti, F. M. (2026). Agent-Based Modeling of Urban Agriculture: Decision-Making, Policy Incentives, and Sustainability in Food Systems. Complexities, 2(1), 2. https://doi.org/10.3390/complexities2010002
  • Rubin, D.M., Keene, P.A.C., Richards, X.L., & George, A. (2026). Can computational modeling in medical education support a constructionist educational framework? Insights from the seminal literature in Papertian constructionism and system dynamics. Frontiers in Education Digital Learning Innovations, 11. https://doi.org/10.3389/feduc.2026.1743544
  • Rusman, M., Almukarramah, A., & Handayani, D. (2026). Optimizing rice production inputs for enhanced smallholder income: An agent-based modeling and simulation approach. Journal Industrial Servicess, 12(1). http://dx.doi.org/10.62870/jiss.v12i1.38844
  • Sabater, A., López, B., Payarol, S., & Palau, I. (2026). Modelling Together for AI-Based Social Services. In Ahrweiler, P., Gilbert, N. (eds), Participatory Modelling and Simulation to Improve AI-based Public Social Services. Artificial Intelligence, Simulation and Society, 27-40. Springer, Cham. https://doi.org/10.1007/978-3-032-15283-1_3
  • Saez, P., & Salas, P. (2026). A product-driven system for incorporating rush orders into a job shop scheduling problem. Research Square. https://doi.org/10.21203/rs.3.rs-9334145/v1
  • Safdar, M.F., Palka, P., Nowak, R.M., & Alkobaisi, S. (2026). Integrating Clinical Assessment Indicators into Cardiovascular Risk Event Simulation Using Machine Learning and Agent Based Modeling. Applied Science, 16(12). https://doi.org/10.3390/app16125808
  • Salahuddin, T., & Vergil, H. (2026). An Agent-Based Model of Ibn Khaldun’s Asabiyah and Wellbeing: Mechanisms, Simulation, and Empirical Validation With World Values Survey. Human Behavior and Emerging Technologies, 2026(1). https://doi.org/10.1155/hbe2/5552161
  • Salas-Peña, A., & Condeço-Melhorado, A. (2026). How do we learn to be multimodal? An urban multimodal mobility route planner coupling Agent-based models and Multi-agent Reinforcement Learning techniques. SSRN. http://dx.doi.org/10.2139/ssrn.6773312
  • Samanamud, M.R.Z., & Boone, R.B. (2026). Modeling the impacts of grazing management on livestock behavior and performance: A case study in the Peruvian Andes. Grassland Research, 1-14. https://doi.org/10.1002/glr2.70060
  • Sánchez-Martínez, J., Komninos, N., Gkitsa, E., & Panori, A. (2026, July). Interactive Optimization for Urban Carbon Neutrality Through Renewable Energy and Nature-Based Solutions. In International Conference on Human-Computer Interaction (pp. 326-351).
  • Sanogo, K., Benhafssa, A.M., & Sahnoun, M. (2026). Transitioning from AGVs to AIVs in integrated job shop scheduling with transportation tasks: a multi-agent simulator for comparative analysis. Simulation, 102(1), 19-43. https://doi.org/10.1177/00375497241288894
  • Santi, E.D. (2026). Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2603.18677
  • Sapienza, A., & Falcone, R. (2026). Trust-Regulated Dependence Networks for Multi-Agent Cooperation: A Simulation Study. Electronics 15(11). https://doi.org/10.3390/electronics15112461
  • Sauke, F., Meyer, K. M., Pincebourde, S., & Wiegand, K. (2026). APPLE-POPDYN: An individual-based model linking apple-tree productivity and leaf miner infestation dynamics. Arthropod-Plant Interactions 20(23). https://doi.org/10.1007/s11829-026-10233-w
  • Saves, P., Mastio, M., Verstaevel, N., & Gaudou, B. (2026). From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models. Proceedings of the 27th International Workshop on Multi-Agent-Based Simulation. https://hal.science/hal-05564415/
  • Sayfullayevich, M.A. (2026). Modeling Urban Migration Patterns Using Social and Economic Indicators. American Journal of Alternative Education, 2(6), 103-105. https://scientificbulletin.com/index.php/AJAE/article/view/1046/1254
  • Scharf, I. (2026). Demonstrating limitations of area-restricted search as a foraging mechanism. Oikos, e12564. https://doi.org/10.1002/oik.12564
  • Schubotz, L., Chappin, E., Scholz, G., & de Vries, G. (2026). Participatory inverse modelling for behavioural insights into Dutch EV charging dynamics. Scientific Reports. https://doi.org/10.1038/s41598-026-56059-7
  • Schuengel, C., Fearon, P., Wass, S., Mahmoud, M., Minnis, H., Van Heerden, A., Bakkum, L., Persico, F., Provida, K., Oosterman, M., Scheper, F. (2026). Behavior and the Development of Attachment: Innovating Assessment and Theory Building. Psychoanalytic Inquiry, pp.1–19. https://doi.org/10.1080/07351690.2025.2609520
  • Sego, T.J., König, Fonseca, L.L., Pathirana, D., Bergmann, F.T., Grecco, H.E., Silberberg, M., Ray, S., Fain, B., Knapp, A.C., Tiwari, K., Hermjakob, H., Sauro, H.M., Glazier, J.A., Laubenbacher, R.C., & Malik-Sheriff, R.S. (2026). EFECT enables reliable and reproducible stochastic simulations across disciplines. npj Complexity. https://doi.org/10.1038/s44260-026-00094-y
  • Serdyuk, A. (2026). Mechanisms of Organizational Adaptation To Social Disruption Caused by the Full-Scale Russian Invasion of Ukraine. [Doctoral Dissertation, National University of Kyiv-Mohyla Academy]. eKMAIR: https://ekmair.ukma.edu.ua/handle/123456789/40549
  • Sert, M. F., & Aydin, S. (2026). Agent-Based Simulation Enhanced by Multilayer Perceptron for Management Information Systems: An Application on Open Education System. Anadolu University Journal of Economics and Administrative Sciences 27(1), 547-573. https://doi.org/10.53443/anadoluibfd.1790925
  • Shiavo, M., Aubert, P.M., & Le Mouël, C. (2026). Modelling the structural and land use effects of support policies for the expansion of organic farming: The case of organic dairy farms in France. Land Use Policy 169. https://doi.org/10.1016/j.landusepol.2026.108163
  • Shrikant, A., Andelfinger, P., & Cai, W. (2026). LLM-Assisted Credibility Scoring for Simulation Models from Raw Time-Series via Automatic Visualization and Interpretable Indicators. In: Neumann, P., Puma, M.J., Lees, M.H., Groen, D., Dongarra, J.J., Sloot, P.M.A. (Eds.), Computational Science – ICCS 2026. 16784, 94-108. https://doi.org/10.1007/978-3-032-29924-6_7
  • Silva, A.C.M., & Neves, J.E.D. (2026). Segurança da informação como estratégia de gestão: avaliação de treinamentos personalizados para prevenção de phishing. Revista Gestão & Tecnologia, 26(2), 216–241. https://doi.org/10.20397/2177-6652/2026.v26i2.3251
  • Silva, R.F.M., Oliveira, S.R.M., & Bergier, I. (2026). From Empirical Evidence to Canonical Modeling: An Agent-Based Model of the Brazilian Cattle Trade Network. Agriculture, 16(12). 10.3390/agriculture16121254
  • Sime, M. G. (2026). Agent-Based Technology Diffusion Model to Enhance Integrated Development of Clustered Small and Medium Enterprises (SMEs). Preprint available on ResearchSquare. https://doi.org/10.21203/rs.3.rs-9725282/v1
  • Skelton, M.L., Leonard-Duke, J., Astrab, L.R., Goedert, J.A., Hannan, R.T., Peirce, S.M., Sturek, J.M., & Caliari, S.R. (2026). An agent-based model suggests how senescent cell behavior and matrix mechanics drive pulmonary fibrosis in aged mice. bioRxiv. https://doi.org/10.64898/2026.05.01.722023
  • Slooten, E., Myers, L.S., & Nabe-Nielsen, J. (2026). An agent-based approach for designing effective protection. bioRxiv. https://doi.org/10.64898/2026.04.03.716393
  • Smarzhevskiy, I., & Pavlov, O. (2026). Communication features of projects aimed at green and low-carbon transformations of operational procedures of energy companies. Unconventional Resources 11. https://doi.org/10.1016/j.uncres.2026.100336
  • Soleimanzadeh, Z., & Ahmadi, A. (2026). A distributed socio-hydrological framework for integrating perception and groundwater dynamics in farmers' crop choice modeling. Scientific Reports. https://doi.org/10.1038/s41598-026-44157-5
  • Soleimanzadeh, Z., & Ahmadi, A. (2026). Simulating Farmer Decision-Making under Groundwater Quantity-Quality Constraints: An Agent-Based and Game-Theoretic Approach. Available at SSRN: http://dx.doi.org/10.2139/ssrn.6993099
  • Stasiowska, D. (2026). Computer Modelling For Extreme Environments – How BEEHAVE Model Can Be Used For Extraterrestrial Applications and Establishing Directions of Future Studies. AGH University of Krakow.
  • Stein, J., Cruz, S., Grossi, D., & Testori, M. (2026). Free information disrupts even Bayesian crowds. Proceedings of the National Academy of Sciences 123(14). https://doi.org/10.1073/pnas.2518472123
  • Studenikin, L.Y., & Zamyatina, E.B. (2026). An Experience in Research and Analysis of Bioinspired Routing Algorithms in VANET Networks. Proceedings of the 2026 International Russian Smart Industry Conference (SmartIndustryCon), IEEE. 10.1109/SmartIndustryCon68821.2026.11493006
  • Sun, D., & Zhang, H. (2026). The Dilemma of Fitness of Higher Vocational Education Specialty Settings and Regional Industrial Structure and the Path of Governance: An Empirical Study Based on Beijing Municipality. In A. Y. M. A. Islam et al. (eds.), Proceedings of the 2025 International Conference on Educational Technology and Management Information Systems (ETMIS 2025), Advances in Computer Science Research, 129. https://doi.org/10.2991/978-94-6239-630-2_28
  • Sznajd-Weron, K., & Kamińska, B. (2026). Emergence of Pluralistic Ignorance: An Agent-BasedApproach. Handbook of Computational Social Psychology. https://doi.org/10.31235/osf.io/6gd4w_v1
  • Tagoe, E.T., Nonvignon, J., Van Der Meer, R., & Megiddo, I. (2026). Improving treatment outcomes in Ghana with agent-based model for diabetes patients' self-management behaviours. Health Care Management Science 29(35). https://doi.org/10.1007/s10729-026-09772-8
  • Tang, X., & Lira, M. (2026). How Drawing Unlocks Students' Developing Mechanistic Reasoning about Complex Systems in Physiology. Advances in Physiology Education. https://doi.org/10.1152/advan.00107.2026open_in_new
  • Tarantino, R. (2026). Agent-based modelling of a nematode system provides general insights into the evolutionary constraints and modulators of phenotypic plasticity, bet-hedging, and environmental homeostasis. bioRxiv. https://doi.org/10.64898/2026.06.02.729670
  • Tarantino, R., Contino, S., Gugliotta, L., Indelicato, G., Panunzi, G., Bertolazzi, G., & Romano, V. (2026). An Agent-Based Model of Protein Polymerization Dynamics: Focus on the Actin System. bioRxiv preprint, https://doi.org/10.64898/2026.01.02.697380
  • Tarantino, R., Contino, S., Gugliotta, L., Indelicato, G., Panunzi, G., Bertolazzi, G., Romano, V. (2026). Agent-Based Model Replication of Global Treadmilling and Competition in the Actin Polymerization System. doi: https://doi.org/10.64898/2026.01.02.697380
  • Tarek, J., Boelee, E., Jensen, P. K. M., De Vlas, S. J., Van de Walle, B. (2026). Relative contributions of public and domestic transmission domains in cholera outbreaks in displacement camps: An exploratory agent-based modelling study. Epidemiology and Infection. https://doi.org/10.1017/S0950268826101575.
  • Terang, L., Nath, J., Goswami, M., Brahma, H., Parbin, M., Bordoloi, A., & Sarma, K. (2026). Agent-based modelling of urban macaque-human conflict: evaluating mitigation strategies in Guwahati, India. Research Square. https://doi.org/10.21203/rs.3.rs-9487556/v1
  • Thomopoulos, V., Petropoulou, M., Convertino, F., & Tsichlas, K. (2026). Agent-Based Modeling of Agricultural Plastic Waste Generation: A First Approach. Available at SSRN: https://dx.doi.org/10.2139/ssrn.6098247
  • Tian, H., Zuo, R., Wang, D., Qian, G., Ye, Q., Kawczynski, A., Trybulski, R., & Clemente, F. M. (2026). The exploration of immune system function changes in marathon athletes after high-intensity training by Agent-Based Model. Frontiers in Sports and Active Living 8. https://doi.org/10.3389/fspor.2026.1780114
  • Tian, Y., Zhang, J., Zhu, Y., Xu, F., Kan, H., Kannan, L., Meganathan, N., & Yiliu Li, A. (2026). From Advisor to Voting Teammate: Institutional Authority and Information Structures of AI Agents in Bounded-Rational Human Groups. In Proceedings of Workshop on Human-Agent Collaboration (CHI 2026). https://doi.org/10.1145/nnnnnnn.nnnnnnn
  • Tóth, Z. (2026). Modelling the interplay between inadvertent social information use and a pesticide-induced foraging bias in bumblebees’ crop visitation. Current Zoology. https://doi.org/10.1093/cz/zoag041
  • Trentesaux, D. (2026). Design and application of non-centralized control architectures: evolution from the 1990s to the 2020s. Université polytechnique Hauts-de-France, Hal Open Science. https://uphf.hal.science/hal-05581341
  • Trounstine, J. (2026). When restrictive economic zoning leads to racial segregation. Political Science Research and Methods, 1-17. https://doi.org/10.1017/psrm.2026.10089
  • Tsurushima, A., & Miyano, S. (2026). Resilience in Evacuation Guidance: A Cognitive Agent Approach to System Failures. SN Computer Science 7. https://doi.org/10.1007/s42979-026-04834-8
  • Uhde, A., Sadeghian, S., & Hassenzahl, M. (2026). Noise-Canceling, Silent Speech, or Quiet Zones? Technology and Social Soundscape Negotiation on Trains. ResearchGate. 1 (1).
  • Ullah, I. I. T. (2026). Sampling Matters: What Simulation Modeling and Microrefuse Sampling Practice Reveal about Archaeological Sampling, Training, and Design. Advances in Archaeological Practice. https://doi.org/10.1017/aap.2025.10146
  • Uner, B.Y., Demir, A., Zhou, P., Taskiran, E.Z., & Wassenaar, T. (2026). Peptide-nanoparticle platforms for antisense therapeutics: A coarse-grained modeling approach to brain delivery. Computers in Biology and Medicine, 203(111479). https://doi.org/10.1016/j.compbiomed.2026.111479
  • Valentini, O., Baruffini, M., Maggi, E., & Vallino, E. (2026). Eco-innovation diffusion in retailers' last-mile delivery: an Agent-Based Model approach. Transport Policy, https://doi.org/10.1016/j.tranpol.2026.104037
  • van Bruggen, A. R., Hoekstra, J., Claassen, L., Princen, M., ter Hoeven, E., van Burgsteden, M., & te Brömmelstroet, M. (2026). Navigating sustainable mobility and healthy neighbourhood spatial design in a contested planning context: lessons from ENKA’s transformative modelling process. Civil Engineering and Environmental Systems, 1–40. https://doi.org/10.1080/10286608.2026.2710079
  • van der Maas, H. L. J., Borsboom, D., & Waldorp, L. (2026). The statistical physics of psychological networks: Zero matters.Psychological Review. Advance online publication. https://doi.org/10.1037/rev0000611
  • van Dijck, A. (2026). Perception of Places in an Urban Context: Towards a Modelling Framework. [Masters Thesis, Universiteit Utrecht].
  • Vance, E., Clarke, B., Ivanova, S. (2026). Agentic AI and Workflow Orchestration: Balancing Automation, Ethics, and Human Oversight in Enterprise Applications.
  • Verma, D., Mumm, O., & Carlow, V. M. (2026). Seeking shade: an agent-based model to study shadow-focused pedestrian movement: Case study in Berlin. Transportation Research Interdisciplinary Perspectives 36. https://doi.org/10.1016/j.trip.2026.101883
  • Vernier, C., Chapuis, M.P., Rossi, J.P., & Piou, C. (2026). Strong alignment between individuals and attraction to trophic resource in patchy landscapes favour group foraging in locusts nymphs. Ecological Modelling, 521(111749). https://doi.org/10.1016/j.ecolmodel.2026.111749
  • Vineeta, K., & Surana, A. (2026). Identification and Classification of Virtual Lab Apps in Science: A Content Analysis Approach. International Journal of Special Education, 41(10s), 1221-1238. https://www.internationalsped.com/index.php/ijse/article/view/3796
  • Vishkin, A., Krasner, R., & Wely, M. (2026). Gender equality predicts female over-representation only in competitive domains where they have a relative advantage over males. PNAS Nexus. https://doi.org/10.1093/pnasnexus/pgag035
  • Viveros, N.C., & Velurtas, M.C. (2026). Agentes, Territorio y Mercado Gore: Una Simulación Basada en Agentes del Riesgo Juvenil en el Suroriente de Ciudad Juárez. Ciencia y Reflexión - Revista Científica Multidisciplinaria, 5(2), 1487 - 1514. https://doi.org/10.70747/cr.v5i2.1188
  • von Bressensdorf, L. (2026). My-side bias, availability bias, and social interaction. Synthese, 208(75). https://doi.org/10.1007/s11229-026-05718-3
  • Vozna, A., Monaldini, A., Costantini, S., De Gasperis, G., dell’Acqua, P., Formisano, A., & Rafanelli, A. (2026). Evolution of Programming Languages in Agent Systems and the Role of Computational Logic. In: Mascardi, V., Omicini, A. (eds) The Agents Journey. Lecture Notes in Computer Science, 16395, 3-51. Springer, Cham. https://doi.org/10.1007/978-3-032-22940-3_1
  • Wakefield, L., Cain, A., Cerny, C., Reed, H., & Belsare, A.V. (2026). A simulation-based approach to strengthen chronic wasting disease surveillance in captive cervid populations. PLOS ONE 21(6): e0350825. https://doi.org/10.1371/journal.pone.0350825
  • Wan, Y., Cai, W., Li, W., Chen, Y., Li, Y., & Hao, G. (2026). Escaping the Rising Flow: A Social Force Model for Underground Flood Evacuation Incorporating Drag, Heterogeneity, and Leader-Following. ISPRS International Journal of Geo-Information, 15(6), 265. https://doi.org/10.3390/ijgi15060265
  • Wang, J., He, D., Chen, K., Wang, Y., & Long, Y. (2026). Policy Incentive Mechanisms for Low-Carbon Pathway Selection in Forest Tourism Destinations. Sustainable Development. https://doi.org/10.1002/sd.71157
  • Wang, J., Shen, X., Yang, S., & Yin, F. (2026). Reducing Unsafe Behaviors in Aviation Maintenance: A Study on Formation Mechanism and Intervention Strategies Based on System Simulation. Aviation 30(1), 62-83. https://doi.org/10.3846/aviation.2026.26000
  • Wang, S., Li, W., & Wang, Z. (2026). Highway Collaborative Transportation Regulation Strategies and Dynamic Simulation in Provincial Region. ResearchSquare. https://doi.org/10.21203/rs.3.rs-9174467/v1
  • Wang, Y. (2026). A machine-learning and agent-based modeling framework for tourism crisis management. Scientific Reports. https://doi.org/10.1038/s41598-026-63675-w
  • Wang, Y., Zhu, Y., Li, X., Zhang, X., Li, S., Zhang, H., Wang, Y., Zhang, Y., Li, Q., & Wang, F.Y. (2025). From Scenarios Engineering to Scenarios Intelligence: Microworld Models for Embodied AI Based on Parallel Intelligence. IEEE, 56(1), 32-45. 10.1109/TSMC.2025.3618120
  • Watts, F.M., Liu, L., Ober, T.M., Song, Y., Valle, E.J.D., Wang, Y., & Zhai, X. (2026). AI Evaluation and Feedback to Support Middle-School Students’ Scientific Argumentation and Reasoning. In: Blanchard, E.G., Chen, G., Chi, M., Isotani, S. (Eds.), Artificial Intelligence in Education. 16585, 507-515. https://doi.org/10.1007/978-3-032-29770-9_55
  • Wehr, N. H., Rosenberry, C. S., Stainbrook, D., Staats, M., Korman, A. L., Walter, W. D. (2026). Statewide agent-based model for management of chronic wasting disease in white-tailed deer using R and NetLogo: PAOvCWD. MethodsX. https://doi.org/10.1016/j.mex.2026.103823
  • Wei, J., & Liu, F. (2026). Path Simulation and Evolution Mechanism of Risk Contagion in Construction Project Portfolios. Complexity, 1. https://doi.org/10.1155/cplx/6286835
  • Whiteley, A. (2026). Modeling the Effects of Urban Green Spaces on Crime Rates. Society for Industrial and Applied Mathematics. https://www.siam.org/publications/siam-news/articles/modeling-the-effects-of-urban-green-spaces-on-crime-rates/
  • Wicaksana, I.D., Salsabilla, S., & Fitrahirsca, A.P. (2026). Simulasi Penyebaran Penyakit Menular HIVDi Ibu Kota Jakarta Menggunakan Permodelan NetLogo. Digital Nexus Systematic Journal, 2(1), 22–26.
  • Wickramanayaka, D. M. R. (2026). How We Apply Agent-Based Modeling of Climate-Induced Internal Migration in Sri Lanka.
  • Widino, A. A., Ariyanto, K., & Putro, U. S. (2026). Stimulating Green Growth in Bandung's Food Sector: An Agent Based Simulation on Food Waste Problem. International Journal of Current Science Research and Review 9(4). https://doi.org/10.47191/ijcsrr/V9-i4-35
  • Xanthopoulou, T.D., Prinz, A., Lunde, H.B., Puga-Gonzalez, I., & Shults, F.L. (2026). Transparent and Accessible ABMs with FODD: Automatic Code from Formal ODD. AAMAS 2026: Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems, 995-1003. https://doi.org/10.65109/XDBV4230
  • Xu, C., Hui, P.M., Xia, C., & Johnson, N.F. (2026). How hate spreads online and why it returns: Re-entrant phases driven by collective behavior. Phys Rev E, arXiv. https://doi.org/10.48550/arXiv.2605.21129
  • Xu, C., Hui, P.M., Xia, C., & Johnson, N.F. (2026). Reentrant spreading in a two-species coalescence-fragmentation model with susceptible-infected-recovered dynamics. Phys Review E, 113 (054312). 10.1103/pghw-mmzz
  • Yang, W., Li, M., Jiang, X., Hu, D., Liang, H., Jiang, X., Liu, X. (2026). An Urban Waterlogging Dynamic Vulnerability Assessment Framework Integrating Hydrological-hydrodynamic Model, Multi-agent Modeling, and the Coquet Integral. Water Resources Management 40(114). https://doi.org/10.1007/s11269-026-04499-1
  • Yasin, A., Fatima, R., Puspitasari, I., Dogar, A.B., Yang, L., & Ishaq, K. (2026). From Prey to Protect: Exploring Social Engineering Through Games and Simulation. Trust, Awareness, and Phishing Defense. International Journal of Information Security 25(32). https://doi.org/10.1007/s10207-025-01193-y
  • Yaw, M.N., Kpieleh, F., Mulahuwaish, A., Qolomany, B., & Abdo, J.B. (2026). Training versus hiring in security operations centers: Agent-based and reinforcement learning simulations of training duration effects on CyberTeam performance. Available at ResearchSquare. https://doi.org/10.21203/rs.3.rs-9044748/v1
  • Yevgeny D. Patarakin, Svetlana N. Vachkova, & Elena Y. Petrayeva. (2026). Mediating complexity: computational models as tools for developing systemic thinking about urban education. Russian Journal of Education and Psychology, 17(1), 471–505. https://doi.org/10.12731/2658-4034-2026-17-1-952
  • Yu, G., Barbera, C., Burton, M., Alder, J., Wittemyer, G., Mayo, C., & Perkins, T.A. (2026). A spatial, agent-based model to explore mechanisms of bluetongue virus persistence at the interface of domestic and wildlife ruminant populations.
  • Yuandita, D., Hardiansyah, Mase, L.Z., Amri, K., Supriani, F. (2026). Agent-Based Modeling of Vertical Tsunami Evacuation in EngganoIsland, Indonesia: Route Dynamics, Shelter Capacity, and Behavioral Performance. Journal of the Civil Engineering Forum, 12(2), 170-188. https://doi.org/10.22146/jcef.24204
  • Zerzzari, W., Charaf, L. A., Azzouzi, S., & Charaf, M. E. H. (2026). A Multi-Agent-Based Approach for Semantic Conformance and Interoperability Testing in IoV Environments. Sensors, 26(14), 4550. https://doi.org/10.3390/s26144550
  • Zhang, C., & Gaudiano, P. (2026). From Retail Scheduling to Supply-Chain Living Wages: An Agent-Based Simulation to Quantify the Business Value of Improving Job Quality for All. DOI:10.20944/preprints202605.1593.v1
  • Zhang, C., Ren, J., & Xu, Y. (2026). A Virus Propagation Model for Scale-Free Networks Incorporating Dynamic Behavioral Responses. IAENG International Journal of Applied Mathematics 56(5), 1832-1845.
  • Zhang, X., Chen, D., & Yao, S. (2026). Effect evaluation of carbon sequestration of grassland ecological compensation policy: Evidence from China’s Yellow River Basin. Chinese Journal of Population, Resources and Environment, 24(1), 27-40. https://doi.org/10.1016/j.cjpre.2026.03.003
  • Zhang, Y. (2026). Research on Modern Transformation and Cultural Expression of Traditional Cultural Symbols in Visual communication Design. Rock Art Research, 1(2). https://rockartresearch.com/index.php/rock/article/view/80159
  • Zhang, Y., Parrott, L., & Apps, C. (2026). Data-driven agent-based modeling for animal space use prediction – an applied example for grizzly bears in the southern interior of British Columbia. Ecological Modelling, 516. https://doi.org/10.1016/j.ecolmodel.2026.111536
  • Zheng, X., Luo, C., Wu, C., Zhang, Z., Zhang, Y., & Skitmore, M. (2026). A Network View of Relational Behavior Diffusion in Mega Construction Projects Incorporating Reciprocal Preference. Journal of Construction Engineering and Management, 152(4), 1-19. Article 04026009. https://doi.org/10.1061/JCEMD4.COENG-16923
  • Zhou, R., Quan, L., & Lei, Y. (2026). Annular layered airspace design in dense urban obstacle environments. Scientific Reports. https://doi.org/10.1038/s41598-026-64706-2
  • Zhou, S., Qu, S., Lu, Y., & Hu, S. (2026). A bottom-up approach to urban functional zones identification: coupling crowdsourced data and agent-based modeling. Geo-Spatial Information Science, 1–19. https://doi.org/10.1080/10095020.2026.2631360
  • Zhu, A., Feng, W., Zheng, H., Huang, Y., He, X., Zhou, C., Yang, L.E. (2026). An Agent-Based Model for Simulating Flood Governance and Community Resilience. MethodsX. https://doi.org/10.1016/j.mex.2026.103820
  • Zhu, X. & Fu, N. (2026). Modeling and Stability Analysis of Malware Propagation in Hierarchically Protected WSNs Based on Epidemiological Theory. IET Control Theory & Applications 20(1). https://doi.org/10.1049/cth2.70100
  • Zhu, Y., Ocké, M.C., de Vet, E. (2026). Exploring spillover effects beyond workplace dietary interventions using agent-based modeling. Public Health Nutrition. doi: 10.1017/S1368980026102018
  • Zhuang, Y. (2026). The Digital Transformation Path of Cross-Border E-Commerce cross-Cultural Communication on the “Digital Silk Road” under the Perspective of Enterprise Operation Synergy. International Journal of Computer Information Systems and Industrial Management Applications 18. https://doi.org/10.70917/ijcisim-2026-0175
  • Μπερμπερίδης, Α. (2026). Υπολογιστική ανάλυση της επιδημιολογικής εξάπλωσης ιού με Agent-Based μοντέλα: χρήση της πλατφόρμας Netlogo.
  • Баян, Ә. Т. (2026). Молекулалық физиканы оқытудағы "есептеу-верификация" әдістемесінің тиімділігі: PhEt және Netlogo орталарындағы эксперименттік талдау [The effectiveness of the "calculation-verification" methodology in teaching molecular physics: Experimental analysis in PhEt and Netlogo environments]. Qazaq Journal of Young Scientist, 4(4), 29. https://qazaqjournal.kz/article/view/599
  • Кутепова, Л. И., Синева, А. И., Исламова, Г. И., & Панков, А. В. (2026). АНАЛИЗ АГЕНТ-ОРИЕНТИРОВАННЫХ МОДЕЛЕЙ ЦИФРОВОЙ ЭКОНОМИКИ В ОБРАЗОВАНИИ. Управленческий учет, (6), 265-271.

2025

  • Burgess, M. D., Sheehan, D. K., White, P. J., Anderson, G. Q., Fisher, G., Grice, P. V., ... & Norris, K. (2025). Inadequate reproductive success is a potential cause of Spotted Flycatcher (Muscicapa striata) population decline in England. Ibis.
  • Thadi, A. (2025). Evolution of Reproduction of Crickets in Extreme Environments: Offspring Investment and Mate-Finding Strategies in Hawaiian Lava Crickets and the Pacific Field Cricket (Doctoral dissertation, University of Minnesota).
  • Zandbergen, B., Quist, J., Blanksma, N., de Waart, W., & Warnier, M. (2025, August). An Agent-Based Modelling Study on Reuse, Rebound Effects and Value Change in Textiles. In Social Simulation Conference (pp. 689-697). Cham: Springer Nature Switzerland.
  • Abas, H. M., Ruzieva, G., Rajesh, D., Matyakubov, M., Sundara Balamurugan, P., & Gurudiwan, P. (2025). Modelling Insect Dispersal in Agricultural Landscapes Using Agent-Based Models (ABM). Natural and Engineering Sciences, 10(2), 305-314. https://doi.org/10.28978/nesciences.1763848
  • Abbasi, E. (2025). Climate Change--Driven Evolution of Insecticide Resistance in Malaria Vectors. Authorea. DOI: 10.22541/au.176719541.18336405/v1
  • Abdelshafie, A., Salah, M., & Kramberger, T. (2025). The Environmental Impact of Inland Empty Container Movements Within Two-Depot Systems. Applied Sciences, 15(14), 7848. https://doi.org/10.3390/app15147848
  • Abedini, V. and abdoli, G. (2025). Agent-Based Simulation of the Effect of Generosity Behavior Variable on Wealth Distribution in Urban Fabric. Journal of Social Problems of Iran, 15(2), 177-195. doi: 10.22059/ijsp.2025.380082.671250
  • Aghamohammadghasem, M. (2025). Simulation- and Machine Learning-Based Methods for Inland Waterway Operation and Maintenance Decision-Making (Publication No. 32166607)[Doctoral dissertation, University of Arkansas). University of Arkansas ProQuest Dissertations & Theses. http://turing.library.northwestern.edu/login?url=https://www.proquest.com/dissertations-theses/simulation-machine-learning-based-methods-inland/docview/3255207345/se-2?accountid=12861
  • Aguilera-Ontiveros, A., Alferes-Varela, G. (2025). Python para el modelado y simulación basado en agentes en las ciencias sociales. El Colegio de San Luis. https://colsan.repositorioinstitucional.mx/jspui/handle/1013/1773
  • Agustín, V. (2025). The Future as Origin.
  • Akinnubi R. T. & Oziegbe R. (2025). Labour–Carbon Dynamics in Rural Nigeria: Econometric Modelling for Inclusive Biochar Value Chains and Global Market Integration. Journal of Education, Science and Technology 2025, (1)1, 94-102.
  • Al-Jaberi, L. A. (2025). Exploring New Energy Solutions in Kongsvinger using AI Methods (Master's thesis, Oslo Metropolitan University).
  • Alaliyat, S., Oucheikh, R. (Eds.). (2025). Agent-Based Modeling of Urban Residential Electricity Demand Incorporating EV Charging Behavior. Proceedings of the Communications of the ECMS. ECMS 39(1). https://www.scs-europe.net/dlib/2025/ecms2025acceptedpapers/0455_simo_ecms2025_0050.pdf
  • Alamro, A. S., & Alsulaiman, F. A. (2025). Adaptive Trust-Based Access Control with Honey Objects and Behavior Analysis. Applied Sciences, 16(1), 242. https://doi.org/10.3390/app16010242
  • Albati, M., Bui, H., Reihani, S., Yadav, V., Mandelli, D., & Mohaghegh, Z. (2025). Integrated Human Reliability Analysis Methodology for External Control Room Emergency Response Scenarios: Application to Modeling FLEX Human Actions in Nuclear Power Plants. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 1-41.
  • Aldea, N., Beltrán-Sánchez, H., Daza, S., García-Aguirre, A., Palloni, A. (2025). Generalized Stable Population and Agent-Based Models of phenotypic transmission in human populations, with an application to body size. Human Hereditary. https://doi.org/10.1159/000549053
  • Alebrahim, E. (2025). Agent-Based Models in Industrial Economics: A Perspective of the Kuwaiti Housing Market Structure. Arab Journal of Administrative Sciences, 32(2), 389–433. https://doi.org/10.34120/ajas.2025.1281
  • Alegre, A. (2025). Capturing cross-cutting concerns in agent-based models using computational effects [Master's thesis, University of Saskatchewan]. Harvest. https://hdl.handle.net/10388/17401
  • Alekseeva, E. (2025). The Problem of Epistemic Injustice and Multi-Agent Model of Epistemic Diversity. Philosophy Journal of the Higher School of Economics, 9(4), 194-220. https://doi.org/10.17323/2587-8719-2025-4-194-220
  • Alford, J. G., Orozco, A. R., Lutterschmidt, W. I., Williams, J. K., & Godwin, W. (2025). PFSt-SIM: an agent-based pine forest stand simulator. Journal of Simulation, 1–18. https://doi.org/10.1080/17477778.2025.2524357
  • Alkaher, D., Frenkel, M., Taylor, E., & Bengo, Y. Tug of War: Modeling Innovation Contagion and Resistance in Public Sector Organizations. Available at SSRN 5225828.
  • Allameh, S. and Ghandehari, M. (2025). Extended Producer Responsibility in the Apparel Industry: An Agent-Based Simulation Incorporating Physical and Emotional Product Durability. Industrial Management Journal, 17(3), 161-179. doi: 10.22059/imj.2025.395722.1008247
  • Almolaa, O.S., Alkawaz, M.H., Al-Arbo, Y., & Mahmood, H.F. (2025). Software-Defined Networking-Enhanced Fisheye State Routing Framework for Mobile Wireless Sensor Networks. Ingenierie des Systemes d'Information 30(11), pp. 3037-3044.
  • Alonso-Adame, A., Farahbakhsh, S., Van Meensel, J., Marchand, F., & Van Passel, S. (2025). Factors to scale out innovative organic farming systems: A case study in Flanders region, Belgium. Agricultural Systems, 224, 104219.
  • Alonte, J., Benalla, P., Jamito, E.R.A., Salvador, M.A., Tolentino, J., & Villanueva, R. (2025). Dynamics of Rabies Spread Using Mathematical Modeling and Simulation. Kafrelsheikh Journal of Information Sciences 5(2).
  • Alova, C.A.R. (2025). On the Fractal Dimension of the Coastline of Negros Island, Philippines. Journal of Higher Education Research, 13, 93-108. 10.70228/JHER2025026
  • Alvarenga, V. F. O., Mesquita, O. C., Magalhães, F., & da Mata, A. S. (2025). O primeiro passo rumo à pesquisa e ao ensino superior: um relato da experiência sobre BIC Júnior. Studies in Education Sciences, 6(2), e16891-e16891.
  • Álvarez, C., Calderón, M., Llamirez, C., Gil-Costa, V., & Marin, M. (2025). Simulación del desplazamiento estudiantil entre aulas en la experiencia académica. JAIIO, Jornadas Argentinas De Informática, 11(15), 1-14.
  • Alves, Bruno C.; Caldas, Letícia B.; F. Junior, Ulisses G.; Santos, Agatha C. S.; Grossmann, Rodolfo B.; BORN, Míriam B.; Aguiar, Marilton S. de. Sistema Multiagente para simulação de propagação viral no contexto da COVID-19 utilizando a ferramenta NetLogo. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 19. , 2025, Fortaleza/CE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 324-331. ISSN 2326-5434. DOI: https://doi.org/10.5753/wesaac.2025.37558.
  • Alves, D., Ferreira, L., & Rossetti, R. (2025). Metro Station Evacuation Simulation: A Case Study Using Three Agent-Based Exit Models. 2025 IEEE International Smart Cities Conference (ISC2), doi: 10.1109/ISC266238.2025.11293308.
  • Alwasel, A., Fakhimi, M., Mustafee, N., & Stergioulas, L. (2025).Modeling and Simulation for Behavioral Analysis in Healthcare: A Review. ACM Transactions on Modeling and Computer Simulation.
  • Amasa, S. N. C., Beleta, T. M. P., Montilla, S. L., & Llantos, O. E. (2025). Simulating HIV transmission dynamics: An agent-based approach using NetLogo. PloS one, 20(9), e0330456.
  • Amasa, S.N.C., Beleta, T.M.P., Montilla, S.L., & Llantos, O.E. (2025). Simulating HIV transmission dynamics: An agent-based approach using NetLogo. PLoS One 20(9): e0330456. https://doi.org/10.1371/journal.pone.0330456
  • An, S., Hammock, J., & Goel, A. (2025). How Online Learners Engage in Self-Directed Modeling: A Behavioral Analysis. International Journal of Artificial Intelligence in Education, 35, 3501-3528.
  • Anastasiou, B. (2025). Computational analysis of the epidemiological spread of a virus with Agent-Based models: use of the Netlogo platform [Thesis, National Technological University of Athens]. DSpace NTUA, 05113902.
  • Ancapi, F.B., Kleijweg, M., Van den Berghe, K., Yorke-Smith, N., & van Bueren, E. (2025). How ex ante policy evaluation supports circular city development: Amsterdam's mass timber construction policy. Journal of Environmental Management 376.
  • Andrade-Garda, J., Carneiro-Díaz, V., Lage-Etchart, D., Suárez-Garaboa, S. (2025). Automated argumentation-based social trust negotiation in collaborative networks. Computers and Industrial Engineering 203. https://doi.org/10.1016/j.cie.2025.111026
  • Andy, Y. W. C., Shiang, C. W., & Paschal, C. H. (2025). Agent-Oriented Modelling and Simulation for Robotic Based Predator Control. JOIV: International Journal on Informatics Visualization, 9(2), 607-616.
  • Angourakis, A., Baudouin, J. P., & Petrie, C. A. (2025). The Weather Model (Indus Village): Procedural Generation of Daily Weather for the Simulation of Small-Scale Socioecological Systems. Available at SSRN 5236256.
  • Araujo, A.S.D., Massucheto, J., Bugs, G.T., Saraiva, M.V.P., Duarte, F. (2025). Unveiling inequalities: The intersection of gender and income in accessibility in Curitiba, Brazil. Journal of Transport Geography 123.
  • Araújo, E. (2025). Uso da modelagem baseada em agentes no estudo de sistemas complexos. Revista Brasileira de Ensino de Física, 47.
  • Ardiansyah, R., & Pamungkas, K.A. (2025). Analysis Of HIV/AIDS Spread Patterns in Central Java Using Agent-Based Simulation. Digital Nexus Systematic Journal, 1(2). https://doi.org/10.26753/dns.v1i2.1553
  • Aros, S. K., & Frakes, D. (2025). Modeling Pedestrian Movement in a Crowd Context with Urgency Preemption. In E. Azar, A. Djanatliev, A. Harper, C. Kogler, V. Ramamohan, A. Anagnostou, and S. J. E. Taylor (Eds.), Proceedings of the 2025 Winter Simulation Conference, 1851-1861, doi: 10.1109/WSC68292.2025.11339043.
  • Assaad, L., Fuchs, R., Phillips, K., Schöppl, K., & Hahn, U. (2025). Capturing Argument in Agent-Based Models. Topoi, 1-19.
  • Assogba, G.G.C., Descheemaeker, K., Adam, M., Berre, D., Speelman, E., & Delay, E. (2025). Bridging Gaps in Agro-Ecosystem Integration: A Multi-Level Modelling Study in Semi-Arid Burkina Faso. SSRN.
  • Astobiza, A.M. (2025). Do AI agents trump human agency?. Discov Artif Intell 5(348). https://doi.org/10.1007/s44163-025-00608-y
  • Athamena, B., Athamena, M.R., Houhamdi, Z., Elrefae, G., Al Qeisi, K. (2025). Supplier selection strategies evaluation: a multi-agent based simulation. International Journal of Economics and Business Research, vol. 29 (14), p. 1-54. DOI: 10.1504/IJEBR.2025.10071838
  • Attiya, W. M., Shams, M.B., Alabbasi, A., & Albuflasa, H. (2025). Agent-based modeling of residential solar adoption in subsidized energy markets. Solar Energy 306.
  • Avila R., Hernandez S., (2025). "Strengthening Public Administration as a Basis for Corruption Control in Honduras: Correlation Analysis and Simulation Model". POSFACE, Universidad Nacional Autonoma de Honduras, UNAH, Honduras.
  • Ayouche, S., & Aligod, S. (2025). Agent-Based Modelling and Simulation of Islamic Microfinance: Evaluating Diminishing Partnership for Poverty Alleviation. Manchester Journal of Transnational Islamic Law & Practice, 21(4), 364-386.
  • Azarov, I. (2025). Reticular network as the lymph nodes railroad system: T cells migration modelling by the free energy minimization technique. Math-Net, (pp. 1- 28).
  • Azurin, C., Carpenter, L., Gorreck, G, Capaldi, A. (2025). Optimizing the Spatial Distribution of Fruiting Agave on Bat-Friendly Tequila Plantations. JMU Mathematics and Statistics REU.
  • Baccega, D., Pernice, S., Castagno, P., Sereno, M., & Beccuti, M. (2025). Living along covid-19: Assessing contention policies through agent-based models. Computational Intelligence Methods for Bioinformatics and Biostatistics, 143–161. https://doi.org/10.1007/978-3-031-90714-2_11
  • Baden-Böhm, F., Hellwig, N., Dauber, J., & Thiele, J. (2025). Efficiency of flower strips for bumblebee colonies depends on nesting habitat and interactions with semi-natural habitats and mass-flowering crops. Landscape Ecology, 40(7), 1-18.
  • Baek, A.J.S., & Martin, G. (2025). X–SYCON: Xylem–Inspired Passive Gradient Control for Communication–Free Swarm Response in Dynamic Disaster Environments. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2512.00018
  • Baer, M., Allhoff, K. T., Barnes, D. K., & Koch, F. (2025). Forecasting bryozoan assemblage dynamics under simulated climate change. bioRxiv, 2025-10.
  • Baghban Khiabani, S., Rahnama, M.R. & Ajza Shokouhi, M. (2025). Integrating GIS and NetLogo-based modelling for simulating land-cover change scenarios in Mashhad Metropolitan Area, Iran. Bulletin of Geography. Socio-economic Series, 68(68): 65-86.
  • Banerjee, S. (2025). Towards a taxonomy of life-like systems: An information theoretic view of life. In Workshop in Physics of Self-Organization in Complex Systems (Vol. 2025).
  • Bao, Y., & Huang, S. (2025). Agricultural Urbanism Planning Agents: Enhancing agricultural productivity in Manchester through agent-based land fragmentation management. Proceedings of the International Conference on Computer Aided Architectural Design Research in Asia.
  • Barker, T. (2025). Fusion Confusion: Sensors, Signals and Swarms. Symposia of the 2025 convention of the UK Society for the Study of Artificial Intelligence and the Simulation of Behavior (AISB), 31-33.
  • Barnard, L. N. Using agent based modelling to determine behavioural rules in host location by Cotesia parasitoids (in environments with competitors and nonhosts).
  • Baroni, P., Fellin, G., Giacomin, M., & Proietti, C. (2025). A NetLogo Tool for Exploring Value-Based Argumentation in Public Interest Communication. https://ceur-ws.org/Vol-4025/short5.pdf.
  • Becerra, A.C.P., Prada, C.F.R., Guerrero-Avendaño, A., & Rico-Bautista, D. (2025). Agent-Based Simulation of Tensile Testing of Metallic Materials: A Systematic Literature Review. Proceedings of 2025 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI). IEEE. 10.1109/CONIITI68560.2025.11366065
  • Bell, A. R., Rakotonarivo, O. S., Zhang, W., De Petris, C., Kipchumba, A., & Meinzen-Dick, R. S. (2025). Understanding pastoralist adaptations to drought via games and choice experiments: field testing among Borana communities. Ecology and Society, 30(1).
  • Beltrán-López, P., Pérez, M.G., & Nespoli, P. (2025). Cyber Deception: Taxonomy, State of the Art, Frameworks, Trends, and Open Challenges. IEEE Communications Surveys & Tutorials, 28, 1520-1556. 10.1109/COMST.2025.3594788
  • Benhamza, K., Abdelmoumène, H., Benselim, R., Seddiki, L. (2025). An Intelligent Hybrid Model for Wildfire Prediction and Environmental Risk Management. IEEE Access, 13, 211517-211532. doi: 10.1109/ACCESS.2025.3642224.
  • Berceanu, C., Bertolotti, F., Arshad, N., Patrascu, M. (2025). Understanding the mechanisms of infodemics: Equation-based vs. agent-based models. PLOS ONE 20(12): e0338614. https://doi.org/10.1371/journal.pone.0338614
  • Bernardin, A., & Perez-Acle, T. (2025). Assessing the impact of misinformation during the spread of infectious diseases. Scientific Reports, 15(1), 34740.
  • Betancur, J. E. H., & Parias, C. G. (2025). NetLogo as a methodological tool for the analysis of social systems: A systematic literature review. Multidisciplinary Reviews.
  • Bi, J., Han, G., & Yang, G. (2025). Intelligent Synergy in Urban Public Health and Safety Governance: A Multi-Agent Simulation Framework with Edge Computing and Optimization Algorithm. In Proceedings of the 2025 International Conference on Smart City and Sustainable Development, pp. 123 - 131.
  • Bolden, N. C. (2025). Staying Alive: Does Knowledge Integration or Team Creativity Drive Resiliency in Hierarchical Teams? (Doctoral dissertation, The Chicago School of Professional Psychology).
  • Boman, B. M., Dinh, T., Decker, K., Emerick, B., Modarai, S. R., Opdenaker, L. M., Fields, J. Z., Raymond, C., & Schleiniger, G. (2025). Dynamic Organization of Cells in Colonic Epithelium is Encoded by Five Biological Rules. Biology of the Cell 117(7).
  • Bonilla, C., Becerra, J., Cervantes, L., Caraveo, C., & Jimenez, S. (2025). Simulation of Drug Interactions in the Body: An Agent-Based Modeling Approach. Proceedings of the 2025 13th International Conference in Software Engineering Research and Innovation (CONISOFT), 131-138. IEEE. doi: 10.1109/CONISOFT66928.2025.00027.
  • Boot, J., van den Ende, M. W., Wiers, R. W., Lees, M. H., & van der Maas, H. L. (2025). Integrating dual-process decision making and social dynamics: A formal modeling framework for addiction. Psychological Review.
  • Borkowski, A. S., & Woźniewicz, M. (2025). Agent-Based Modeling of Fire Situation in NetLogo Using BIM Model. TechRxiv.
  • Bouchra, A., & Sarra, H.A. (2025). A Dissimilarity Measure for High-Dimensional Gene Expression Datasets: "Distance-DissimRatio" for Quantifying Transcriptomic Variation [Master's thesis, Université Frères Mentouri Constantine 1].
  • Bozhko, L.L., Zhukovskaya, I.E., Seitkhamzina, G.J. (2025). Environmental Policy of Central Asian Countries in the SDG Context: Regional Challenges and Prospects. WSEAS Transactions on Environment and Development 21. DOI: 10.37394/232015.2025.21.109.
  • Braccini, M., De Filippo, A., Lombardi, M., & Milano, M. (2025). Dance Choreography Driven by Swarm Intelligence in Extended Reality Scenarios: Perspectives and Implications. 2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR). 348-354.
  • Bradley, L.M., Duvall, E.S., Akinnifesi, O.J., Jaromerska, T.N., Junker, J.R., Wong, E. (2025). Exploring the multi-scale ecological consequences of stoichimetric imbalance using an agent-based modeling approach. Frontiers in Ecology and Evolution 13. https://doi.org/10.3389/fevo.2025.1505145
  • Breen, M. J., Kebede, A. S., & König, C. S. (2025). Agent-based socio-spatial modelling of coupled human-flood interactions along the UK coast. Environmental Research: Infrastructure and Sustainability.
  • Breitwieser, L. J. (2025). Design and analysis of an extreme-scale, high-performance, and modular agent-based simulation platform (Doctoral dissertation, ETH Zurich). arXiv. https://doi.org/10.48550/arXiv.2503.10796
  • Brophy, G., Rips-Goodwin, A., Wilson, L., & Lewis, A. (2025). Using Agent-Based Modeling to Understand the Impact of Community Interactions on Voter Apathy and Election Outcome. The PUMP Journal of Undergraduate Research 8, 286–321. https://doi.org/10.46787/pump.v8i.5455
  • Cabuya-Padilla, D., Díaz-López, D., Martínez-Páez, J., Hernández, L., & Castaneda-Marroquin, C. (2025). SERDUX-MARCIM: Maritime Cyberattack simulation using Dynamic Modeling, Compartmental Models in Epidemiology and Agent-based Modeling. International Journal of Information Security, 24(3), 1-48.
  • Calabrò, G., Le Pira, M., Inturri, G. (2025). Where and when does demand-responsive transport work best? A parametric analysis using agent-based modelling. EURO Journal on Transportation and Logistics 15. https://doi.org/10.1016/j.ejtl.2025.100173
  • Cálix Hernández, A. E. (2025). Éxito y Preferencia Omnicanal en Comercio Internacional: Un Modelado Basado en Agentes. Revista Científica Profundidad Construyendo Futuro, 24(24), 65–79. https://doi.org/10.22463/24221783.5353
  • Cálix-Hernández, A.E. (2025). Online omnichannel customer service interaction in international trade. Science, Economics and Business, 9, 5-24. https://doi.org/10.22206/ciene.2025.v9.3643
  • Callegari, E. (2025). Can Word Order in the Left Periphery Emerge from Local Interactions? A Complexity Inspired Simulation Framework. University of Iceland, https://www.elenacallegari.com/documents/JLM_Left_Periphery_ABM_EC.pdf.
  • Campbell, A. M., Kula, A. C., Jabaily, R. S., Oberle, B., Sidoti, B., Capaldi, A., & Bodine, E. N. (2025). Predicting potential recovery of the endangered bromeliad Tillandsia utriculata: An agent-based modeling approach. PLOS Computational Biology, 21(6), e1013157.
  • Cannavucciuolo, L., Maione, V., Ponsiglione, C., & Primario, S. (2025). Agent-based modelling of electric vehicle adoption: a multidimensional perspective assessment. Research in Transportation Business & Management 61.
  • Cantatore, E., Bernardini, G., Quagliarini, E., & Fatiguso, F. (2025). Evaluating and Mitigating Terrorist Risk in Historical Outdoor Open Areas Including User Factors. In: Albatici, R., Dalprà, M., Gatti, M.P., Maracchini, G., Torresin, S. (eds) Envisioning the Futures - Designing and Building for People and the Environment. Colloqui.AT.e 2025. Lecture Notes in Civil Engineering, vol 765. Springer, Cham.
  • Caravaggio, A., & Leoni, S. (2025). An Agent-Based approach to high-cost drugs for infectious diseases. Dipartimento di Scienze per l’Economia e l’Impresa 9(50127). https://www.disei.unifi.it/upload/sub/pubblicazioni/repec/pdf/wp20_2025.pdf
  • Cardoso, G. E. M. (2025). Aprendizaje de la derivada en estudiantes de bachillerato mediante tecnología arduino, Netlogo y Geogebra. [Master's Thesis], Benemérita Universidad Autónoma de Puebla.
  • Carnovale, S., DuHadway, S., Patrucco, A. S., & Yeniyurt, S. (2025). Balancing risk and resilience: how network structures and firm strategies mitigate supply chain disruptions. International Journal of Physical Distribution & Logistics Management.
  • Carreón Vázquez G., Hernández Zavaleta, J. E., Cortés Berrueco, L. E. & Carrión Velázquez, V. (2025). Modelación basada en agentes en el bachillerato: un estudio de las experiencias cotidianas y conceptos científicos. En A. Solares-Rojas, & A. P. Preciado Babb (Eds.), La investigación en modelización matemática: un diálogo entre educadores de Latinoamérica y España (pp. 205–229). Editorial SOMIDEM.
  • Castro Castro, V. (2025). Ictiodiversidad de Laguna Pampa El Cabildo, Chiapas, México. Una aproximación de análisis como un sistema complejo adaptativo.
  • Cela, H., Vogrin, M., Schmickl, T., & Wood, G. (2025). Emotional dynamics and engagement cycles in swiping dating apps: An agent-based modeling approach. Computers in Human Behavior Reports, 100775.
  • Čerepinko, D., Žuliček, M. (2025). The Simulation of Society Evolution Based on the Impact of Communication Messages. Communication Management Review 10(2), p. 62-75. DOI 10.2252/cmr202502111
  • Chan, A. (2025). A systems theoretic model of complex adaptation [Doctoral dissertation, McGill University]. eScholarship. https://escholarship.mcgill.ca/concern/theses/qj72pf02h
  • Chatter, S., Chokethaworn, K., & Chaiboonsri, C. (2025). Simulating the Impact of FDI Shocks on Macroeconomic Stability Using Agent-Based Modeling. International Journal of Science and Social Science Research 3(1).
  • Chaturvedi, V., & de Vries, W. T. (2025). Residential Location Preferences in a Post-Conflict Context: An Agent-Based Modeling Approach to Assess High-Demand Areas in Kabul New City, Afghanistan. Land 14(7), 1502.
  • Chen, F., & He, W. (2025). Agent-Based Models for Simulating Consumer-led Innovation: Pathways to Dual Upgrading in China's Economic Development Strategy. Computational Economics, 1-33. https://doi.org/10.1007/s10614-025-11050-8
  • Chen, M., Pan, Z., Alfonso, Y. N., Bell, S. O., & Bishai, D. (2025). The impact of investing in private clinics and pharmacies on modern contraceptive uptake: an agent-based model of a segmented market. BMC Health Services Research, 25, 1177.
  • Chen, S. (2025). Economic Simulations in Swarm: 20 Years On. New Mathematics and Natural Computation. https://doi.org/10.1142/S1793005726500572.
  • Chen, Y. (2025). Designing Constructionist Systems for Scaling and Scaffolding Open-Ended Human Learning (Publication No. 32284922) [Doctoral dissertation, Northwestern University]. ProQuest Dissertations & Theses.
  • Chen, Y., Wang, C., Shen, Y., & Zhang, Y. (2025). A simulation-based method for the optimal building evacuation plan: a study of emergency behavior among older adults. Engineering, Construction and Architectural Management, 1-18.
  • Cheng, Y. & Siljak, H. (2025). Emergent Multi-View Fidelity in Autonomous UAV Swarm Sport Injury Detection. arXiv. https://doi.org/10.48550/arXiv.2505.06588
  • Cheng, Y., & Šiljak, H. (2025). Strategies for decentralised UAV-based collisions monitoring in rugby. IEEE Internet of Things Journal, IEEE. 10.1109/JIOT.2026.3665829
  • Chi, H., Wang, L., Li, R., Tang, H., Xu, M., Cao, Z., & You, L. (2025). Research on UAV swarm effectiveness evaluation method based on deep learning. Proceedings of the 4th International Symposium on Aerospace Engineering and Systems (ISAES), 93-97, IEEE, 10.1109/ISAES66870.2025.11274275.
  • Chiang, C., & Calautit, J. (2025). Reducing Building Energy Performance Gap: Integrating Agent-Based Modelling and Building Performance Simulation. Buildings 15(10). https://doi.org/10.3390/buildings15101728
  • Chicacausa, N., Otalora-Low, M., Carrillo-Acosta, N., Arenas-Bautista, M. C., & Preziosi-Ribero, A. (2025). Agent-based models for integrated water resource management: quantifying land use changes by integrating economic and social incentives. Case study: Vista Hermosa (Meta). DYNA, 92(238), 57-65.
  • Chidley, B. (2025). A Framework Agnostic Method for Sensitivity Analysis and Parameter Specification on Agent-Based Disease Models (Doctoral dissertation).
  • Christ, S. I. (2025). Towards an understanding of wildfire risk at the wildland urban interface as a socio-ecological system. [PhD Thesis - University of Twente]. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/1.9789036568722
  • Cintra, L.M.F., Zech, P., Kassab, M., Lima, E.A., Costa Paiva, S.L., & Neto, V.V.G. (2025). M&SCheck: Towards a Checklist to Support Software Engineering Newcomers to the Modeling and Simulation Area. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2509.25625
  • Coca, J. R., Fernández-Vilas, E., Lopes, R. P., & Pereira, M. Ethodiversity Reduction in Elderly: a Preliminar Study with Agent-Based Social Simulation. EVOLUTION OF THE BIOSOCIAL WORLD, 73.
  • Cocho-Bermejo, A. (2025). Advancing Spatial Cognition Analysis: Integrating Agent-Based Modelling with Space Syntax in the NetLogo Framework. In B. Marques, C. Ruivo, D.L. Viana, & J.V. Vaz (Eds.), Formal Methods in Architecture: Proceedings of the 7th International Symposium on Formal Methods in Architecture (7FMA) (pp. 136-157). Springer Nature. https://doi.org/10.1007/978-3-032-02782-5_8
  • Cohen, M. & Colombo, M. (2025) Paths to Diversity: A Simulation of Conformity in Environments with Different Geometries. Ergo an Open Access Journal of Philosophy. 12(0). doi: https://doi.org/10.3998/ergo.8584
  • Collard, J.D., & Stattner, E. (2025). Deep Reinforcement Learning for Collaborative Parking Search Optimization. In: Nicosia, G., et al. [Eds], The 11th International Conference on Machine Learning, Optimization, and Data Science. Springer. https://doi.org/10.1007/978-3-032-21480-5_26
  • Contreras, R.T.C., Cervantes Huerta, L., Caraveo, C., Jiménez, S., & Torres, D.L.B. (2025). Design and Implementation of a Software-Based Simulation for Reliability Analysis in Renewable Microgrids. Proceedings of the 2025 13th International Conference in Software Engineering Research and Innovation (CONISOFT), 72-80. IEEE. doi: 10.1109/CONISOFT66928.2025.00020.
  • Correia, L. G., & Mena-Chalco, J. P. (2025). The Impact Factor Game: An Agent-Based Exploration of Self-Citation Influence and Interdisciplinary Dynamics on Impact Metrics.
  • Coto-Sarmiento, M., Namen, A., Varis, A., & Iovita, R. (2025). The Impact of Cooperation Under Climate Constraints: An Agent-Based Model for Exploring Paleolithic Behavioral Adaptations in the Inner Asian Mountain Corridor. Journal of Archaeological Method and Theory, 33(8).
  • Cozzi, F., Pangallo, M., Perotti, A., Panisson, A., & Monti, C. (2025). Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks. arXiv:2505.21426.
  • Crabtree, R., Conner, M., & Treves, A. (2025). Misleading biases in methods for estimating wolf abundance using spatial models. Academia Biology, 3(4). https://doi.org/10.20935/AcadBiol7924
  • Cruz-Martínez, G. A. (2025). Gobernanza y transformación digital en el sector salud. Innovare Revista De Ciencia Y tecnología, 14(1), 1–7. https://doi.org/10.69845/innovare.v14i1.480
  • da Silva, T. F., and Maia, J. (2025). Leadership Detection via Time-Lagged Correlation-Based Network Inference. Cornell University. arXiv:2507.04917. https://doi.org/10.48550/arXiv.2507.04917.
  • Dabholkar, S., Irgens, G. A., & Wilensky, U. (2025). Characterizing Integrated Learning of Disciplinary Core Ideas and Science Practices in a Computational Thinking (CT)–Integrated Biology Curriculum. Journal of Science Education and Technology, 1-16.
  • Dacey, K. (2025). Navigation and the environment: Understanding the interaction between human navigation behaviour and terrain through Agent-Based Modelling in the wilderness [Doctoral thesis, Charles Sturt University]. Charles Sturt University.
  • Daemen, J.-P., & Leoni, S. (2025). Simulating tertiary educational decision dynamics: An agent-based model for the Netherlands. Journal of Economic Interaction and Coordination.
  • Dagnino, A.M., Perez, L. (2025). Simulating Social-Ecological Outcomes of Urban Greening Initiatives: A Cellular Automata Case Study in Montreal. Proceedings of the 32nd International Cartographic Conference 10(54). https://doi.org/10.5194/ica-abs-10-54-2025
  • Dai, D., Zhang, R., Wang, Y., Long, R. (2025). Walkability evaluation and optimization based on pedestrian and environment interaction: an agent-based modeling approach. Journal of Transport and Health, vol. 44. doi: https://doi.org/10.1016/j.jth.2025.102107.
  • Dal Pont, T., Sartor, G., Wyner, A., & Sartor, G. (2025). You Take the High Road, and I'll Take the Low Road: Large Language Models Logical English and the Highway Code. In J. Maranhão (Ed.), Proceedings of the Twentieth International Conference on Artificial Intelligence and Law. https://doi.org/10.1145/3769126.3769254
  • Dawood, M. M., Olukanmi, S. O. (2025). A Comparative Study of Human-Inspired Communication Strategies in Multi-Agent Environments. In Proceedings of the 2025 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, 5, 1-8.
  • Dawson, D., Minucci, J., Purucker, T. (2025). Incorporating a nectar-based pesticide module into BeeHave, an agent-based model of honeybee population dynamics. Ecological Solutions and Evidence, vol. 6 (3), e70105. https://doi.org/10.1002/2688-8319.70105
  • de Oliveira Leite Nascimento, C., Pira, M. L., Fazio, M., Marcucci, E., Gatta, V., & Pluchino, A. (2025). Customers in action: Involving Crowdshippers in e-grocery deliveries. Transportation Research Part A: Policy and Practice, 199, 104582.
  • de Tarlé, L. D., Michelini, M., Šešelja, D., & Straßer, C. (2025). Argumentative Agent-Based Models.
  • DelaPaz-Ruíz, N., Augustijn, P.W.M., Farnaghi, M., Abdulkareem, S.A., Zurita-Milla, R. (2025). Integrating agent-based disease, mobility and wastewater models for the study of the spread of communicable diseases. Geospatial Health 20(1).
  • Delay, E., Broutin, L., Fallot, A., Perrotton, A., & Masse, D. (2025). From Local Actors to Leaf Carers: Companion Modeling for Rethinking Tree Protection in Senegal's Groundnut Basin. Journal of Artificial Societies and Social Simulation 28(4) 6.
  • Desai, A. (2025). Precision Elimination: Proof-of-Concept In Silico Testing of a Novel Construct for Optimizing HIV-1 Eradication. doi: 10.20944/preprints202503.0071.v2
  • Dijvejin, F. G. (2025). Analysis of Economic Systems Using Complex Systems Simulation Models. Business, Marketing, and Finance Open, 1-13.
  • Divasson-J, A., Macarulla, A. M., Garcia, J. I., & Borges, C. E. (2025). Agent-based modeling in urban human mobility: A systematic review. Cities, 158, 105697.
  • Dixon, P., Sieprawski, K., Tang, A., & Lira, M. (2025). Making drawings count for assessing quantitative knowledge. In Seitamaa-Hakkarainen, P. & Kangas, K. (Eds.), Proceedings of the International Conference of the Learning Sciences, 2901–2903.
  • Do, D.T., Tran, V.T. (2025). NetLogo for transport and logistics: agent-based modeling of flows, control, and operations. Acta Logistica - International Scientific Journal about Logistics 12(4), p. 643-655. https://doi.org/10.22306/al.v12i4.699
  • Do, K. B., Nguyen, N. A., Pham, N. S., Tran, N. D., & Huynh, Q. L. (2025). Modeling the Physiological Response of Skeletal Muscle to Various Physical Activities. In International Conference on the Development of Biomedical Engineering in Vietnam (pp. 908-918). Springer, Cham.
  • Do, T. D., & Bui, H. P. (2025). Modeling Lane-Changing Behavior on Two-Lane Roadways Using a NetLogo-Based Simulation Framework. Journal of Resilient Urbanism & Sustainable Design, 1(1). https://doi.org/10.64822/jrusd.v1i1.19
  • Do, T.D., & Huy, B.P. (2025). Evaluating Discretionary Lane-Changing under Cogested Conditions: An Agent-Based Cellular Automata Model in NetLogo. The Journal of Resilient Urbanism and Sustainable Design, 1(1). https://doi.org/10.64822/jrusd.v1i1.19
  • Dorchies, D., Bonté, B., Promduangsri, P., & Sil, D. (2025). Fostering integrated water resource management coupling airGRiwrm hydrological model and agent-based modeling (NetLogo) (No. EGU25-18255). Copernicus Meetings.
  • Dragomir, O. E., & Dragomir, F. (2025). Multi-Agent-Based Estimation and Control of Energy Consumption in Residential Buildings. Processes 2025, 13, 2261.
  • Drennan, J. L., & Tumulty, J. P. (2025). Frogs With a Southern Drawl: Wide Recognition Space Facilitates Heterospecific Aggression in Territorial Cricket Frogs. Ethology 2025 0:1-13. https://doi.org/10.1111/eth.70038
  • Dubey, P., Dubey, P., Zakariah, M., Almazyad, A. S. & Alsekait, D. M. (2025). TRANSHEALTH: A Transformer-BDI Hybrid Framework for Real Psychological Distress Detection in Ambient Healthcare. Computers, Materials, and Continua 85(2). DOI:10.32604/cmc.2025.066882
  • Dukker, R. (2025). The Influence of Socio-Technical Factors on Evacuation Performance of University Buildings [Master Thesis, Delft University of Technology]. TUDelft. https://repository.tudelft.nl/.
  • Dwivedi, S., da Silva Torres, R., Hameed, I. A., Tufte, G., & Karlsen, A.S.T. (2025). Koopman-based data-driven soft artificial life: obtaining rulesets from observed data. Natural Computing 24, 799-812.
  • Dwyer, M. (2025). Waabizheshiwag (Martens americana) in the Heart of Anishinaabe-Aki. Purdue University, Thesis, https://www.proquest.com/openview/9d9994ecaa0df4521bf605bbe217f0fa/1?pq-origsite=gscholar&cbl=18750&diss=y.
  • Dzokom, Alexis. (2025). Socio-economic Adaptation Strategies in the Lake Chad Area. hal-04990490
  • Dzreke, S. S. Sovereignty arbitrage 5.3 x ROI: Apple’s $900 M strategic pivot and the geopolitical resilience matrix (GRM). Engineering Science & Technology Journal, 6(8), 450-467. https://doi.org/10.51594/estj.v6i8.2036
  • Echeverry-Galvis, M.A., Meza, P.A., Peláez, M.E., Otalora-Löw, M., & Gonzalez, M. (2025). Moving in the concrete maze: Functional connectivity by agent-based modelling in a diverse, rich city. Available at SSRN: https://ssrn.com/abstract=5476334
  • Edalati, O., Salehnia, N., seyed Ramezani, F. (2025). Agent-Based Modeling in the Design and Management of Circular Marine Tourism Economy. 2nd International Conference on Blue Economy, University of Hormozgan.
  • Edi Yoga, K. (2025). Tree Island Enhanced and Monoculture Oil Palm Plantation Landscape Evolution Modelling (TIEMOPLEM) Catchment-scale soil and water redistribution effects of tree islands enrichments. Thesis. Wageningen University and Research. https://edepot.wur.nl/697091
  • Ehsanitabar, A., Hassanzadeh, Y., Aalami, M., & Sadeghfam, S. (2025). Agent-based modeling for demand management of reservoirs considering social and hydrological interactions under uncertainty. Journal of Environmental Management 380.
  • Ekoh, L.A., Eneh, C.A., & Onyebuchi, G.C. (2025). Breaking the Lineage: Modelling the Transmutation of Economic Trauma Through Generational Moral Disruption and Spiritual Realignment. Unizik Journal of Education Graduates 10.
  • Emerenini, B., Beck, E., Cantrel, K., & Kurtay, V. (2025). The effect of environmental factors in biofilm and phage interactions in agent based model. arXiv. https://doi.org/10.48550/arXiv.2502.18507
  • Emerenini, B., Beck, E., Cantrel, K., & Kurtay, V. (2025). The effect of environmental factors in biofilm and phage interactions in Agent Based Model. arXiv. https://doi.org/10.48550/arXiv.2502.18507
  • Espinoza, K., Mas, E., & Koshimura, S. (2025). Agent-based simulation of tsunami evacuation using public buses in Puntarenas, Costa Rica [Master's thesis, International Institute of Seismology and Earthquake Engineering]. IISEE. https://iisee.kenken.go.jp/syndb/data/MEE24712_Kristel_Synopsis-tdb.pdf
  • Essakkat, K., Wu, L., Atallah, S.S., & Khanna, M. (2025, July 27-29). Spatial-Dynamic Adoption of AI Weeding Robots: Insights from A Choice Experiment and an Agent-Based Model [paper presentation]. 2025 AAEA & WAEA Joint Annual Meeting in Denver, CO.
  • Etemadidavan, S., & Collins, A. J. (2025). Agent-based model for multiple team membership: implications for system design. International Journal of Organization Theory & Behavior.
  • Fabris, B. (2025). Evaluating IoT-based Landslide Early Warning Systems. [Master's Thesis, Malmö University]. DIVA Portal: 2002608
  • Fabris, B., Tucker, J., & Lorig, F. (2025). Using Agent-Based Social Simulations to Inform Organ Donation Policymaking: Adopting the Spanish Approach in Sweden. In Vidler, A. & Swarup, S. (Eds), Proceedings of The 26th International Workshop on Multi-Agent-Based Simulation.
  • Fakhari, Z., & Ahmadi, A. (2025). Integrating the agent-based approach with hydraulic analysis of the water distribution network to a realistic micro-scale simulation of end-users in GAZ City, Iran. Journal of Hydrology: Regional Studies, 62, 102805.
  • Fauvelle, M., Grier, C., García-Piquer, A. (2025). The Archaeology of Seafaring in Small-Scale Societies: Negotiating Watery Worlds. University Press of Florida. https://doi.org/10.5744/9780813079493
  • Federico, F., Balakrishna, R., & Janssen, M. A. (2025). Evolution of conditional cooperation in a spatial public goods game. PLOS Complex Systems. https://doi.org/10.1371/journal.pcsy.0000040
  • Feliciani, T. (2025). Divided Spaces and Divided Opinions: Modeling the Impact of Residential Segregation on Opinion Polarization.
  • Feng, W., Yang, L.E., Ai, M., Chen, S., Wang, Z., Wu, W., Chen, J., Fang, Y., Xu, Y., & Garschagen, M. (2025). FRAMe: Empirically informed agent-based modeling of flood resilience in the Mekong River Basin. MethodsX, 15(103682). https://doi.org/10.1016/j.mex.2025.103682
  • Fernandez, M. D., & Castañeda, C. Y. M. (2025). Simulación multiagente para analizar el comportamiento exportador de las PYMES: un modelo basado en NetLogo. Región Científica, 4(2), 14.
  • Fernando, W. W. A. D. R., Ariyarathne, D. M. T. W., Dilshan, B. A. T., Wijerama, N. S., Lahiru, U. P., & Rajapakse, R. A. C. P. (2025). Agent-Based Modeling of Human-Elephant Conflict Dynamics: A Simulation Approach Using NetLogo. 5th International Conference on Advanced Research in Computing (ICARC), pp. 1-6, doi: 10.1109/ICARC64760.2025.10962930.
  • Ferraciolli, E., Renzini, F., Araújo, T., Squazzoni, F. 2025. The Devil’s Dung? Money as a mechanism of generalized reciprocity in human societies. https://doi.org/10.48550/arXiv.2506.20691.
  • Flinspach, L., Wiegand, T., Camarero, J. J., Batllori, E., Gutiérrez, E., & Bader, M. Y. (2025). Quantification of small‐scale spatial patterns in Alpine–Treeline ecotones. Ecology and Evolution, 15(5). https://doi.org/10.1002/ece3.71186
  • Fogo, G.M., Torres, F.J.T., Speas, R.L., Anzell, A.R., & Sanderson, T.H. (2025). Agent-based modeling of neuronal mitochondrial dynamics using intrinsic variables of individual mitochondria. iScience, 28(5). 10.1016/j.isci.2025.112390
  • Fonseca, L. L., Böttcher, L., Mehrad, B., Laubenbacher, R. C. (2025). Optimal control of agent-based models via surrogate modeling. PLoS Computational Biology 21(1). https://doi.org/10.1371/journal.pcbi.1012138
  • Foxfoot, I. R., Cushway, K. C., Schwalb, A. N., Smith, D. R., Swannack, T. M. (2025). Evaluating freshwater mussel sampling methodologies using a simulation model. Ecological Indicators 179, https://doi.org/10.1016/j.ecolind.2025.114172.
  • Fuhrmann, T., Rabinowitz, G., Peel, A., Jacob, S., Burke, Q., Krakowski, A., ... & Lee, I. (2025). Computational Thinking Without Writing Code: What’s Next for Computational Modeling?. In Proceedings of the 19th International Conference of the Learning Sciences-ICLS 2025, pp. 2417-2425. International Society of the Learning Sciences.
  • Fukuda, M., Wang, K., Panther, S., Wong, N., Dudder, I., & Shao, Q. (2025). An Analysis of Three C/C++ Cluster-Computing Libraries for Agent-Based Models. ACM Transactions on Modeling and Computer Simulation.
  • Galeano, R. E. G. R. E., Wong, A., Rexachs, D., Suppi, R., Bruballa, E., Epelde, F., & Luque, E. (2025). Impact of the population pyramid on the ED. Journal of Computer Science and Technology, 25(1), e05-e05.
  • Gallagher, C.A., Radchuk, V., Dammhahn, M., & Jeltsch, F. (2025). Pattern-Informed Energetics: Inferring energy allocation and the emergence of life‑history strategies. EcoEvoRxiv. 10.32942/X20W6V
  • Gao, S., Zhu, B., Zhang, X. (2025). Optimizing Emergency Response by Digital Spontaneous Volunteers: Insight From Agent-Based Modeling Analysis. Risk Analysis, 45(12), 4077-5086. https://doi.org/10.1111/risa.70158
  • Gao, Y., Zhuang, Y., Wang, J., & Li, M. (2025, January). Research and implementation of a simulation training system for grid control strategies with integration of distributed new energy sources and energy storage devices. In International Conference on Mechatronics and Intelligent Control (ICMIC 2024) (Vol. 13447, pp. 326-332). SPIE.
  • García-Piquer, A. (2025). In A. García-Piquer, M. Fauvelle, & C. Grier (Eds.), Navigating Paradigms: Seafaring, Settlement Patterns, and Social Interaction in Southernmost South America (pp. 17-42). University of Florida Press.
  • García, M.G.C. (2025). Strategic Accounting Decisions and Business Cooperation in Nearshoring: A Simulation Approach. Realpolitik, 1(1).
  • Garty, B., Gambash, G., Levy, S. T., Bar-Oz, G. (2025). Wine economy in Byzantine Shivta (Negev, Israel): Exploring the role of runoff agriculture and droughts through Agent-Based Modeling. PLoS One 20(7): e0325204. https://doi.org/10.1371/journal.pone.0325204.
  • Gauvin, L., & Randon-Furling, J. (2025). Segregation in cities. Compendium of Urban Complexity, 137.
  • Gavric, S. (2025). Unified Urban Surface and Air Mobility Traffic Management: Concept of Operations and Microsimulation Modeling (Publication No. 31840901)[Doctoral dissertation, University of Pittsburgh]. University of Pittsburgh ProQuest Dissertations & Theses. https://d-scholarship.pitt.edu/concern/etds/f34cd88f-0fc1-4ed5-82ac-92c750766326
  • Gavric, S., Stevanovic, A., Mitrovic, N., Netjasov, F., & Rakas, J. (2025). Urban surface and Air Mobility Control: A microsimulation integrating intelligent fly-drive vehicles into surface traffic. IEEE Transactions on Intelligent Transportation Systems, 26(7), 9893–9906.
  • Gerada, A., Rosato, C., Howard, A., Green, P.L., Hope, W. (2025). Gastro-intestinal transmission and colonisation of pathogenic bacteria: calibration of an agent-based model. International Journal of Infectious Diseases 152, 68, https://doi.org/10.1016/j.ijid.2024.107600
  • Gerrits, B. & Mes, M. (2025). Self-Organization in Crowd-Sourced Food Delivery Systems. In E. Azar, A. Djanatliev, A. Harper, C. Kogler, V. Ramamohan, A. Anagnostou, and S. J. E. Taylor (Eds.), Proceedings of the 2025 Winter Simulation Conference, 175-187, doi: 10.1109/WSC68292.2025.11339016.
  • Ghaitaranpour, A., Mohebbi, M., & Koocheki, A. (2025). An innovative agent-based technique for determination of tortuosity in porous materials - Case study: bread and bread dough. Current Research in Food Science 10. https://doi.org/10.1016/j.crfs.2025.100995
  • Gharamaleki, M. M., & Elahi, N. Investigating the effect of tax on money on the macroeconomic variables in a closed economy using the agent-based general equilibrium model.
  • Gheibi A., & Rassafi A. A. (2025). Designing a Decentralized Reverse Logistics System for Waste Management. LogForum, 21(3), 347-359. https://doi.org/10.17270/J.LOG.001229
  • Giabbanelli, P. J., Daumas, C., Flandre, N. Y., Pitkar, A., & Vazquez-Estrada, J. (2025). Promoting empathy in decision-making by turning agent-based models into stories using large-language models. Journal of Simulation, 1-21.
  • Gil de Zúñiga, H., Wang, R. Y., & Cheng, Z. (2025). Selective Exposure to News, Homogeneous Political Discussion Networks, and Affective Political Polarization: An Agent-Based Modeling of Minimal versus Strong Communication Effects. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393251387282
  • Gok, S., Levy, S. T., Wilensky, U., & Dawud, T. (2025). Dynamic Equilibrium in Science Education: Cross-Disciplinary Perspectives. In P. Seitamaa-Hakkaraine & K. Kangas (Eds.), Proceedings of 19th International Conference of the Learning Sciences (ICLS) 2025.
  • Gorvett, Rick. (2025). Agent-Based Modeling: Introduction and Actuarial Applications. CAS E-Forum Quarter 1.
  • Govind, G., Lange, M., Grimm, V., Frank, K., & Groeneveld, J. (2025). A machine learning-derived metamodel of BEEHAVE predicts how honey yield depends on weather and region across Germany. Ecological Modelling, 512(111422). https://doi.org/10.1016/j.ecolmodel.2025.111422
  • Grando, L. (2025). Study for Optimization of Battery Consumption for Unmanned Aerial Vehicles. Thesis. Universidade Estadual de Campinas (UNICAMP). https://doi.org/10.47749/T/UNICAMP.2025.1499477
  • Grando, L., Jaramillo, J. F. G., Leite, J. R. E., Ursini, E. L. (2025). Agent-based simulation of UAV battery recharging for IoT applications: precision agriculture, disaster recovery, and dengue vector control. arXiv:2503.12685
  • Grando, L., Leite, J. R. E., Ursini, E. L. (2025). Agent-based simulation for drone charging in an internet of things environment system. arXiv:2509.10867
  • Granger, J., Nevitt, G., & Johnson, S. (2025). Multispecies Sensory Networks and Social Foraging Strategies: Implications for Population Decline in Procellariiform Seabirds. Proceedings of the National Academy of Sciences, 122(41).
  • Green, M., Sarkani, S., & Mazzuchi, T. (2025). Mitigating Cybersecurity Risks in Grids With a High Penetration of Distributed Renewables. IET Cyber-Physical Systems: Theory & Applications 10(1). https://doi.org/10.1049/cps2.70026
  • Grgičević, L., Coates, E. M., Fossen, T. I., Bye, R. T., & Osen, O. L. (2025). Centralised Decision Support in Maritime Vessel Traffic Services: A Polymatrix Game Solution. IEEE Access 13. https://doi.org/10.1109/ACCESS.2025.3564718
  • Grigoryan, G., Soylu, M.Y., Lee, J., Sembrat, E., & Harmon, S. (2025). Digital Twin of Self-Regulated Learners: Effects of Metacognition, Feedback, and Task Complexity. In H. Wang & J.D. Texier (Eds.), 2025 IEEE Digital Education and MOOCS Conference (DEMOcon), Boca Raton, FL, pp. 1-6.
  • Grimm, V., Berger, U., Calabrese, J. M., Cortés-Avizanda, A., Ferrer, J., Franz, M., ... & Railsback, S. F. (2025). Using the ODD protocol and NetLogo to replicate agent-based models. Ecological Modelling, 501, 110967.
  • Gritter, K., Pybus, M., Lewis, M. A., & Merrill, E. (2025). Artificial Attractants: Implications for Disease Management in Deer. Ecology and Evolution 15(2). https://doi.org/10.1002/ece3.71013
  • Grizioti, M., & Kynigos, C. (2025, June). Extending Constructionist Media With Emerging Digital Technologies for Critical Computational Thinking: Emerging Technologies for Critical CT. In Constructionism Conference Proceedings (Vol. 8, pp. 123-136).
  • Groot, M., Quist, J., Leising, E., Kort, M., & Warnier, M. (2025). Extended Producer Responsibility to Reduce Plastic Waste Pollution: An Agent-Based Modelling Study to Analyze EPR Instruments in Indonesia. Proceedings of the Social Simulation Conference 2025. TU Delft.
  • Gschiel, D.J. (2025). Multi Agent Reinforcement Learning: Single Agent Strategies in Multi Agent Setups [Master's thesis, FH Joanneum]. https://epub.fh-joanneum.at/obvfhjhs/content/titleinfo/13363916/full.pdf
  • Gumahad B., & Collins, AJ. (2025). Simulating emergent behavior of autonomous swarm systems using agent-based modeling. SIMULATION 0(0). doi:10.1177/00375497251349538
  • Gumahad, A. T. (2025). Comparative Analysis of Emergent Behaviors of Three Drone Swarm System Models for Targeting Using Agent Based Modeling and Simulation. [Doctoral Dissertation, Old Dominion University]. Available from ProQuest Dissertations & Theses Global.
  • Gumeniuk, L., Markina, L., Satsyk, V., Humeniuk, P., & Lashch, A. (2025). Application of multi-agent programming for modeling the viscosity state of mash in alcohol production. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 15(1), 27-32.
  • Günther, A., van der Kam, M., Rasanan, A. H., Gluth, S., & Hahnel, U. (2025). Divergent effects of economic and behavioural policy coupling on electric vehicle adoption at individual and system levels. Research Square. https://doi.org/10.21203/rs.3.rs-8135836/v1
  • Guo, X., & Hu, W. (2025). Simulation study on the impact of emergency command on earthquake emergency rescue based on agent-based modeling. Journal of Tsinghua University 65(6).
  • Gurkan, C., Jwalapuram, N. K., Wang, K., Danda, R., Chen, J., Rasmussen, L., & Wilensky, U. (2025). LEAR: LLM-Driven Evolution of Agent-Based Rules. In Ochoa, G. (Ed), Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2309-2326.
  • Güz, Ş., & Murakami, S. (2025). A systematic literature review of simulation models for flows, markets, and sustainability of critical energy transition minerals. Resources, Conservation & Recycling Advances, 200271.f
  • Habtemariam, D. Y., Kim, Y., Kim, M., & Lee, J. (2025). WSim4ABM: Agent-based Modelling simulation Web service with Message-broker middleware and Annotation processing library. SoftwareX, 30, 102173.
  • Hadfield, R.K., Jordan, S., Polhemus, D.A., Sutherland, L.N., Peck, S.L., Abbott, J.C., Frandsen, P.B., Guralnik, R.P., Kalkman, V.J., Ware, J.L., & Bybee, S.M. (2025). Diversification and evolution of Hawaiian Megalagrion damselflies (Pinapinao, Odonata: Coenagrionidae). Systematic Entomology, 51(1). https://doi.org/10.1111/syen.70015
  • Hager, T., Mellacher, P., & Rath, M. (2025). Gender norms and network structure: A model of the intrahousehold division of Labor. Feminist Economics, 31(3), 237–274.
  • Hajdu, F., Kuti, R., & Hajdu, C. (2025). Cognitive aspects of agent-based based evacuation simulation. Proceedings of the 2025 IEEE 16th International Conference on Cognitive Infocommunications (CogInfoCom). IEEE. 10.1109/CogInfoCom66819.2025.11200742
  • Hajian-Forooshani, Z., Rivera-Salinas, I. S., Perfecto, I., & Vandermeer, J. (2025). The population dynamics of clustered consumer–resource spatial patterns: Insights from the demographics of a Turing mechanism. Proceedings of the National Academy of Sciences, 122(3), e2407991121.
  • Hamed, S., Tabesh, M. (2025). A socio-hydrological agent-based model of farmer adaptation to groundwater salinity stress in an arid region of Iran. SSRN. http://dx.doi.org/10.2139/ssrn.5944155
  • Hampwaye, N., Wang, J., Revell, A., Manchester, E., Aldersley, T., Zuhlke, L., ... & Ngoepe, M. (2025). Growth in a two-dimensional model of coarctation of the aorta: A CFD-informed agent based model. Journal of Biomechanics, 112514.
  • Hart, P. (2025). Can Power Be Given to Fishers to Manage Their Own Fishery? A Modelling Study Incorporating the Concept of Social Capital as a Force for Good Management. A Modelling Study Incorporating the Concept of Social Capital as a Force for Good Management.
  • Hayman, G., & Pang, R.K.K. (2025). Reframing Financial Markets as Complex Systems: Tools for Systemic Risk Analysis, Portfolio Management, and System-Level Investing. CFA Institute.
  • He, Q., & Sumiyoshi, D. (2025). Simulating air-conditioning use in Office Buildings by Modeling the Relationship between Psychological Factors and Occupants' Behavior in Japan. In T. Widström (Ed.), Proceedings of The 17th ROOMVENT Conference. E3S Web of Conferences 672.
  • Henzler, J., Beyer, N., Hanß, S., Fiedler, S., Simpkins, C. D., & Wiegand, K. (2025). Coupling agent-based and InVEST models for spatio-temporal simulation of land-use change effects on ecosystem services. Available at SSRN 5856141.
  • Heredia Rengifo, M. G., & Hernández Díaz-Ambrona, C. G. Modelo dinámico-probabilístico sobre el comportamiento de los pueblos indígenas en aislamiento de la Amazonia Ecuatoriana.
  • Hernandez, W.V., Quidayan, J.E.C., & Selarde, J.D.M. (2025). Simulating predator-prey dynamics between clown knifefish (Chitala ornata) and ayungin (Leiopotherapan plumbeus) using agent-based modeling. Capstones. 163. https://www.ukdr.uplb.edu.ph/etd-capstone/163
  • Herzog, R. H., Degkwitz, T., & Verma, T. (2025). The Urban Model Platform: A Public Backbone for Modeling and Simulation in Urban Digital Twins. arXiv preprint arXiv:2506.10964.
  • Hirst, J, Singarayer, J, Lombardo, U, & Mayle, F. (2025). Modelling Maize Agriculture by the Pre-Columbian Casarabe Culture of Amazonian Bolivia: An Agent-based Approach. Journal of Artificial Societies and Social Simulation 28(4) 5.
  • Hirst, J., Singarayer, J. S., Lombardo, U., & Mayle, F. (2025). Constraining the population size estimates of the pre-Columbian Casarabe Culture of Amazonian Bolivia. PLoS One 20(5): e0325104.
  • Hou, B., Miao, H., Wang, Q., & Xu, C. (2025). Comparison of crowd evacuation efficiency under static signage and dynamic personnel guidance from an emotional contagion perspective. Physica A: Statistical Mechanics and its Applications, vol. 678 (130977). ISSN 0378-4371, https://doi.org/10.1016/j.physa.2025.130977.
  • Hricisak, E. (2025). Modeling and simulating the prisoner's dilemma in the workplace. [Master's Thesis, Masaryk University].
  • Huang, B., Huang, Q., Yang, L., Chen, Z. (2025). Spatial optimization of fragmented farmland based on clustering algorithm and multi-agent system. Journal of Agriculture Resources and Environment 42(5). DOI:10.13254/j.jare.2024.0438
  • HUANG, X., Yilan, B. U., Junbei, L. I. U., Meng, M. E. N. G., ZHANG, J., & ZHUGE, C. (2025). A spatial agent-based approach to simulating the ride-hailing system and its environmental impacts. Transport Policy, 103848.
  • Huo, F. Y., Restrepo, D. J., Manrique, P. D., Woo, G., & Johnson, N. F. (2025). Physics reveals and explains patterns in conflict casualties. Europhysics Letters 151. DOI 10.1209/0295-5075/ade8a1.
  • Hwang, Y. S. (2025). Feedback Loops of Influence: An Agent-Based Simulation of Media-Voter Dynamics and Political Polarization (Doctoral dissertation, The Claremont Graduate University).
  • Hyunwoo, K. (2025). Production and Distribution of Unified Silla Pottery through Agent-Based Modeling A Hypothetical Approach to Producer and Consumer Choice Mechanisms. Journal of Humanities, vol. 82 (2). DOI 10.17326/jhsnu.82.2.202505.187
  • Iancu, L. D., Drăgoi, P.-A., Sandu, A., & Cotfas, L.-A. (2025). Fire-induced evacuation in concert venues: An agent-based simulation approach using NetLogo. Proceedings of the International Conference on Business Excellence, 19(1), 599–610.
  • Iapăscurtă, V. (2025). Exploring the human circulatory system through system dynamics: a model-based approach. In: Journal of Engineering Science, 2024, 31(4), pp. 95-104. ISSN 2587-3474. DOI: https://doi.org/10.52326/jes.utm.2024.31(4).07
  • Iapăscurtă, V., Falenciuc, R., Munteanu, V., Arnaut, O. (2025). Advancing Biomedical Engineering: An Agent-Based Approach to Pulmonary Edema Simulation. In: Sontea, V., Tiginyanu, I., Railean, S. (eds) 7th International Conference on Nanotechnologies and Biomedical Engineering. ICNBME 2025. IFMBE Proceedings, vol 135. Springer, Cham. https://doi.org/10.1007/978-3-032-06497-4_7
  • Iapăscurtă, V., Munteanu, V., Belîi, A. (2025). Exploring Maternal-Placental-Fetal Interactions: A Hybrid Modeling Approach for Biomedical Engineering. In: Sontea, V., Tiginyanu, I., Railean, S. (eds) 7th International Conference on Nanotechnologies and Biomedical Engineering. ICNBME 2025. IFMBE Proceedings, vol 135. Springer, Cham. https://doi.org/10.1007/978-3-032-06497-4_30
  • Iapăscurtă, Victor. (2025). Agent-based modeling of fluid dynamics in lung tissue engineering. Cells and tissues transplantation Actualities and perspectives, Ed. 3, 21-22
  • Iler, S., & Clark, W. (2025). NetLogo: Exploring Stocks and Flows for Climate Change: A Netlogo Model. Sustainable Development, Sustainability Science. https://sustainabilityscience.org/course/course-library/learning-games/netlogo-stocks-flows.pdf
  • Imtiyaz, I., Parvaz, M., & Kolankeh, A.K. (2025). SwarmForm: A Survey of Shape Formation in Modular and Decentralized Robot Systems. 10.1109/ICAMAC67779.2025.11398586
  • Innocenti, E., Prunetti, D., Delhom, M., Idda, C. (2025). A Modular Approach for ABM/LMM Models: Specification of Reusable Building Blocks Centred on the Economic Concepts of WTA and WTP. International Journal on Advances in Software 18. hal-05161281
  • Innocenti, F., & Rozzi, R. (2025). An Evolutionary Analysis of Narrative Selection. arXiv preprint arXiv:2508.03540.
  • Ionescu, Ş., Tătaru, G., Dumitrecu, G., & Stanică, N. B. (2025, May). Modeling Financial Contagion: Exploring Interconnectedness and Systemic Risk Through Agent-Based. In Proceedings of 23rd International Conference on Informatics in Economy (IE 2024): Recent Results in Education, Research, and Applications (Vol. 426, p. 263). Springer Nature.
  • Irving-Beck, Fiona. (2025). Weathering and Beyond: Leveraging Mathematical Modeling to Simulate Erosion in Digital Media. Scripps Senior Theses. 2617.
  • Isnan, S.S.M., Abdullah, A.F., Sharif, A.R.M., Ishak, I., Ismail, S.N.S., Tai, D., Appanan, M.R. (2025). Leveraging Computational Model Approach in Understanding Infectious Disease: A Case Study in Sabah, Malaysia. Pertankia Journals Science & Technology 33(2), 653-676, https://doi.org/10.47836/pjst.33.2.06
  • Ivanova, Y. A. (2025). Aviation Safety Simulation Study on the Impact of Turbulence on Unmanned Aircraft Systems Management. 2025 International Conference on Information Technologies (InfoTech), Sofia, Bulgaria, pp. 1-4.
  • Ivanova, Y.A. (2025). Developing a Simulation-Based Methodology for Risk Assessment in Unmanned Aircraft Systems. Proceedings of the 2025 International Conference on Information Technologies (InfoTech), pp. 1-4. 10.1109/InfoTech67177.2025.11175952.
  • Jacauna, R.D.P., Coleho, D.P.J., Menezes, C.S. (2025). Inclusive Pedagogical Architecture in Action: Teaching Complex Systems to Visually. Inclusiva em Ação: Ensinando Sistemas Complexos a Pessoas com Deficiência Visual. EaD em Foco 15(1), e2513. doi: https://doi.org/10.18264/eadf.v15i1.2513
  • Jacknis, N., Maddala, K. 2025. Forecasting the Adoption of New Technology Products Combining Machine Learning with Multi-Agent Simulations. Available at SSRN.
  • Jager, W., Antosz, P., Bouman, L., Li, T., Polhill, G., Szczepanska, T., & Wang, S. (2025). HUMAT: An Integrated Framework for Modelling Individual Motivations, Social Exchange and Network Dynamics. Journal of Artificial Societies and Social Simulation 28(1) 4. DOI: 10.18564/jasss.5611
  • Jahic, E., & Larsson, L. (2025). Multiagent Modelling of Energy Consumption in the Household. [Independent thesis, Malmö University]. Digitala Vetenskapliga Arkivet. https://www.diva-portal.org/smash/get/diva2:1997961/FULLTEXT02.pdf.
  • Jaiswal, A., & Karabiyik, T. (2025), Emotional Intelligence as a Tool for Sustainable Development: Insights from Student Projects. INCOSE International Symposium, 35: 1479-1489. https://doi.org/10.1002/iis2.70042
  • Jayasooriya, V., Waidyalankara, S., Dassanayake, S. (2025). Mobility and Transport Improvements for Human Health. In: Brears, R.C. (eds) The Palgrave Handbook of Ecosystems and Wellbeing. Palgrave Studies in Sustainable Futures. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-24952-5_20-1
  • Ji, S., Zhang, J., Su, H., Hu, D., Hu, Z., Zhang, H., & He, W. (2025). Optimizing Charging Pile Resource Allocation in Public Charging Stations Using Agent-Based Modelling. 2nd International Seminar on Artificial Intelligence, Computer Technology and Control Engineering. https://doi.org/10.1109/ACTCE66599.2025.00121
  • Ji, T., Stankov, I., Sherman Egan, N., Hassmiller Lich, K., Thornton, R. L.J., Wang, Q., Igusa, T., Wei, H., & Matson, P.A. (2025). Investigating the impacts of alcohol outlet zoning policy on alcohol consumption and access to non-alcoholic services: A spatial agent-based simulation. Health & Place 94.
  • Jiang, Y., Ding, Q., Li, M. and Ding, W., Cheng, C. (2025). Phase transition and opinion-action decoupling in social innovation: A two-layer network model with the spiral of silence. SSRN. http://dx.doi.org/10.2139/ssrn.5947722
  • Jimenez-Romero, C., Yegenoglu, A., Blum, C. (2025). Multi-agent systems powered by large language models: applications in swarm intelligence. Frontiers in Artificial Intelligence 8, https://doi.org/10.3389/frai.2025.1593017
  • Jin, C., LI, L., Zhang, X., Wang, J., Merveille, N., & Zhang, J. Trading-Off Non-Monetary for Monetary Incentives in Community-Based Home Charging Stations Sharing Diffusion. Available at SSRN 5236362.
  • Joshi, C. H. (2025). Persist or Give Up? Social Insect Decision-Making When Faced With Problems (Doctoral dissertation, The University of Arizona).
  • Jung, J., & Gulden, T. R. (2025). From inclusion to innovation: using an agent-based simulation to unravel the micro-mechanisms of the macro-level tolerance-innovation link. International Journal of Organization Theory & Behavior 28(3) 343-371. https://doi.org/10.1108/IJOTB-09-2023-0189
  • Jung, J., Miller, J. H., & Page, S. E. (2025). Agent-based modeling for psychological research on social phenomena. The American Psychologist. https://doi.org/10.1037/amp0001530
  • Kaim, A., Schmitt, T.M., Annuth, S.H., Haensel, M., & Koellner, T. (2025). An agent-based model to simulate field-specific nitrogen fertilizer applications in grasslands. European Journal of Agronomy 165.
  • Kalu, E. I., Amadi, A. N., Enebeli, U. U., Uzochukwu, B. S. C., Or-Dinioha, B., Cherima, Y. J., Kalu, F. A., Igwe, P. C., Kalu, J. J., Kalu, B. O., Nwokenna, U. S., & Hassan, R. K. (2025). Spatio-temporal dynamics of flood-induced Anopheles breeding sites and malaria hotspots in urban Abia State: A GIS-based cohort study. Hensard Journal of Environment, 1(1)
  • Kamal, J.B., Kashem, S.B., Rashid, S. (2025). Islamic Versus Mainstream Bank Behavior Using Agent-Based Modelling Simulation. In: Taghizadeh-Hesary, F., Mohd Thas Thaker, H., Bhatti, M.I., Allah Pitchay, A. (eds) Islamic Financial Markets and Institutions. Contributions to Economics. Springer, Singapore. https://doi.org/10.1007/978-981-96-8650-6_3
  • Kamali, A., Niksokhan, M. H., & Ardestani, M. (2025). Evaluating the environmental and economic effects of groundwater quotas and markets using multi-agent system simulation and centralized optimal model. Agricultural Water Management, 320, 109853.
  • Kamińska, B., Sznajd-Weron, K. (2025). Impact of cognitive dissonance on social hysteresis: Insights from the expressed and private opinions model. Expert Systems with Applications 273, https://doi.org/10.1016/j.eswa.2025.126851
  • Karabiyik, T., Jaiswal, A., & Laux, C. (2025). From Classroom to Global Impact: Systems Thinking in Addressing UN-Identified Challenges. In IISE Annual Conference. Proceedings (pp. 1-6). Institute of Industrial and Systems Engineers (IISE).
  • Karel Vlaeminck, Charlotte Nys, Katrien Arijs, Jelle Mertens, Karel De Schamphelaere, A modeling approach to predict population-level impact of silver (Ag) to rainbow trout (Oncorhynchus mykiss), Environmental Toxicology and Chemistry, Volume 44, Issue 7, July 2025, Pages 1937–1952
  • Karoglan, M. (2025). Usporedba algoritama na modelu Braessovog paradoksa u Netlogo-u. [Master's thesis, : University of Split]. Digital Repository of Faculty of Science in Split. https://urn.nsk.hr/urn:nbn:hr:166:458782
  • Karrenberg, C., Edwards, E., Berglund, E.Z. (2025). Agent-based modeling to assess the effects of time-of-use water tariffs on social equity. Utilities Policy 99. https://doi.org/10.1016/j.jup.2025.102130
  • Kaushik, K. (2025). Nationalist Mobilisation in the Digital Age: The Role of Social Media in Driving Support for Radical-Right Parties [Doctoral dissertation, Universidad Carlos III de Madrid]. Political Behaviour Journal.
  • Kelter, J. Z., & Emery, J. D., & Wilensky, U. J. (2025, June). Computational Modeling in Materials Science and Engineering: Student Responses to a Restructurated. Introductory Course Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada. doi: 10.18260/1-2--56132
  • Keshavamurthy, D., Kumar, M., Tsaramirsis, G., & Oroumchian, F. (2025). An AI-Based Framework for Secure and Transparent Banking: Integrating Adversarial Robustness, Interpretability, and Organizational Modeling. Security and Privacy 9(1), https://doi.org/10.1002/spy2.70153.
  • Khan, I. (2025). [Social] Allostasis: Or, How I Learned To Stop Worrying and Love The Noise. Proceedings of the Artificial Life Conference 2025, ASME. https://doi.org/10.1162/ISAL.a.912
  • Kim, E., & Cha, H. (2025). Development and Effects of a Simulation-Based Evolution Instruction Program for Middle School Students. Biology Education, 53(4), 557-569. http://dx.doi.org/10.15717/bioedu.2025.53.4.557
  • Kimemiah Mwangi, P., T. Njenga, S., & Ndung’u Kamau, G. (2025). Temporal Analysis of an IOT distributed ledger simulation using NetLogo and agents.jl. Journal of Computer Sciences and Applications, 13(1), 16–28.
  • Kininmonth, S. J. & Beeden, R. J. (2025). Snakes and Ladders: Adopting a Globally Renowned Game to Enhance Understanding of Ecological Sustainability. Preprints.org
  • Kitagawa, Y., & Akiyama, E. (2025). Influence of Agent Social and Network Segregation on Fake News Diffusion. 2025 IEEE International Conference on Big Data. DOI: 10.1109/BigData66926.2025.11402069.
  • Knoke, D. (2025). Network Collective Action: Agent-Based Models of Pandemics, Riots, Social Movements, Insurrections and Insurgencies. Springer. https://doi.org/10.1007/978-3-031-86199-4
  • Koblentz, E., & Kennedy, W. (2025). The Impact of Information Availability on Trust Emergence in an Agent-Based Model of Stag Hunt. Journal of Student-Scientists' Research, 7.
  • Koid, E., van der Voort, H., & Warnier, M. (2025). Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections. Data & Policy, 7, e6.
  • Koide, R., Murakami, S., Yamamoto, H., Nansai, K., Quist, J., & Chappin, E. (2025). Prospective life cycle and circularity assessment of circular business models using an empirically grounded agent-based model. Journal of Industrial Ecology 29(5), 1897-1911, https://doi.org/10.1111/jiec.70090.
  • Konrad, C. M., Dabiri, A., Schulte, F., Moore, J. K., & Happee, R. (2025). Cycling safety assessment in microscopic traffic simulation: A review and methodological framework. Transportation Research Interdisciplinary Perspectives, 34(101734). https://doi.org/10.1016/j.trip.2025.101734
  • Koraman, E., & Erdil, E. (2025). Modeling Hierarchical Power Distance Relationship in Group Decision Making Dynamics Through Agent-Based Modeling. Available at SSRN. http://dx.doi.org/10.2139/ssrn.5723661
  • Kreikemeyer, J. N., Jankowski, M., Wilsdorf, P., & Uhrmacher, A. M. (2025, June). Synopsis: Using (Not-so) Large Language Models to Generate Simulation Models in a Formal DSL: A Study on Reaction Networks. In 39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (pp. 56-57).
  • Krisnawati, M., Mulyani, Y.P., & Sopha, B.M. (2025). Introducing Adaptive Q-Learning in Agent-Based Models: A Case Study of Household Climate Decision. Proceedings of the 2025 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE. 10.1109/IEEM63636.2025.11357584
  • Kuang, M., Yan, W., Wang, Q., & Zhang, Y. (2025). Self-Organizing Coverage Method of Swarm Robots Based on Dynamic Virtual Force. Symmetry, 17(8), 1202.
  • Kuo, T.C., Zulkarnain, I., Rahmat, D.A., Rumanti, A.A., Pulungan, M.A. (2025). Large Language Model Integration in Agent-Based Modeling for the Simulation and Recycling System. SSRN. http://dx.doi.org/10.2139/ssrn.5897130
  • Laatabi, A., Gaudou, B., Hanachi, C., & Stolf, P. (2025). A Survey on the Use of Agent-Based Modeling and Simulation for the Vehicle Routing Problem. ACM Computing Surveys.
  • Lammers, D., Grimm, V., Requier, F., Focks, A., & Groeneveld, J.  (2025). Towards using the BEEHAVE honey bee model across climates: a heuristic approach to let egg-laying rates emerge from weather conditions, pollen storage and brood pheromones. SSRN 5334581.
  • Langbeheim, E., & Bielik, T. (2025). Student engagement in constructing computer models in K-12 science education: a systematic.
  • Lange, J., Koch, A., Knöchelmann, L., & Alves, H. (2025). Attitude Formation Based on Political Views: An Expectancy-Value Model of Interpersonal Likability in Politics. Personality and Social Psychology Bulletin. Advance online publication.
  • Lara-Lopez, L. C., Helo-Guzmán, J., Trejos-Zelaya, I., & Núñez-Corrales, S. (2025). A Feature Space Maturity Model for ABM Frameworks: Definition and Experimentation. IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies. https://doi.org/10.1109/CHILECON66915.2025.11476046
  • Lara-Lopez, L. C., Helo-Guzmán, J., Trejos-Zelaya, I., & Núñez-Corrales, S. (2025). Macroscopic Statistical Analysis & Specification Fidelity of ABM Simulations. 2025 LI Latin American Computer Conference. https://doi.org/10.1109/CLEI67442.2025.11420587
  • Laver, M. (2025). District magnitude and substantive representation. Irish Political Studies, 1–18. https://doi.org/10.1080/07907184.2025.2553530
  • Lee, H. W., DeAngelis, D. L., Yurek, S., & Papastamatiou, Y. P. (2025). Foraging of wading birds on a patchy landscape: Simulating effects of social information, interference competition, and patch selection on prey intake and individual distribution. Ecological Modelling 507.
  • Leonard-Duke, J., Agro, S. M., Csordas, D. J., Bruce, A. C., Eggertsen, T. G., Tavakol, T. N., ... & Peirce, S. M. (2025). Multiscale computational model predicts how environmental changes and treatments affect microvascular remodeling in fibrotic disease. PNAS Nexus, 4(1), pgae551.
  • Leoni, S. & Catola, M. (2025). Green Transition and Environmental Policy in Imperfectly Competitive Markets: Insights from Agent-Based Modelling. Department of Economics and Management – University di Pisa, Discussion Paper (326).
  • Letschert, J., Müller, B., Dressler, G., Möllmann, C., & Stelzenmüller, V. (2025). Simulating fishery dynamics by combining empirical data and behavioral theory. Ecological Modelling, 501, 111036.
  • Levy, S.T., Sacks, D.P. (2025). Simulations Storybook: Supporting Young Children's Growing Understanding of Complex Systems. Proceedings of the 20th Chais Conference for the Study of Innovation and Learning Technologies: Learning in the Digital Era.
  • Li, H., Huan, J. & Xie, J. (2025). Research on the evolution law of supply chain disruption based on Complex Adaptive System. Flex Services and Manufacturing Journal. https://doi.org/10.1007/s10696-025-09627-4
  • Li, J., Wang, J., Wu, J., & Wang, X. (2025). Bacillus subtilis biofilm expansion mediated by the interaction between matrix-producing cells formed “Van Gogh bundles” and other phenotypic cells. Colloids and Surfaces B: Biointerfaces, 251(114611). https://doi.org/10.1016/j.colsurfb.2025.114611
  • Li, L., Zhang, X., Wang, J., Jin, C., Harris, M., Merveille, N., & Zhang, J. (2025). Replacing monetary with social incentives for community-based home charger owners’ sharing adoption. Transportation Research Part D: Transport and Environment, 149, 105070.
  • Li, R., Wang, Z., Long, T., Tang, H., Xu, M., You, L., & Wang, L. (2025). Evaluation of Contribution Rate of UAV Swarms System Based on Machine Learning. Proceedings of the 4th International Symposium on Aerospace Engineering and Systems (ISAES), 104-109, IEEE, 10.1109/ISAES66870.2025.11274397.
  • Li, S., Hao, J., Chai, Y., Liu, Y., Feng, T., & Zhu, X. (2026). Emotional Contagion Mechanism and Response Strategy Simulation of Corporate Crises from the Perspective of Online Collective Action. Systems, 14(1), 1. https://doi.org/10.3390/systems14010001
  • Li, T. (2025). Consumer Heterogeneity and E-Retailing Market Concentration: An Agent-Based Approach. Journal of Artificial Societies and Social Simulation, 28(4). doi: 10.18564/jasss.5699
  • Li, T., Flache, A., & Jager, W. (2025). How culture can affect opinion dynamics: the case of vaccination. Journal of Computational Social Science, 8(1), 1-45.
  • Li, T., Wang, S., Zhou, D., & Razzaq, A. (2025). Consumer attention and market concentration in e-commerce: an agent-based perspective. Journal of Economic Interaction and Coordination 20.
  • Li, X., Hao, J., Ding, Z., Yang, J., & Yao, Y. (2025). Unveiling the driving mechanism of greenwashing behaviors: an agent-based modeling approach integrating new institutionalism and halo effect theories. Humanities and Social Sciences Communications, 12(1), 1-16.
  • Li, Y., Liu, Y., & Wang, W. (2025). How demand-side incentive policies drive the diffusion of Forest Wellness Tourism Products: An agent-based modeling analysis. Forest Policy and Economics, 174, 103496.
  • Li, Y., Xia, P., Zhu, Y., Yuan, B., Wang, Z., & Zhang, B. (2025). The Constraints and Incentives of Carbon Quota Penalty Intensity on Both Parties of Carbon Quota Trading: An ABM Modeling Based on the Power Industry in the National Carbon Market. Proceedings of the 2025 5th Power System and Green Energy Conference (PSGEC). IEEE. 10.1109/PSGEC66102.2025.11150973
  • Liang, C.-L., Collins, B. (2025). Applying Agent-Based Modeling to Examine Business Strategies – Tools and Examples for Researchers and Practitioners. Small Business Institute Journal, 21(1), 16–24. https://doi.org/10.53703/001c.138368
  • Liberatore, A., Sousa, A. E., Cardoso, P., & Pais, S. (2025). Immersive Technologies in Italian Cultural Tourism: An Exploratory Analysis of Visitor Satisfaction and Predictive Modeling. Studia Universitatis Babes Bolyai - Negotia 70(2) 43-76.
  • Limina, V. (2025). The ‘sustainable’ lightness of being ‘complex’. RELOAD: a new project for reconstructing ancient landscapes in northern Tuscany. In G. D’Alessio, S. De Nisi, C. Felici, D. Gangale Risoleo, S. Pesce & I. Raimondo (Eds.), Landscape 4: una sintesi di elementi diacronici (pp. 202-216). Archaeopress.
  • Lin, B., Liu, J., Li, K., & Zhong, X. (2025). Modeling transmission of pre-exposure prophylaxis non-adherence among MSM using data-driven agent-based approaches: a real-world study. Annals of Operations Research, 1-22.
  • Lin, S., Koch, J. R., Barnes, A. J., Hayes, R. B., & Xue, H. (2025). Assessing the effects of social media based anti-tobacco campaigns on tobacco use among youth in Virginia: an agent-based simulation approach. European Journal of Public Health 35(5).
  • Linzbuoy, L., Gilthorpe, M. S., Heppenstall, A. J., & Ge, J. (2025). A causal inference framework for individual-level models. International Journal of Population Data Science, 10(5).
  • Liu, B., Shao, Y., Zhou, X., & Jin, Y. (2025). Can government subsidies affect enterprises’ strategies of breakthrough innovation? A two-stage evolutionary analysis. Strategic Business Research, 100009.
  • Liu, C., Moss, S. R., Perraki, A., Plumb, W., & Kaundun, S. S. (2025). Inferring herbicide non-target-site cross-resistance from dose response and mixture treatments. Pest Management Science 81(6), 2901-2909.
  • Liu, C., Wang, W., Hou, Z., & Chen, S. (2025). Optimization and Enhancement of Government-Guided Fund Management Models: A Simulation-Based Analysis of District J in Shanghai. In A. Mallik et al. (Eds.), Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025), Atlantis Highlights in Economics, Business and Management 4, https://doi.org/10.2991/978-94-6463-845-5_80
  • LIU, J., BU, Y., ZHOU, S., ZHANG, Y., HUANG, X., TANG, J. H. C., & ZHUGE, C. SelfSim: An Agent-based Integrated Framework for Simulating the Impacts of Sustainable Technologies, Infrastructures and Policies in Smart Cities.
  • Liu, J., Yuan, X., Ruan, W., & Bai, J. (2025). Infection Risk Control of Pedestrians in Subway Stations under Epidemic of Respiratory Infectious Diseases. Chinese Journal of Management Science, 33(5), 236-246. https://doi.org/10.16381/j.cnki.issn1003-207x.2022.1511
  • Liu, J., Zhuge, C., Tang, J.H.C.G., Meng, M., & Zhang, J. (2025). Consumers' attitudes toward benefits and drawbacks of vehicle-to-grid technology: An agent-based model. International Journal of Sustainable Transportation 19(12).
  • Liu, L. (2025). 12 Agent-Based Modeling and. Urban Human Mobility: Practices, Analytics, and Strategies for Smart Cities, 138.
  • Liu, Q. & Wang, Y. (2025). Modeling and Analysis of Opinion Dynamics Model Based on the Information Point Mechanism. In Proceedings of the 2025 International Conference on Digital Management and Information Technology, pp. 553-560.
  • Liu, S., Li, C., He, Q., Men, B., Wu, D., Hai, R., Wu, X. & Ding, H. (2025). Layer-resolving ability and model analysis of LIBS for multilayer samples with a four-layer structure under different focusing conditions. Journal of Analytical Atomic Spectrometry.
  • Liu, T., & Zhu, Q. (2025). The impact of data sharing on the sustainable development of aged care service platforms. Journal of Cleaner Production, 534(147063). https://doi.org/10.1016/j.jclepro.2025.147063
  • Liu, X., Li, W., Pan, C., & Zhou, S. (2025). AI-DTCEM: A Capability Ecology Framework for Dual-Qualified Teacher Team Construction. Applied Sciences, 15(21), 11392.
  • Liu, Y., & Zhang, Y. (2025). Returning to Poverty due to Unequal Opportunities in China: The Key Influencing Factors and Mitigation Policies. Sustainable Development. https://doi.org/10.1002/sd.70567
  • Lockhart, V., McCreary, D., Peterson, T.A. (2025). MicroSims: A Framework for AI-Generated, Scalable Educational Simulations with Universal Embedding and Adaptive Learning Support. Arxiv Cornell University. https://doi.org/10.48550/arXiv.2511.19864
  • López, A.R., & Fraire, R.O.R. (2025). Efectos del modelo Ciudad Cercana en la ciudad de Chihuahua: análisis de la segregación socioespacial mediante simulación basada en agentes. Teoría y Praxis 34, 89-97. 10.22403/UQROOMX/TyP34/10
  • Lorenz, J., & Gunes, E. (2025). Filter bubbles in an agent-based model where agents update their worldviews with LLMs. ESWC 2025 Workshops and Tutorials Joint Proceedings. https://ceur-ws.org/Vol-3977/SemGenAge-3.pdf
  • Lovaco, J., Munjulury, R. C., & Krus, P. (2025). Contextualised generative AI in system of systems modelling: an approach for firefighting aircraft requirements. The Aeronautical Journal, 1–29. doi:10.1017/aer.2025.10097
  • Lovell, C., Williams, J., Mondolini, S., Pettorelli, N. (2025). Individual-based models allow accurate prediction of introduced large herbivore populations in rewilded landscapes. In Sreekar, R. (Ed.), Journal of Applied Ecology 00(e70264). https://doi.org/10.1111/1365-2664.70264
  • Lu, G., Fu, F., & Tang, Z. (2025). Research on Quantification Mechanism of Data Source Reliability Based on Trust Evaluation. Computers, Materials, and Continue 83(3), 4239-4256.
  • Lu, L., Li, J., Yang, X., & Zhang, Y. (2025). Spatiotemporal Epidemic Diffusion in Urban Tourist Areas: Modelling Tourist Flow and Virus Spread. Applied Spatial Analysis and Policy, 18(4), 138.
  • Lu, P., & Li, Y. (2025). Agent Model Based on Large Language Model as a Pathway to General Artificial Intelligence in Revolutionizing Crowd Dynamics Research. SSRN. http://dx.doi.org/10.2139/ssrn.5496390.
  • Lu, P., & Li, Y. (2025). An agent-based model for simulating cooperative behavior in crowd evacuation during toxic gas terrorist attacks. Chaos, Solitons, and Fractals 196. https://doi.org/10.1016/j.chaos.2025.116397
  • Lu, X., Xu, Z., Li, Y., Hu, X., Song, P., Shi, Q. (2025). A micro–macro coupled approach to assess urban road traffic flood risk at the city scale. Journal of Hydrology 664(B), https://doi.org/10.1016/j.jhydrol.2025.134435.
  • Luis, M., Harper, A., Monks, T., & Mustafee, N. NETLOGOPY: UNLOCKING ADVANCED SIMULATION AND INTEGRATION FOR NETLOGO USING PYTHON.
  • Luttermann, M., Prestele, R., Grimm, V., & Groeneveld, J. (2025). Expanding the Scope of the Bumblebee Model BEE-STEWARD: A Simple Foraging Module Facilitates the Parameterization. Ecology and Evolution 15(5).
  • Lv, J., Wang, X., & Chen, Y. (2025). Agent-based simulation of trust networks and opportunistic behaviours of hydraulic infrastructure project participants. PloS one, 20(1), e0316992.
  • Lv, W., Deng, F., Wang, J., Han, Y., & Yang, S. (2025). Large-scale real-time evacuation modeling during urban floods: a coupled agent-based multi-model framework. Simulation Modelling Practice and Theory, 103075.
  • M. Yingling, T. O'Neill, T. C. Skalak and S. Peirce-Cottler, "A cellular automata model of circulating cell adhesion and transmigration in the microvaculature," 2005 IEEE Design Symposium, Systems and Information Engineering, Charlottesville, VA, USA, 2005, pp. 356-361, doi: 10.1109/SIEDS.2005.193280. keywords: {Adhesives;Stem cells;Bones;Computational modeling;Predictive models;Injuries;Mice;Muscles;Animals;Medical treatment},
  • Mack, D. (2025). Compartmental Disaggregation: Bridging Simulation and Sampling Methods for Synthetic Population Data Generation. [Doctoral Dissertation, Washington University in St. Louis]. McKelvey School of Engineering Theses & Dissertations.
  • Macke, S., Sadiq, A. O., Lyons, M., & Swahn, M. (2025). Modeling Homelessness: An Agent-Based Simulation of Social Dynamics and Housing Instability.
  • Madamba, T., Moreira, R. G., Castillo, A., & Mohebbi, Z. (2025). Simulating listeria monocytogenes cross‐contamination of fresh avocado in a dry pack line: A hybrid approach to food safety. Journal of Food Process Engineering, 48(10). https://doi.org/10.1111/jfpe.70223
  • Mahdi, M. A., Yousif, A. Y., & Salman, M. A. (2024). Learning Approach. In Innovations of Intelligent Informatics, Networking, and Cybersecurity: Second International Conference, 3INC 2024, Babylon, Iraq, October 15–16, 2024, Proceedings (p. 3). Springer Nature.
  • Maione, V., Ponsiglione, C., & Primario, S. (2025). Modeling Fake News Diffusion for a Resilient Social Media Platform: Agent-Based Approaches to Responsible Social Media. Unina.
  • Maire, J.L., Jimenez, J.F., & Himmiche, S. (2025). ErgoTwin: A Human-Centric Digital Twin for Real-Time and Predictive Ergonomic Risk Assessment in Smart Manufacturing. SSRN. http://dx.doi.org/10.2139/ssrn.5645063
  • Malik, M. M. S3. 2.4: Introduction to Computational and Data Science in the Social Sciences.
  • Malikov, M. (2025). A Network Approach to Scaling Agent-Based Model Outputs (Publication No. 32042796) [Doctoral Dissertation, George Mason University]. ProQuest Dissertations & Theses.
  • Malikov, M. & Salazar-Serna, K. (2025). Realistic Social Networks in Agent-Based Modeling. 2025 Annual Modeling and Simulation Conference (ANNSIM), Madrid, Spain.
  • Mandal, P., & Manley, E. (2025). Modelling the Role of Mobility in Cholera Transmission Dynamics. 33rd Annual GIS Research UK Conference.
  • Manseau, J. (2025). Understanding The Effects Of AI Tasks And Social Deskilling: An Agent-Based Model.
  • Mapa, M., Sun, Y., Kuligowski, E. D., Cova, T. J., Lovreglio, R., & Zhao, X. (2026). Analyzing evacuation route choices during the 2021 Marshall Fire. Safety Science, 194, 107046.
  • Marisetty, V., van Heeswijk, W., & Narayanan, A. An Agent-Based Model of Rumor-Induced Volatility in Financial Markets. Available at SSRN 5375758.
  • Mathieu, P., Nongaillard, A., & Simon, E.P. (2025). Infrastructure intelligente pour l'allocation de puissance dans les systèmes MIMO de type cell-free. Journées Francophones sur les Systèmes Multi-Agents (JFSMA), Jul 2025, Dijon, France. pp.43-53. Available at hal-05214291.
  • Matsui, K., & Hashimoto, T. (2025). The methodology for identifying factors contributing to the acceptance of behavioral change‑led policies: the case of stay‑at‑home requests during the COVID‑19 pandemic in Japan. Evolutionary and Institutional Economics Review 22, 19-54. https://doi.org/10.1007/s40844-025-00299-1
  • McDannald, A. Agent, Agentic, and Distributed Artificial Intelligence: From Managing Next-Generation Labs to the Philosophy of Science A. Gilad Kusne1, 2 (ORCID: 0000-0001-8904-2087).
  • Medupe, D. (2025). The Role of Risk in the Emergence of Intensive Agriculture and Complex Stratified Societies: The Oasis-Goldilocks Hypothesis of Agriculture Intensification. [Doctoral dissertation, Pennsylvania State University].
  • Meister, H. (2025). Some Agent-Based Concepts for Modeling and Simulation. Fakultät für Mathematik und Informatik.
  • Meng, X., Li, D., Li, C., Zhang, H., Piao, X., & Luan, H. (2025). Coordinated Optimal Dispatch of Source–Grid–Load–Storage Based on Dynamic Electricity Price Mechanism. Energies, 18(23), 6277. https://doi.org/10.3390/en18236277
  • Merz, M. (2025). Ecological Consequences of Small Mammal Personalities in Forest Ecosystems (Publication No. 4217) [Doctoral dissertation, University of Maine]. Electronic Theses and Dissertations. https://digitalcommons.library.umaine.edu/etd/4217
  • Merz, M.R., Brehm, A.M., Humphreys, B.R. et al. (2025). Integrating small mammal personality and population abundance into forest regeneration predictions for a managed, mixed species forest in Maine, USA. Sci Rep 15(45426). https://doi.org/10.1038/s41598-025-29879-2
  • Mesas, F., Taboada, M., Rexachs, D., Epelde, F., Wong, A., & Luque, E. (2025). A Lego-inspired, Modular Agent-Based Metasystem for Emergency Departments. International Journal on Advances in Intelligent Systems, 18(1 & 2). http://www.iariajournals.org/intelligent_systems/
  • Mesas, F., Taboada, M., Rexachs, D., Epelde, F., Wong, A., & Luque, E. (2025). An Adaptive Agent-Based Model Metasystem for Emergency Departments. In P. Pesado and P. Thomas (Eds.), Computer Science – CACIC 2024: 30th Argentine Congress of Computer Science, La Plata, Argentina, October 7–10, 2024, Revised Selected Papers, 37-49. https://doi.org/10.1007/978-3-032-00718-6_3
  • Mesas, F., Taboada, M., Rexachs, D., Epelde, F., Wong, A., Luque, E. (2025). A Customizable Agent-Based Simulation Framework for Emergency Departments. In: Lees, M.H., et al. Computational Science – ICCS 2025. ICCS 2025. Lecture Notes in Computer Science, vol 15906. Springer, Cham. https://doi.org/10.1007/978-3-031-97635-3_6
  • Mhade, S., Bhosekar, U., Hill, M.D., Sinclair, S., Agrawal, S., Guerrini, J., Zou, L., Koebcke, A., Kummer, A.G., Ventura, P.C., Del Valle, S.Y., Chinazzi, M., Livinova, M., Vespignani, A., & Ajelli, M. (2025). Mapping the Landscape of Individual-Based Models for Respiratory Pathogen Transmission in the Pandemic and Post-Pandemic Era (2020-2024): A Systematic Review. SSRN. http://dx.doi.org/10.2139/ssrn.5736584
  • Mokros, J., Sagrans, J., & Noyce, P. (2025). Supporting Middle School Students’ Understanding of Time-Series Data With Graph Comparisons. Journal of Statistics and Data Science Education, 34(1), 101-112. https://doi.org/10.1080/26939169.2025.2560338
  • Molle, A. (2025). Hearts, Minds, and Explosives: Modeling Civilian Loyalty Dynamics in Irregular Warfare. Advance. 10.31124/advance.175696898.82318757/v1
  • Mondal, K., Menon, B.G., & Sahadev, S. (2025). Modeling for the Growth of Unorganized Retailing in the Presence of Organized and E-Retailing in Indian Pharmaceutical Industry. arXiv:2507.17023.
  • Monlezun, A. C., Boone, R. B., Wagner, G., Trainor, N., & Lynn, S. J. (2025). Agent-Based Modeling as a Tool for Ecological Comanagement of Grazing Lands. Rangeland Ecology & Management 99, 88-101. https://doi.org/10.1016/j.rama.2025.01.003
  • Monroe, J.I., Nayani, K., Simpson, T.K., Walker, H.L., & Clausen, E. (2025). University of Arkansas Chemical Engineering Summer Academy for High School Students. Proceedings of the 2025 ASEE Midwest Section Conference, American Society for Engineering Education.
  • Moraes, L. B. D. (2025). Simulação de um sistema de fabrico usando modelação baseada em agente.
  • Morales, D., Chaigneau, S. E., Canessa, E. (2025). An Agent-Based Model of Semantic Memory Search: Disentangling Cognitive Control and Semantic Space Organization. Cognitive Science 49(12).
  • Morell, J.A., Parunak, H.V.D., Knestis, K., & Hwalek, M. (2025). Agent-based Modeling as an Evaluation Methodology. Evaluation Uncertainty.
  • Moreno, M. (2025). Subtracting Self-Selection Bias from Scientific Inquiry: A Computational Exploration. Erkenntnis, 1-28.
  • Moss, A. D. (2025). Follow the Leader: Group Recruitment in the “Greedy” Ant Tetramorium immigrans (32396536). [Doctoral Dissertation, University of Colorado Denver]. ProQuest Dissertations & Theses Global.
  • Mubina, T. (2025). UMUMTA’LIM MAKTABLARIDA BIOLOGIYA VA MATEMATIKA FANLARIDAN INTEGRATSIYA DARSLARINI TASHKIL ETISH. GERMANY-SCIENTIFIC REVIEW OF THE PROBLEMS AND PROSPECTS OF MODERN SCIENCE AND EDUCATION, 1(4), 22-28.
  • Mukherjee, S., Datta, S., & Majumder, S. (2025). On Separation and Connection: A Multi-Systems Study of Developer Interaction using Agent-Based Models. SSRN. http://dx.doi.org/10.2139/ssrn.5734055
  • Murphy, K. J. (2025). Agent‐based models in applied ecology: Designing data‐informed simulations for wildlife conservation and management. Ecosphere, 16(9), e70342.
  • Murphy, K. J., Byrne, A. W., Marples, N., O'Hagan, M. J. H., Kelly, D. J., Quinn, D., Breslin, P., Morera-Pujol, V., Khouri, R. M., Barrett, D., McGrath, G., Ciuti, S. (2025). Wildlife response to land-use change forces encounters between zoonotic disease hosts and farms in agricultural landscapes. Agriculture, Ecosystems, & Environment 386. https://doi.org/10.1016/j.agee.2025.109561
  • Mustillo, T. (2025). Building a FAIR, linked, and open data ecosystem: innovation cascades in political science. European Political Science, 1-16.
  • Mutlutürk, M., Badur, B., & Metin, B. (2025). Phishing-Driven Malware in Smes: A Socio-Technical Agent-Based Model of Endpoint Security and Response Policies. SSRN. http://dx.doi.org/10.2139/ssrn.5402207
  • Nabila, P. T., Hardhienata, H., & Alatas, H. (2025). Modeling of communication within termite-like population using agent-based model: Pheromone evaporation effects. BIO Web of Conferences, 175, 03006. https://doi.org/10.1051/bioconf/202517503006
  • Nascimento, D.A.d., Albuquerque, N.H.A.d., Alves, M.H.C., Souza, P.V.S. (2025). Tráfego urbano e sistemas complexos: explorando o NetLogo no ensino de física. A Física na Escola 23(1). DOI: 10.59727/fne.v23i1.311.
  • Nasirzonouzi, A., & Gershenson, C. The Dynamics of Word-of-Mouth Marketing: An Agent-Based Modeling Approach to Message Credibility and Consumer Engagement. Northeast Journal of Complex Systems 7(2). https://doi.org/10.63562/2577-8439.1116
  • Ne’matabad, E.F., Abbasi, E., & Hadizadeh, A. (2025). Designing an Agent-Based Model for Evaluating the Feasibility of Operational Budgeting in the Municipalities of Qazvin Province. Rahbord-e-Tousee, 21(83), (pp. 357-384).
  • Nejad, A. Z. S., Baltes, J., Nejad, A. Z. S., & Sadeghnejad, S. Developing an Open Innovation Model for Advanced Robotics: A Systems Thinking Framework in the FIRA Innovation & Business League.
  • Ngo, A.T., See, L., & Van Mai, T. (2025). Economic Coordination for Climate Policy: Optimizing Livestock Sector Emission Reduction Using Genetic Algorithms and Agent-Based Modeling in Quang Binh, Vietnam. Environmental Modeling and Assessment, 30, 12153-1268.
  • Nikulina, A. (2025). Anthropogenic landscapes? Modelling the role of huntergatherers in interglacial ecosystems in Europe (813904) [Doctoral Dissertation, Leiden University]. Leiden University Scholarly Publications.
  • Nikulina, A., Zapolska, A., Serge, M.A., Mazier, F., Davoli, M., Pearce, E.A., Svenning, J., van Wees, D., Fyfe, R., MacDonald, K., Roebroeks, W., & Scherjon, F. On the ecological impact of prehistoric hunter-gatherers in Europe: Early Holocene (Mesolithic) and Last Interglacial (Neanderthal) foragers compared. PLoS One 20(10): e0328218. https://doi.org/10.1371/journal.pone.0328218
  • Niu, Z., Asselman, J., Janssen, C. R., Linley, C. P., De Schamphelaere, K., Everaert, G., & Catarino, A. I. (2025). Combined exposure of poly(lactic-co-glycolic acid) microplastics and global warming affects the population dynamics of a coastal copepod. Marine Pollution Bulletin, 221.
  • Njegovanovic, A., & Flodin, S. (2025). A Comparison of the Crowd Simulators NetLogo and Pathfinder in Arena Evacuation Situations (Dissertation). https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-367565.
  • Nunes, J. X., Ramos, M. I., Costa, W. C., & Almeida, R. T. S. (2025). AVALIAÇÃO ESPACIAL DOS REGISTROS DE DENGUE NO MUNICÍPIO DE NERÓPOLIS NO ANO DE 2022. REVISTA UNIARAGUAIA, 20(1), 56-65.
  • Núñez Barahona, E. G. (2025). Modelado basado en agentes y el surgimiento del liderazgo en equipos nuevos: optimización de la toma de decisiones desde el enfoque del liderazgo ético. Región Científica, 4(2), 2025495. https://doi.org/10.58763/rc2025495.
  • Nwokoye, C.H. (2025). Multigroup e-SIjRjTj Model for Malware Propagation in Wireless Sensor Networks. IAENG International Journal of Computer Science, vol 52. (7), p. 2075-2090.
  • Nwokoye, C.H., Iloka, B.O., Waldron, S., & Ezzeh, P. (2025). Machine Learning Epidemic Predictions Using Agent-based Wireless Sensor Network Models. arXiv, Cornell University, https://doi.org/10.48550/arXiv.2511.15982.
  • O’Gara, D., Kasman, M., Hébert-Dufresne, L., & Hammond, R. A. (2025). Adaptive behaviour during epidemics: a social risk appraisal approach to modelling dynamics. Journal of the Royal Society Interface, 22(222), 20240363.
  • Obringer, R., Peterson, G., White, D. D. (2025). Exploring the Impacts of Climate Change and Water Conservation Attitudes on Urban Water Supply in the Colorado River Basin. Water Resources Research 61(12). https://doi.org/10.1029/2024WR039403.
  • Odden, T. O. B., & Zwickl, B. (2025). How physics students build computational literacy by creating computational literature. Journal of the Learning Sciences, 1-43.
  • Orth, A., & Schütte, R. Dynamic dune management on Sylt: A modelling approach. Crossing Borders, Blending Perspectives, 155.
  • Oruro, E. M., & Pardo, G. E. (2025). From morphogenesis to morphodynamics neuroscience: Modeling the growth of dendritic shape in pyramidal cells of the piriform cortex in infant rats. The European Physical Journal B, 98(5).
  • Osorio Antonia, J., Rivas-Tovar, L. A., & Pérez-Juárez, F. (2025). Simulation of Corn Production in Chicontepec Veracruz with Agent-Based Modeling. Computación y Sistemas, 29(1), 529-544. doi: 10.13053/CyS-29-1-5293
  • Ospina, J.P., & Sánchez, J.F. (2025). A Computational Justice Model for Dynamic ResourceAllocation in Ad Hoc Networks. Complex Systems Informatics and Modeling Quarterly, 43(238), 53-76. https://doi.org/10.7250/csimq.2025-43.04
  • Özkök, M. K. (2025). What A Really Small World: A Simulation Model for the Formation and Patterns of Social Networks in Urban Environments. ASR: CMU Journal of Social Sciences and Humanities (2025) Vol.12 No.2. doi: https://doi.org/10.12982/CMUJASR.2025.018
  • Paiva, E. J. D., & Rocha, A. M. A. (2025). Simulation on Home Healthcare Problem: A Systematic Literature Review. In International Conference on Innovations in Bio-Inspired Computing and Applications (pp. 24-36). Springer, Cham.
  • Palardy, G., Yoo, K. W., Fatma, S., Mukherjee, A., Wang, C., Ravi, P., & Chitnis, A. (2025). SoxB1 family members inhibit Wnt signaling to promote maturation and deposition of stable neuromasts by the zebrafish Posterior Lateral Line primordium. bioRxiv, 2025-04.
  • Palrecha, J., Hegade, P., & Shettar, A. (2025). NetLogo Models for Pattern Recognition in Problem Based Learning. Journal of Engineering Education Transformations 38(2).
  • Pamungkas, K. A., & Ardiansyah, R. (2025). Analysis Of HIV/AIDS Spread Patterns in Central Java Using Agent-Based Simulation. Digital Nexus Systematic Journal, 1(2), 5-8.
  • Pangallo, M., Giachini, D., & Vandin, A. (2025). Statistical Model Checking of NetLogo Models. Cornell University. https://arxiv.org/abs/2509.10977.
  • Pantoja Rojas, L.M. (2025). Simulación de Agentes Cognitivos para el Análisis de Incertidumbre en Gestión de Proyectos. [Doctoral Thesis, Universidad Distrital Francisco José de Caldas]. http://hdl.handle.net/11349/100754
  • Pantoja-Rojas, L, Alzate-Monroy, M, Medina-Garcia, V, & Moreno-Sandoval, L. (2025). Cognitive Agent-Based Modeling to Optimize Complex Project Management Under Uncertainty. IEEE Access, 13, 127263-127279.
  • Pardo, G. E., Cuevas, L. B., Vásquez-Lizárraga, J., Pary, A. C., Pacheco-Otalora, L. F., & Oruro, E. M. (2025). Temporal variation in maternal behavioral transitions under limited nesting conditions across postpartum windows in rats: Implications for neurobehavioral development. Behavioural Brain Research, 495(115769), https://doi.org/10.1016/j.bbr.2025.115769
  • Pardo, G. E., Pary, A. C., Ccori, M. E. G., Aguilar, M. C., Cuevas, L. B., Oruro, E. M., & Pacheco-Otalora, L. F. (2025). Strain-dependent variation in maternal care and early behavioral development in laboratory rats. bioRxiv. https://doi.org/10.1101/2025.11.05.686817
  • Passey, D. J. (2025). Connecting Probabilistic and Mechanistic Frameworks for Causality to the Dynamic Intervention Response Problem (Doctoral dissertation, The University of North Carolina at Chapel Hill).
  • Patil, V., & Naik, V. (2025). Use of Agent-based Modeling to Gauge Impact of Lockdown Strategies on Spread of COVID-19 on an Educational Campus. 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). Available on IEEE XPlore. DOI: 10.1109/COMSNETS63942.2025.10885590
  • Patten, S. B. (2025). A Dimensional Diagnostic Strategy for Depressive Disorders. Journal of Clinical Medicine 14(3). https://doi.org/10.3390/jcm14030844
  • Pavanini, T. (2025). Rural Demand Responsive Transport: Current Developments and Analysis of a Case Study in an Italian Inner Area. Springer Nature.
  • Pazos, A. J. E. (2025). Mecanismo de subasta de los títulos de deuda pública – TES, una aproximación desde modelos basados en agentes. [Master's thesis, Universidad Nacional de Colombia]. Repositorio Institucional Universidad Nacional de Colombia.
  • Pelaez, K.K.E. (2025). Model of adaptation of cloud-based project management technologies in industrial enterprises [Master's thesis, Ural Federal University]. UrFU Electronic Scientific Archive. https://elar.urfu.ru/handle/10995/147450
  • Perumal, C. D., Nofal, A., Kolber, B. J., & Miller Neilan, R. (2025). Web Application for Simulation of an Agent-Based Model in NetLogo3D. Spora: A Journal of Biomathematics, 11(1), 64-75.
  • Picazo, A.G., Graber, A., Feusier, C., Campfens, J.K.E.K, Edwards, J., Baggenstos, M., & Labounko, T. (2025). Between Roots and Resistance: Social Dynamics of Forest Management in Canavese. PLUS. Retrieved January 22, 2026, from https://www.researchgate.net/profile/Jair-Kees-Evert-Karel-Campfens/publication/397785614_Between_Roots_and_Resistance_Social_Dynamics_of_Forest_Management_in_Canavese/links/691ed3b8c2a8e047ef3e3b57/Between-Roots-and-Resistance-Social-Dynamics-of-Forest-Management-in-Canavese.pdf
  • PINDELSKI, M. (2025). STRATEGIC INJUSTICE IN GOALS SETTING AFTER USE OF AI. Scientific Papers of Silesian University of Technology. Organization & Management/Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie, (229).
  • Piskorska, K.M., & Takac, M. (2025). Multi-agent modelling of climate change denial spread. https://cogsci.fmph.uniba.sk/kuz2025/zbornik/34_Piskorska_final.pdf
  • Platt, L. S. & Zeinali, A. (2025). Modeling the Dynamic Outcomes of Infection Prevention Behaviors in an ICU Environment. Health Environments Research & Design Journal 18(2), 174-192.
  • Pleyer, J., & Fleck, C. (2025). cellular_raza: Cellular Agent-based Modeling from a Clean Slate. Journal of Open Source Software, 10(110), 7723.
  • Poodiack Parsons, S., Torenvlied, R. (2025) When noise mitigates bias in human–algorithm decision-making: An agent-based model. PLOS ONE 20(12): e0339273. https://doi.org/10.1371/journal.pone.0339273
  • Prasher, S. (2025). An Empirical and Computational Investigation of Relationship Dynamics and Emergent Social Structure (Publication No. 32322770) [Doctoral dissertation, University of Cincinnati]. University of Cincinnati ProQuest Dissertations & Theses. http://turing.library.northwestern.edu/login?url=https://www.proquest.com/dissertations-theses/empirical-computational-investigation/docview/3256616848/se-2?accountid=12861
  • Prasher, S., & Hobson, E. A. (2025). Exploring the Factors Underlying Adaptive Social Plasticity in Foragers Using an Agent-Based Model. Integrative and Comparative Biology 65(3) 615-630.
  • Praveen, R., Boob, N.S., MuhsnHasan, M., Bansal, S., Kansal, L., & Rajalakshmi, B. (2025). Digital Twin-Enabled Urban Management: Multi-Modal Data Fusion for Real-Time City Simulation and Prediction. Proceedings of the 2025 International Conference on Computing and Communications (COMPUTINGCON) Pune, India. Sep 01-03, 2025. IEEE. 10.1109/COMPUTINGCON64838.2025.11376962
  • Premo, L. S., & Rezek, Z. (2025). Modeling Cultural Transmission in Structured Populations Raises Important Questions for Archaeologists. Journal of Archaeological Method and Theory, 32(1), 1-34.
  • Priya, S.S., & Suganya, R. (2025). Enhanced Context-Aware Testing for Mobile Applications Using a Hybrid Grammatical Evolution Method. Proceedings of 2025 IEEE 4th International Conference for Advancement in Technology (ICONAT). IEEE. 10.1109/ICONAT66879.2025.11362663
  • Prunetti, D., Innocenti, E., Maraninchi, G., Idda, C., Detotto, C., Yuheng, L., & Parker, D. C. (2025). Evaluation of Development Policies of a Tourist Territory Confronted with Intensive Urban Development using a Multi-Agent Modeling. Hal Open Science.
  • Puche Regaliza, J. C., Porras Alfonso, S., Casado Yusta, S., & Pacheco Bonrostro, J. (2025). Impact of gamification to understanding Bayes’ Theorem: A study using the Monty Hall Dilemma Simulation. Dirección y Organización, (86), 16–28.
  • Puspitorini, P. S., Sholikha, N. K., & Efendi, I. B. (2025). AGENT-BASED SIMULATION APPROACH FOR MANAGING LOSS RATE IN THE AGROSUPPLY CHAINS UNDER THE GOVERNMENT SUBSIDY POLICY. JISO: Journal of Industrial and Systems Optimization, 8(2), 108-116.
  • Qasim, R. (2025). Multiscale Integration of Receptor-Ligand Dynamics Into Discrete and Continuous Tumor Growth Models With Application to Tyrosine Kinase Inhibitor Treatment (Master's thesis, The University of Texas at El Paso).
  • Qin, D., & Siyang, L. (2025). Using Agent-Based Modeling to Study the Adaptation and Evolution of Human Emotions and Cognition. Journal of Psychological Science, 48(6), 1314-1332, 10.16719/j.cnki.1671-6981.20250603.
  • Rabiee, H., Ladani, B.T., & Sahafizadeh, E. (2025). Unmasking Influence: The Power of Artificial Entities in Shaping Public Opinion on Social Networks. IEEE Transaction on Computational Social Systems. DOI: 10.1109/TCSS.2025.3631707.
  • Radisch, F. (2025). Accountability and Educational Improvement Arnoud Oude Groote Beverborg Tobias Feldhoff Katharina Maag Merki.
  • Rahimifar, A., Kaniyamattam, K., Wiegert, J.G., Tedeschi, L.O. (2025). ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Developing an agent-based model to calculate growth performance and predict nutrient requirements of growing-finishing pigs toward sustainability. Journal of Animal Science, skaf443. https://doi.org/10.1093/jas/skaf443
  • Rahimifar, A., Wiegert, J., Kaniyamattam, K., Tedeschi, L. O. (2025). An agent-based modeling approach for balancing feed efficiency and environmental footprint of growing-finishing pig production systems. Journal of Animal Science 103, 65-66. https://doi.org/10.1093/jas/skaf300.082
  • Rahmatina, N., Permatasari, A. D., Yaqin, M. A. (2025). Evaluasi Strategi Versioning Produk terhadap Retensi dan Profitabilitas. Pustaka Galeri Mandiri, 5(2), 311-322.
  • Raikar, G.V.S, Raikar, A.S., Gururaj, H.L., et al. (2025). Revolutionizing UTI Treatment: Harnessing Ant Colony Optimization for Effective E. coli Combat in the Urinary Tract. PREPRINT (Version 1)Research Square, https://doi.org/10.21203/rs.3.rs-5905599/v1
  • RAIMBAULT, J. (2025). Co-evolution in Urban Systems. Centralities and Hierarchies of Networks and Territories, 217.
  • Raj, S. M. A., & Preetha, S. (2025). Information Communication Technology as a Catalyst for Strengthening Employee Self-Efficacy and Organizational Citizenship Behaviour. 3rd International Conference on Advances in Computation, Communication and Information Technology. DOI: 10.1109/ICAICCIT68829.2025.11434470.
  • Rakotonarivo, O. S., Bell, A., Dillon, B., Duthie, A. B., Kipchumba, A., Rasolofoson, R. A., ... & Bunnefeld, N. (2021). Experimental evidence on the impact of payments and property rights on forest user decisions. Frontiers in Conservation Science, 2, 661987.
  • Rakotonarivo, O. S., Jones, I. L., Bell, A., Duthie, A. B., Cusack, J., Minderman, J., ... & Bunnefeld, N. (2021). Experimental evidence for conservation conflict interventions: The importance of financial payments, community trust and equity attitudes. People and Nature, 3(1), 162-175.
  • Ramaswamy, A., Kalvas, F., Sanders-Jackson, A., Slater, M.D. (2025). Exploring the Influence of Open-Mindedness and Social Identity on Opinion Polarization in a Modified Hegselmann-Krause Model. Journal of Artificial Societies and Social Simulation, 28(3). 10.18564/jasss.5661
  • Rana, V., & Singh, V. (2025). Designing Agent-Based Models: A Methodological Approach from Practical Insights and Lessons Learned from Case Study.
  • Rands, S. A., Botías, C., Nicholls, E., Hempel de Ibarra, N. (2025). A spatially explicit model of pollinator-plant-pathogen interactions. bioRxiv, https://doi.org/10.1101/2025.03.11.642689
  • Rapallo, F., Scalas, E., & Terna, P. (2025). How many outbreaks before an epidemic? arXiv. https://doi.org/10.48550/arXiv.2511.15736
  • Real Ortega, C. R., Hernández Solís, A., Peña Acuña, F., & Solares-Rojas, A. (2025). Uso de representaciones semióticas mediante la modelización basada en agentes. En A. Solares-Rojas, & A. P. Preciado Babb (Eds.), La investigación en modelización matemática: un diálogo entre educadores de Latinoamérica y España (pp. 231–254). Editorial SOMIDEM. https://doi.org/10.24844/SOMIDEM/S2/2025/01-10
  • Reambonanza, K.C. & Llantos, O.E. (2025). Simulating the Spread of Antimicrobial Resistance in Agricultural Ecosystems Using an Agent-Based Approach with Parallelization. 2025 IEEE Cyber Science and Technology Congress, 479-486, doi: 10.1109/CyberSciTech68397.2025.00071.
  • Reddy Sareddy, M. (2025). Enhancing Smart City Healthcare with Hybrid Swarm Optimization: A Comparison of MFO-PSO and ACO Approaches. IRO Journal on Sustainable Wireless Systems 7(1), 1-18, 10.36548/jsws.2025.1.001.
  • Reintgen Kamphuisen, F. M., Joye, Y., & Bolderdijk, J. W. (2025). An agent-based model of the extinction of experience: How nature availability and connectedness to nature co-evolve over time. Journal of Environmental Psychology, 106, 102629.
  • Renzini, F. (2025). Network formation dynamics in asymmetric coordination games. Rationality and Society, 10434631241309265.
  • Repenning, A. (2025, June). From TikTok to Hard Fun: Progressive Engagement in Computational Thinking through Game Design. In Constructionism Conference Proceedings (Vol. 8, pp. 227-238).
  • Reyes, C., Knight, O., & Stewart, P. (2025). Agent-Based Simulation of Competitive Market Dynamics for Strategic Business Planning. Journal of Innovation in Governance and Business Practices, 1(1), 1-31.
  • Rincón-González, C. E., y Montoya-Arango, J. A. (2025). Resiliencia de la arquitectura regional de bahareque: simulación según un modelo basado en agentes con el software NetLogo. Revista de Arquitectura (Bogotá), 27(2), 15-37.
  • Rini, N.K., Deliana, Y., Wibowo, K. A., Trimo, L., Setiawan, I., Sukmawani, R., Widianto, S., Wulandari, E. (2025). Simulating value co-creation in digital agri-food platforms: An agent-based model of consumer–producer interaction in the organic vegetable market. Edelweiss Applied Science and Technology. Learning Gate, vol. 9(6), pages 2553-2567. doi: 10.55214/25768484.v9i6.8432
  • Rodeles Lara, I.G., Velásquez, L.M.G., & Campas, M.R.Z. (2025). Impact of Transparency on the Legitimacy and Effectiveness of Public Policies during COVID-19. Int. J. Ad. Social Sciences, 13(4), 173-176. 10.52711/2454-2679.2025.00026
  • Rodrigues Pedroso, G. (2025). Explorando Modelos de Propagação de Sinal em Aplicações Críticas de Comando e Controle para Redes Militares Táticas. [Bachelor's Thesis, UFRGS].
  • Rodríguez Díez, G. (2025). Sistema de transporte adaptado a la demanda con gamificación y simulación multiagente.
  • Rodriguez-Falcon, S., & Stucchi, L. (2025). Quantifying Emergent Behaviors in Agent-Based Models using Mean Information Gain. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2510.10381
  • Rodriguez-Lucatero, C. (2025). Simulation of Virus Propagation in Complex Networks Using NetLogo Multiagent Testbed. Complex Systems with Artificial Intelligence - Sustainability and Self-Constitution: Sustainability and Self-Constitution, 33-54, http://dx.doi.org/10.5772/intechnopen.1007441
  • Rodríguez, A., Gaitán-Angulo, M., Gómez-Caicedo, M. I., Robayo-Acuña, P., & Ruíz-Castro, I. R. (2025). Innovameter: Agent-based modeling of innovation determinants in American and European countries. PloS one, 20(1), e0313756.
  • Roelofsen, R. (2025). The Influence of ACT-R’s Declarative Memory System on the HUMAT Agent-Based Model (Doctoral dissertation).
  • Roos, M. (2025). The complexity of problem-solving human social systems: Structural vs dynamic complexity. PLOS Complex Systems 2(7): e0000055. https://doi.org/10.1371/journal.pcsy.0000055
  • Roukounis, C. N., Chondros, M., Tsoukala, V. K., & Tsihrintzis, V. A. (2025). Agent-Based Modeling Under Climate Uncertainties: The Coastal Resilience (CRes-ABM) Model. Journal of Geovisualization and Spatial Analysis, 9(1), 11.
  • Ruan, W., Liu, J., Tang, H., & Liu, C. A Multi-Stage Modeling Framework for Pedestrian Exit Choice: Decision-Node Identification and Cross-Scenario Validation. Chang, A Multi-Stage Modeling Framework for Pedestrian Exit Choice: Decision-Node Identification and Cross-Scenario Validation.
  • Ruesink, B. (2025). Addressing Resource Constraints: Multi-Method Insights on Rural Development and Refugee Integration in Sub-Saharan Africa [Doctoral dissertation, Gottfried Wilhelm Leibniz Universität Hannover]. Gottfried Wilhelm Leibniz Universität Hannover. https://repo.uni-hannover.de/items/3b7b44ff-5485-4545-9251-ad1f7999cb10
  • Ruesink, B., & Gronau, S. (2025). Forest resource management, refugee integration, and food security in rural Zambia: balancing sustainability and equity. Food Security, 1-27.
  • Rugaber, S., Bunin, S., Hornback, A., Sungeun, A., Goel, A. (2025). Learning Ecology with VERA Using Conceptual Models and Simulations. arXiv:2510.16944v1
  • Ruíz, N.D.P. (2025). Spatiotemporal Modeling for Wastewater Surveillance and Epidemiology [Doctoral dissertation, University of Twente]. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/1.9789036567527
  • Saavedra-Robinson, L.A., Salazar-Serna, K., Cadavid L. (2025).Safety performance: Effectiveness of preventive strategies in construction projects. Ingeniería y Competitividad 27(3)e-20915259. https://doi.org/10.25100/iyc.v27i3.15259
  • Saba, J., Kapur, M., & Roll, I. (2025). Learning about multivariable causality with interactive simulations: exploration before instruction may hurt immediate gains but benefits transfer. Instructional Science 54, 1603-1632.
  • Sabutkevich, A. M., Vikhlyaev, D. A., Kuboy, N. A., & Nikiforov, I. V. (2025, July). of Multi-agent System Behavior Using Simulation System. In Digital and Information Technologies in Economics and Management: Proceedings of the International Scientific and Practical Conference" Digital and Information Technologies in Economics and Management"(DITEM2024) (Vol. 1422, p. 55). Springer Nature.
  • Sadeghi Foroushani, S. (2025). Harnessing sustainability mobile apps for carbon reduction: a multilevel approach (Doctoral dissertation, University of British Columbia).
  • Sáez, P., Rivera, J., Salas, P., & Parada, V. (2025). Rescheduling Under Disruption: A Product-Driven Framework with Heuristic and Reinforcement Learning Strategies. Management and Production Engineering Review.
  • Sakata, A., Kikuchi, T., Kunigami, M., & Terano, T. (2025). A Framework to Support Upgrading Gaming Simulations in Response to Feedback from the External Environment. IEEE/ACIS 23rd International Conference on Software Engineering Research, Management and Applications (SERA), Las Vegas, NV, USA. pp. 456-463, doi: 10.1109/SERA65747.2025.11154581.
  • Salas-Peña, A., & García-Palomares, J. C. (2025). Simulating Co-Evolution and Knowledge Transfer in Logistics Clusters Using a Multi-Agent-Based Approach. ISPRS International Journal of Geo-Information 14(4), 179. https://doi.org/10.3390/ijgi14040179
  • Salazar Serna, K. G. (2025). Evaluación de Políticas para el Sector de Motocicletas. [Doctoral Thesis, Universidad Nacional de Colombia].
  • Saleen, K., Saleem, M., Almogren, A., Almogren, A., Kaur, U., Bharany, S., & Rehman, A. U. (2025). Multi-Agent Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations. IEEE Access 13, 36162 - 36174, DOI: 10.1109/ACCESS.2025.3544096.
  • Sani Iyal, A., Afarasimu, A. (2025). Improved Dijkstra Indoor Path Planning for Scalable Multi-Robot with Time-Stepped Coordination. UMYU Scientifica. https://publications.umyu.edu.ng/scientifica/index.php/usci/article/download/361/302/1055?inline=1
  • Sardarzehi, M., Sajadi, S. M., Esmaelnezhad, D., Taghizadeh-Yazdi, M., & Nazari-Shirkouhi, S. (2025). A novel integrated Six Sigma and simulation approach for reducing waiting time in emergency departments. Journal of Simulation, 1–24. https://doi.org/10.1080/17477778.2025.2603493
  • Sargent, R., Rakotonarivo, O. S., Rushton, S. P., Cascio, B., Grau, A., Bell, A. R., ... & Pfeifer, M. (2022). An experimental game to examine pastoralists' preferences for human–lion coexistence strategies. People and Nature, 4(5), 1263-1278.
  • Sartor, G., Wyner, A., Contissa, G. (2025). Mind the Gaps: Logical English, Prolog, and Multi-agent Systems for Autonomous Vehicles. 40th International Conference on Logic Programming (ICLP 2024) EPTCS 416, 2025, pp. 111–124, https://doi.org/10.48550/arXiv.2502.09216
  • Sayfullayevich, M.A. (2025). Modeling Urban Migration Patterns Using Social and Economic Indicators. American Journal of Alternative Education 2(6).
  • Scharf, I. (2025). Active predators do not necessarily specialize in sedentary prey: A simulation model. Ann NY Acad Sci., 1549, 199–207.
  • Scheffel, E. J. S., Schneider, D., & Motta, C. L. R. (2025). Collaborative Model for Developing Computing Skills in Basic Education. In B. du Boulay, E. Tovar, T. Di Mascio, & C. Meinel (Eds.), Proceedings of the 17th International Conference on Computer Supported Education 2, (pp. 942-949). DOI: 10.5220/0013439500003932
  • Schiavo, M., Aubert, P.M., Le Mouël, C. (2025). The Impact of Agroecological Transition on the Meat Industry: An Agent-Based Modelling Approach Applied to the French Livestock Sector. Journal of Agricultural Economics 76(2).
  • Schlüter, M., Hertz, T., Klein, A., & Wijermans, N. Disentangling the entangled in productive ways: modelling social-ecological systems from a process-relational perspective. Sustainability Science 20, 793-815. https://doi.org/10.1007/s11625-025-01648-0
  • Schneider, J.S., Reutemann, A.V., Sassone, A.B. et al. An interactive heuristic model to test ecological and evolutionary hypotheses on incipient polyploid species. Sci Rep (2025). https://doi.org/10.1038/s41598-025-29286-7.
  • Schnelle, C. K. (2025). AGENT-BASED SIMULATION OF ENERGY POVERTY: INSIGHTS INTO ENERGY JUSTICE AND EQUITY SOLUTIONS.
  • Schöppl, K. (2025). Industry Influencing Collective Scientific Reasoning: A Bayesian, Agent-based Exploration. In Barner, D., Bramley, N.R., Ruggeri, A., & Walker, C.M. (Eds), Proceedings of the Annual Meeting of the Cognitive Science Society, 47, 1591 - 1597.
  • Schranz, M., Elmenreich, W., & Arvin, F. (2025). Simulating swarms of cyber-physical systems. Engineering Swarms of Cyber-Physical Systems, 67–83.
  • Schubert, D. (2025). Modelling segregation based on surveyed neighbourhood preferences. Review of Regional Research, 1-21.
  • Schubotz, L., Chappin, E., & Scholz, G. (2025). Structural Sculpting: Making Inverse Modelling Generate and Deal with Variable Structures. Journal of Artificial Societies and Social Simulation 28 (4). 10.18564/jasss.5830
  • Schulz, A. W. (2025). Intraspecific economics: interpersonal utility comparisons, evolution and culture. Behavioural Public Policy, 1-22.
  • Schwarz, N., Augustijn, P.W.M., Cray, L., & Dou, Y. (2025). Teaching agent-based modelling online – lessons learnt. Poster session presented at Social Simulation Conference 2025, Delft, Netherlands.
  • Segundo, C., Oliveira, M., & Gonçalves, E. (2025). An Agent-Based Simulation for Task Allocation in Software Development Teams based on the Truck Factor Metric. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14(e32600). https://doi.org/10.14201/adcaij.32600
  • Seniuk, I., Kravchenko, V., Honcharov, O., Galuzinska, L., Matviichuk, O. (2025). Integration of interactive methods into the educational process for biological educational components in the conditions of online-learning in pharmaceutical higher education institutions. ScienceRise: Pedagogical Education, 4 (65), 64–75. http://doi.org/10.15587/2519-4984.2025.349435
  • Sepanosian, T., & Bemthuis, R. (2025). IoT-Enabled Multi-Agent Simulation for Hazard Detection and Safety in Construction. Procedia Computer Science, 257, 354-363. https://doi.org/10.1016/j.procs.2025.03.047
  • Serdyuk A. (2025). Agent-based simulating of Ukrainian universities adaptation to a full-scale Russian invasion. Communities and Collections, pp. 68-73. https://ekmair.ukma.edu.ua/handle/123456789/37650
  • Serdyuk, A. (2025). Policy Responses and Organizational Adaptation of Ukrainian Universities During the Full-Scale Russian Invasion: An Agent-based Approach. International Scientific and Practical Conference “Interdisciplinary Expertise for the Recovery and Development of Ukraine.
  • Serdyuk, A. (2025). Validation Strategies For Agent-Based Models In Social Science: Insights from Modeling of Migration Caused by the Full-Scale Russian Invasion of Ukraine. National University of Kyiv-Mohyla Academy.
  • Sert, M. F., Kartal, B., Çakıroğlu, K. I., & Öztürk, A. (2025). Predictive modelling of future tea consumption: Analysing consumer behaviour with combining the machine learning and agent-based simulation approaches. Journal of the Operational Research Society, 1–16.
  • Seth, R., Delatouche, L., Sester, M., Drogoul, A., & Cheab, S. (2025). Modelling insect interactions in rice crops on the GAMA platform as a basis for educational VR games. Proceedings of the ASEAN Conference on Emerging Technology, pp.103-113. https://www.idri.edu.kh/wp-content/uploads/2026/01/S6.pdf
  • Setiawan, E., Natang, R.H., Anggrianita, W. (2025). An Agent-Based Simulation Model to Analyze the Impact of Land Use Change, Climate Change, and Pest Attacks on the Decline of Rice Production in Central Java. Digital Nexus Systematic Journal, vol. 1, no. 2. doi: 10.26753/dns.v1i2.1552.
  • Shamsi Gamchi, N., Esmaeili, M., & Afshar Sedigh, A. H. (2025). Mitigating direct and indirect costs of unexpected pandemics: a parallel vaccination strategy utilizing agent-based simulation. Journal of Simulation, 1–22. https://doi.org/10.1080/17477778.2025.2600537
  • Shan, S., Wang, Y., Hao, J., Guo, R., & Zhang, Y. (2025) The Relationship Between Individual Willingness to Collaborate and the Performance of Collaborative Public Crisis Governance: An Agent Based Model. MDPI Mathematics 13(10).
  • Sharbini, H., Chan, C.P., Mat, A.R., Borhan, N.H., Talib, M.S., Mustaffa, N.H. (2025). Agent-based simulation with awareness levels of the emergency evacuation of educational facilities. Edelweiss Applied Science and Technology, Learning Gate, vol. 9(7), pages 2063-2077.
  • Shen, Y., & Yang, Q. (2025). A mathematical model for biological motor learning based on synaptic dynamics. Chaos, Solitons & Fractals, 191, 115839.
  • Shin, B., Cockrell, R., & An, G. (2025). The Burn Agent-Based Model (BABM): Developing a Computational Agent-based Model of the Burn Wound Healing Process to Examine Fibrosis and Re-epithelialization After a Partial-thickness Burn Injury.
  • Shoger, J. L. (2025). From Analogue to Digital Command and Control Decision-Making: The Law of Requisite Variety and Fuzzy Methods in Quantifying Situation Assessment (Doctoral dissertation, State University of New York at Binghamton).
  • Sibala, L.S.B., Oguis, G.F.R., & Rivas, N.A.B. (2025). Assessing the Dynamics of the Coffee Value Chain in Davao del Sur: An Agent-Based Modeling Approach. arXiv. https://doi.org/10.48550/arXiv.2505.05797.
  • Silalahi, S. A., Pujawan, I. N., & Singgih, M. L. (2025). Agent-based simulation of digital interoperability thresholds in fragmented air cargo systems: Evidence from a developing country. Logistics, 9(4), 160.
  • Silva de Carvalho, L.F.B., de Souza, V.S., Bonatto, A.N., Perez, T., Baronea, D.A.C., Wickboldt, J.A. (2025). Multi-agent systems modeling of command and control systems: a metrics-driven approach to simulator evaluation and co-simulation. Journal of Simulation, (pp. 1–19). https://doi.org/10.1080/17477778.2025.2584542
  • Simpkins, C. E., Bellingham, P. J., Reihana, K., Brock, J. M., & Perry, G. L. (2025). Evaluating the effects of two newly emerging plant pathogens on northern Aotearoa-New Zealand forests using an individual-based model. Ecological Modelling, 500, 110938.
  • SINGH, N. K., THAKUR, S. S., & KHULLAR, S. (2025). EMOTIONAL CONTAGION MODELLING IN PROTEST MOVEMENTS USING MIXED METHODS. TPM – Testing, Psychometrics, Methodology in Applied Psychology, 32(S4), 930–935. Retrieved from https://tpmap.org/submission/index.php/tpm/article/view/641
  • Singh, S.P. (2025). Smart Contracts in Agri-Finance Supply Chains. Scientific Journal of Artificial Intelligence and Blockchain Technologies, 2(2), 9-22. https://doi.org/10.63345/sjaibt.v2.i2.102
  • Skach, C. (2025). Constitutional Frameworks and Democratic Consolidation Revisited: A Computational Analysis. SSRN. http://dx.doi.org/10.2139/ssrn.5735922
  • Skevas, T., & Thompson, W. (2025). Farm policies and their impact on sector composition and risk aversion. Journal of Agricultural Economics 77(1), (pp. 171-182). https://doi.org/10.1111/1477-9552.70014
  • Snyder, B. (2025). Sexual Selection Creates Status-Seeking Males and Unsustainable Economic Growth. Evolutionary Psychological Science 11, 367–380. https://doi.org/10.1007/s40806-025-00448-0
  • Soares, P. H. I., da Silva, M. A. S., Dompieri, M. H. G., de Moura, F. R., Batista, N. C. S., Medeiros, S. d. S. (2025). An Agent-Based Model Integrating the Theory of Planned Behavior for Simulating the Effect of Public Policies on Agricultural Diversity. Encontro Nacional de Inteligencia Arificial e Computacional. https://doi.org/10.5753/eniac.2025.14420.
  • Socci, V., Vitanza, E., & Mocenni, C. (2025). An agent-based model to foster citizens’ sustainable behavior in the Italian city of Siena. The European Physical Journal B, 98(4), 72.
  • Sohn, D. (2025). When Opinions Polarize Without Persuasion: Modeling the Dynamics of Attitude-Opinion Convergence and Decoupling. Communication Research, 00936502251362278.
  • Sommer, I., Faggian, R., Sposito, V., & Dieguez Cameroni, F. (2025). Agent-based modelling applied to nitrogen and phosphorus assessment in agroecosystems: A case study of a Uruguayan dairy farm. Agrociencia Uruguay 29.
  • Sosa, B., Zellner, M., Chiale, C., & Achkar, M. (2025). Explaining woody invasions in riparian systems with agent-based simulations: Implications for conservation management. Forest Ecology and Management, 576, 122363.
  • Souto-Maior, J.M. (2025). School racial composition and the emergence of Black-White within-school inequalities: network-based foundations. The Journal of Mathematical Sociology, 50(1), 1-42. https://doi.org/10.1080/0022250X.2025.2458304
  • Souza, C. F. D. (2025). Ferramenta facilita o aprendizado de ciências para pessoas com deficiência visual. Jornal da Universidade, 15 maio 2025, n. 236.
  • Stein, J., Romeijn, J.W., & Mäs, M. (2025). Ill-informed Consensus or Truthful Disagreement? How Argumentation Styles and Preference Perceptions Affect Deliberation Outcomes in Groups with Conflicting Stakes. Erkenntnis, 91, 743-768. https://doi.org/10.1007/s10670-024-00913-5
  • Stella, F., Sapienza, A., & Falcone, R. (2025). The Roles of Autonomy and Trust in Hybrid Societies: a simulation-based study in the healthcare domain. 2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). DOI: 10.1109/DCOSS-IoT65416.2025.00125.
  • Stephan, J., Fritzsch, S., & Weiss, G. N. (2025). Riding the Crypto Rollercoaster: Agent-Based Insights into Bitcoin’s Price Dynamics.
  • Stockwell, J. D., Chapina, R. J., & Dur, G. (2025). An individual-based model to evaluate the potential contribution of benthic resources to growth and production of a vertically migrating omnivorous macroinvertebrate. Canadian Journal of Fisheries and Aquatic Sciences, 82.
  • Strombom, N. A. (2025). Family and medical leave act and childhood secure attachment: Are we doing enough to ensure children establish good patterns for lifelong health?(Order No. 32401534). ProQuest Dissertations & Theses Global. (3284363060). https://www.proquest.com/dissertations-theses/family-medical-leave-act-childhood-secure/docview/3284363060/se-2
  • Stufano Melone, M. R., & Camarda, D. (2025). Risk as a wicked problem in planning: the role of future non-knowledge. TeMA - Journal of Land Use, Mobility and Environment, 18(3), 457–473. https://doi.org/10.6093/1970-9870/11883
  • Stukey, R., Melagoda, A., Shanthanam, S. & Aravinthan, V. (2025). Modeling Electric Vehicle Charging Load on Power Grid Considering Travel Behavior. 2025 IEEE Green Technologies Conference. https://doi.org/10.1109/GreenTech62170.2025.10977706
  • Su, L., Kong, D., Zhu, K., & Han, J. (2025). Classroom Behavior Analysis from the Perspective of Complexity Science: Functional Logic and Possibility Paths. Modern Educational Technology 35(8), 107-115. DOI: 10.3969/j.issn.1009-8097.2025.08.011
  • Su, M., Chi, M.T.H., & Nagashima, T. (2025). Applying the PAIR-C framework to foster deep understanding and address misconceptions in science education. Journal of the Learning Sciences. https://doi.org/10.1080/10508406.2025.2546471
  • Sun, H., Shao, C., Zhang, J., Wang, K., & Huang, W. (2025). Evolution Analysis of Network Attack and Defense Situation Based on Game Theory. Computers, Materials & Continua, 83(1).
  • Sun, Y., Wang, J., Li, J., Lei, X., Wang, W., Jing, T., & Bai, J. (2025). A Baseline Method for Multi-Aircraft Air Combat Situation Assessment Based on Weighted Average. Proceedings of the 2025 International Symposium on Artificial Intelligence and Computational Social Sciences, 151-157.
  • Susco, A. (2025). Enhancing Efficiency in Last-Mile Food Delivery: Analysis of innovative and alternative delivery systems through Mixed-Methods and Simulation. Università degli Studi di Torino. https://hdl.handle.net/2318/2059754
  • Swannack, T.M., Cushway, K.C., Carrillo, C.C., Calvo, C., Determan, K.R., Mierzejewski, C.M., Quintana, V.M., Riggins, C.L., Sams, M.D., Wadsworth, W.E. (2025). Cracking the code: Linking good modeling and coding practices for new ecological modelers. Ecological Modelling 499. https://doi.org/10.1016/j.ecolmodel.2024.110926
  • Sybertz, A., Ottermanns, R., & Roß-Nickoll, M. (2025). MIXCOP - Implementation of a model to assess the time-dependent effects of mixed pesticides on a collembolan population. MethodsX 14.
  • Sznajd-Weron, K., & Kamińska, B. (2025). Emergence of Pluralistic Ignorance: An Agent-Based Approach. In WORking papers in Management Science. Department of Computational Social Science, Wroclaw University of Science and Technology. https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_25_08.pdf.
  • Tagliati, A., Hazlerigg, C. R. E., Salinas, E. R., Lagadic, L., & Preuss, T. G. (2025). Assessment of Population Relevance of Endocrine-sensitive Apical Endpoints in Fish Chronic Studies Using Individual-Based Models. Environmental Science and Technology 59, 21431-21441, https://doi.org/10.1021/acs.est.5c03364
  • Taillandier, P., Zucker, J.D., Grignard, A., Gaudou, B., Nghi, H.Q., Drogoul, A. (2025). Integrating LLM in Agent-Based Social Simulation: Opportunities and Challenges. Cornell University. https://doi.org/10.48550/arXiv.2507.19364.
  • Talavera, S. E. (2025). Aplicación de modelos multiagentes para la simulación de incendios forestales en las Islas Canarias. Universidad de La Laguna. file:///Users/ccl_1/Downloads/Aplicacion%20de%20modelos%20multiagentes%20para%20la%20simulacion%20de%20incendios%20forestales%20en%20las%20Islas%20Canarias.pdf.
  • Talid, M., Rubaie, E., & Talid, Z. (2025). A Proposed Spectral Clustering Method for Wireless Sensor Networks. National Journal of Antennas and Propagation, Vol. 7, No. 2.
  • Tang, X., Lira, M., Lindgren, R. (2025). Haptic Experiences Shift Students' Representational Gestures and Knowledge Resources. Journal of Research in Science Teaching, p. 1-23. https://doi.org/10.1002/tea.70033
  • Tarantino R, Garcia-Gonzalez F (2025) Agent-based modelling reveals feedback loops and non-linearity between mating system evolution and disease dynamics. PLOS ONE 20(12): e0336020. https://doi.org/10.1371/journal.pone.0336020
  • Tarantino, R. (2025). The Polysemy of Genes: Analogical and Agent-Based Models Investigation of Phenotypic Plasticity. [Doctoral Thesis, Università degli Studi di Palermo]. https://hdl.handle.net/20.500.14242/188350
  • Teixeira, S., Campos, P. (2025). Strategic Alliances in NetLogo: A Flocking Algorithm with Reinforcement Learning. In: Campos, P., Rao, A., Margarido, J. (eds) Machine Learning Perspectives of Agent-Based Models. Springer, 287-306.
  • Tejeda, H., Espinilla, M., & Malakooti, B. (2025). Integrating Artificial And Computational Models For Intelligent Decision-Making Across Domain. ResearchGate.
  • Thelin, M., & Helgesson, M. (2025). Comparative Analysis of Discrete-Event Simulation Programs. [Master Thesis, Lund University].
  • Thomas, D., & Helgeson, J. (2025). Supply Chain Disruption and Agent-Based Modeling.
  • Tkach, Y. (2025, April). Agent-Based Modelling of Information. In Mathematical Modeling and Simulation of Systems: Selected Papers of 19th International Conference, MODS, November 11–13, 2024, Chernihiv, Ukraine (Vol. 1391, p. 195). Springer Nature.
  • Toth, Z. (2025). Can foraging in pesticide-treated patches become an ecological trap for pollinators? Modelling visitation patterns on a simulated landscape. bioRxiv, 2025-07.
  • Turgay, S., & Tural, M. (2025). Enhancing Emergency Management with Cognitive Modeling: An Agent-Based Simulation Perspective. Design, Construction, Maintenance 5, 155-171. DOI: 10.37394/232022.2025.5.15
  • Túry-Angyal, E. & Lőrincz, L. (2025). Sharing political news online: A network model of information spread on Facebook. East European Journal of Society and Politics. 11(1), 40–62. DOI:https://doi.org/10.17356/ieejsp.v11i1.1169.
  • Twumwaa Tagoe, T., Megiddo, I., & Van Deer Meer, R. (2025). Improving diabetes treatment outcomes: an agent-based model of patients’ self-management behaviours and treatment outcomes in Ghana. In Luis, M., Harper, A., Monks, T., & Mustafee, N. (Eds), Proceedings of the Operational Research Society Simulation Workshop 2025.
  • Ty, C., Case, A., Mezzulo, E., Penland, A., & Larripa, K. (2025). An Agent-Based Model of Microglia and Neuron Interaction: Implications in Neurodegenerative Disease. Spora: A Journal of Biomathematics, 11(1), 20-35.
  • Ty, C., Penland, A., Gerving, J., Mendoza-Ceja, M., Pratt, M., & Larripa, K. (2025). Investigating Potential Alzheimer's Disease Therapies through an Agent-Based Model of Impaired Microglia MetabolismModeling Impaired Microglia Metabolism. Spora: A Journal of Biomathematics, 11(1), 110-136.
  • Tzioutziou, A., Velasco, M-J. P., Loukogeorgaki, E. (2025). Comparative evaluation of environmental impacts from renewable-energy projects with agent-based modeling. Clean Energy 9(6), 244-255. https://doi.org/10.1093/ce/zkaf053.
  • Umlauft, M. & Schranz, M. (2025). Flocking Behavior: An Innovative Inspiration for the Optimization of Production Plants. arXiv. https://doi.org/10.48550/arXiv.2508.19963
  • Upadhyaya, A. N. (2025). Tools and Technologies. Ethics and AI Integration Into Modern Classrooms, 65.
  • Usul, M. E. (2025). THOVEA: A Three-Layer Visual Analytics System for Information Diffusion over Large Networks (Doctoral dissertation, Technische Universität Wien).
  • Utami, I. D., Novianti, T., Mutjtahid, A. H., Hariyanto, H. H., & Achyar, M. A. N. (2025, June). The design of Cabya (Piper retrofractum Vahl.) distribution to minimize the shipment costs. In AIP Conference Proceedings (Vol. 3234, No. 1, p. 060004). AIP Publishing LLC.
  • Van Den Berg, F. P. W., Noyons, B. C., & Van, M. A. (2025). Safety assessment with an agent based model for levees with beaver burrowing during high-water events (). Twenty-Eighth International Congress on Large Dams, 967–984. https://doi.org/10.1201/9781003642428-41
  • van der Koogh, M. L. (2025). Dynamics of Charging.
  • van Loo, J. (2025). Hosting Military Transits: An Agent-Based Model (ABM) to simulate the effect of simultaneous infrastructure use by civilian and military vehicles, as well as different intervention strategies [Master Thesis, Delft University of Technology]. TU Delft Repository.
  • van Rosmalen, R., Harmanny, K., & Schulp, C. J. (2025). Impact of environmental land lease contract specification on implementation of nature inclusive farming. Open Research Europe, 5, 289.
  • Vannier, C., Choulette, K., Richards, D., Heays, A., Herzig, A., & Lavorel, S. (2025). SMARTscape: An agent-based model to analyse rural landscapes multifunctionality. Environmental Modelling & Software, 106732.
  • Vasconcellos, L.H.R., de Figueiredo, J.C.B., & Ferreira, F.C.M. (2025). So what now, Dr. Jos? How are we going to solve the queues? Understanding queue management in services through multi-agent simulation. GVcasos, 16(1). https://doi.org/10.12660/gvcasos.v16.n1.97823
  • Vasellini, R., Ferrandino, G., Sernicola, L., Vilone, D., & Mocenni, C. (2025). Cooperative behavior in pre-state societies: an agent based approach to the Axum civilization. Frontiers in Complex Systems 3. https://doi.org/10.3389/fcpxs.2025.1590952
  • Vega, R., Mattson, C., Zhu, K., Brown, D.S., & Nowzari, C. (2025). Analytical Swarm Chemistry: Characterization and Analysis of Emergent Swarm Behaviors. arXiv, Cornell University. https://doi.org/10.48550/arXiv.2510.22821
  • Velikovsky, J T. (2025). Big History and Evolutionary Culturology: Enhancing the Eight Thresholds with a Universal Systems Meta-Meta-Science.Journal of Big History, 8(3) (pp. 140–183). https://doi.org/10.22339/jbh.v8i3.8305
  • Vergani, M., Giovannetti, A., Ng, S., Lim, C. P., Zhang, J., & Scott, R. (2025). Synergies between "hard" and "soft" interventions against online extremism: An agent-based simulation and cost-benefit analysis. Terrorism and Political Violence. https://doi.org/10.1080/09546553.2025.2544628
  • Verlaan, T., Birks, D., & van der Mei, R. (2025). The Effect of Police Deployment Strategy on Emergency Response Times: An Agent-based Modelling Investigation. Journal of Quantitative Criminology.
  • Verma, R., Rashid Anwar, S., Mayan, J.A., Jena, A., Allamprabhu, C.Y., Yeasmin, L. (2025). Agent-Based Modeling of Human-Wildlife Conflict in Agricultural Landscapes. NESciences, 10 (3), 119-134. doi: 10.28978/nesciences.1811111
  • Verza, M., Camanzi, L., Mulazzani, L., Giampaolo, A., Rodriguez, S., Malorgio, G., & Mattas, K. (2025). Underutilized crops for diversified agri-food systems: spatial modeling and farmer adoption of buckwheat in Italy. Frontiers in Sustainable Food Systems 9. doi: 10.3389/fsufs.2025.1534246
  • Viani, A. (2025). Beyond Mobility: Modelling Non-Migration Decisions Under Climatic Stress in Southwestern Coastal Bangladesh [Unpublished manuscript]. Utrecht University.
  • Viertel, S. P., Pessini, L., Secomandi, L., Cavion, R., & Staudt, F. H. BOARDING EFFICIENCY IN REGIONAL AVIATION: A COMPARATIVE ANALYSIS OF METHODS.
  • Vijayan, M., Patil, A., & Kapse, V. (2025). Embracing the Complexity of Cities: Agent-Based Modelling for Energy Planning and Policy. In Urban Planning and Design for Megacities in the Global South: Smart and Sustainable Development (pp. 419-439). Singapore: Springer Nature Singapore.
  • Visser, R. M., Lambers, K., Aitchison, K., Jutte, A., Rocks-Macqueen, D., van der Knaap, L., et al.(2025). Lessons Learned: Creating Tutorials to Teach Agent-Based Modelling to Archaeologists. Journal of Computer Applications in Archaeology, 8(1), 354–367. https://doi.org/10.5334/jcaa.241
  • Vogelstein, L., Wagh, A., Champion, D. (2025). Towards Expansive and Equitable Modeling: Syncretic Modeling through the Lenses of Dance, Science, and computation. International Society of the Learning Societies. https://repository.isls.org/bitstream/1/11293/1/ICLS2025_1449-1453.pdf.
  • Vriens, E., Andrighetto, G., & Wijermans, N. (2025). Embracing differences: What group composition reveals about the dynamics of collective action. SSRN. http://dx.doi.org/10.2139/ssrn.5743705
  • Vulic, J., Jacobsen, M., & Levin, J.A. (2025). Computational Educational Research: An Exploration of Instructional Frameworks. Paper presented at the 2025 annual meeting of the American Educational Research Association. Retrieved December 2025 from the AERA Online Paper Repository.
  • W. Yi, J. Cao, Z. Zhou, B. Niu, W. You and H. Li, "Dynamic Trust-Based Secure Node Selection in 5G/6G Federated Learning," 2025 11th IEEE International Conference on Privacy Computing and Data Security (PCDS), Hakodate, Japan, 2025, pp. 01-08, doi: 10.1109/PCDS65695.2025.00024.
  • Wagh, A., Rosenbaum, L. F., Eloy, A., Fuhrmann, T., Henrique, B., Blikstein, P., & Wilkerson, M. H. (2025). Designing for Discourse: Enacting Responsive Teaching through Code Reflections in a Computational Modeling Unit. In Proceedings of the 19th International Conference of the Learning Sciences-ICLS 2025, pp. 377-385. International Society of the Learning Sciences.
  • Wagh, A., Wilkerson, M. (2025). Programming Still Matters, but How?: Reflections about Programming for K-12 Math & Science Learning in the Age of AI. J. ACM 37, 4, Article 111.
  • Wang, A., Zheng, W., Tan, Z., Han, M., & Chan, E. H. W. (2025). Synergies and trade-offs in achieving sustainable targets of urban renewal: A decision-making support framework. Environment and Planning B: Urban Analytics and City Science 52(2), 490-508. https://doi.org/10.1177/23998083241261750
  • Wang, H., Banerjee, P., Ragland, C. J., Grant, W. E., Peterson, T. R. (2025). An agent-based model facilitating stakeholder engagement in management of estuaries along the Texas Gulf Coast (USA). MethodsX 14. https://doi.org/10.1016/j.mex.2024.103121
  • Wang, H.H., Grant, W.E., Donaldson, T.G., Thomas, D.B., Lohmeyer, K.H., Pérez de León, A.A., & Teel, P.D. (2025). Leveraging location intelligence and individual-based modeling to simulate Rhipicephalus microplus infestation and eradication dynamics at the cattle-wildlife interface. Current Research in Parasitology & Vector-Borne Diseases, 8(100323). https://doi.org/10.1016/j.crpvbd.2025.100323
  • Wang, J., Li, J., Wu, J., Tang, Y., Zhang, Z., Fu, Y., & Wang, X. (2025). Mechanical modeling of biofilm morphology variation induced by changes in environments and internal stress. International Biodeterioration & Biodegradation, 198, 105989.
  • Wang, K. Y., & Yu, J. (2026). Agent-based policy analysis: Youth housing affordability and urban market stability. Cities, 170(106629). https://doi.org/10.1016/j.cities.2025.106629
  • Wang, K., Wang, T. & Li, X. Topology optimization of cooling channels based on swarm intelligence. Struct Multidisc Optim 68, 140 (2025). https://doi.org/10.1007/s00158-025-04076-x
  • Wang, R., Feng, Y., Tang, P., Li, P., Dong, Y., Xu, Y, Wang, C., Liu, S., Jin, Y., Liu, S., & Tong, X. (2025). A Multiagent Decision-Making Model for Martian Landing Site Selection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 18.
  • Wang, R., Zhang. J., & Zhao, C. (2025). From Photovoltaics to Resilience: Agent-Based Modeling of Household PV-ESS Adoption. 2nd International Seminar on Artificial Intelligence, Computer Technology and Control Engineering. https://doi.org/10.1109/ACTCE66599.2025.00042
  • Wang, S., & Wang, Z. (2025). Modelling and algorithms of highway transportation network in urban agglomerations. Scientific Reports, 15(1), 28145.
  • Wang, W., & Liu, Z. (2025). Design and Implementation of Network Intrusion Detection System (NIDS) Based on Deep Learning. Security and Privacy 8(6). https://doi.org/10.1002/spy2.70133
  • Wang, X., Wu, Z., & Liu, K. (2025). Evolutionary dynamics of knowledge sharing on online platforms under different Charging models. SSRN. http://dx.doi.org/10.2139/ssrn.5558055
  • Wang, Y., Ge, J., & Comber, A. (2025). Modelling emergent pedestrian evacuation behaviors from intelligent, game-playing agents. Journal of Computational Social Sciences 8(49).
  • Wang, Y., Mahmood, A., Sabri, M.F.M., & Zen, H. (2025). Trust Management in the Internet of Vehicles: A Survey of the State-of-the-Art. TechRxiv. 10.36227/techrxiv.175977259.92821772/v1
  • Waterson, P., Baber, C., Hunt, E., Milivojevic, S., Maynard, S., & Musolesi, M. (2025, April 28-30). Function Allocation for Responsible Artificial Intelligence: How do we allocate trust and responsibility? [Paper presentation]. CIEHF, Trent, UK.
  • Watts, F. M., Liu, L., Ober, T. M., Song, Y., Jusino-Del Valle, E., Zhai, X., Wang, Y., & Liu, N. (2025). A Framework for Designing an AI Chatbot to Support Scientific Argumentation. Education Sciences, 15(11), 1507. https://doi.org/10.3390/educsci15111507
  • Weerasinghe, O., Hewage, K., Razi, F., Fatmi, M., & Sadiq, R. (2025). Applicability of Land-Use Models for Sustainable Urban Planning: A Review. Journal of Urban Planning and Development, 151(3), 04025037.
  • Weerasinghe, W.A.R.N. (2025). An integrated assessment of climate change impacts on Asian honey bee behavior, crop yield contribution, and farmer livelihoods with a focus on beekeeping for agricultural sustainability. Swedish University of Agricultural Sciences.
  • Wei, L., Shang, L., Zhang, M., Li, H., Zhu, X. Evaluation and optimization of resource matching for perception services in power communication networks. Sci Rep (2025). https://doi.org/10.1038/s41598-025-31776-7
  • Weraduwa, S., Kannangara, M., Gayantha, N., Piyumantha, K., & Rajapaksa, C. (2025). An Agent-Based Model to Address the Gap in Internship Opportunities for IT Undergraduates in Sri Lanka. 5th International Conference on Advanced Research in Computing (ICARC), pp. 1-6, doi:10.1109/ICARC64760.2025.10963178.
  • Westmattelmann, D., Sprenger, M., Lanfer, J., Stoffers, B., & Petróczi, A. (2025). The impact of sample retention and further analysis on doping behavior and detection: Evidence from agent-based simulations. Frontiers in Sports and Active Living, 7.
  • Will, M., Jager, F., & Muller, B. (2025). Farmer decision-making on agri-environmental schemes: An agent-based modelling approach to evaluate different policy designs in Saxony, Germany. Agricultural Systems 229. https://doi.org/10.1016/j.agsy.2025.104439.
  • William, P., Kuppusamy, S., Samundeeswari, A., Vijayabharathi, R., Hajiyeva, R. J., Thangavel, K., & Alagarsamy, M. (2025). Integrating IoT and Machine Learning for Real-Time Monitoring of Infectious Waste Management. In Hospital Waste Management and Toxicity Evaluation (pp. 299-320). IGI Global Scientific Publishing.
  • Williams, D. L. (2025). Multi-Agent Communication Protocol in Collaborative Problem Solving: A Design Science Approach. [Masters Thesis, Uppsala University]. Diva Portal: 1970755.
  • Williams, N. F. (2025). Causes and Implications of Fox Population Dynamics in Central Southern England. [Doctoral Thesis, Bournemouth University]. Bournemouth University Research Online.
  • Wilmes, L., & Jamshidnejad, A. (2025). GT-BDI Model: A Combined Game-Theoretic and BDI-Based Computational Model for Emergency Evacuation With Search and Rescue Robots. IEEE Access 13. DOI: 10.1109/ACCESS.2025.3555870
  • Wogatai, K., Sinanović, E., & Elmenreich, W. (2025). Applying a slime mold-inspired algorithm to network planning challenges in the Carinthian region. Carinthia II Part 3 - Carinthia Nature Tech, 2(2). https://doi.org/10.71911/cii-p3-nt-2025222
  • Wu, W., Zhang, Y., & Liu, H. (2025). Analyzing Crowd Emotional Contagion in Metro Emergencies Through the Lens of the Weber-Fechner Law: Predictions Based on Computational Techniques Applied to Science. Applied Science 15(3). https://doi.org/10.3390/app15031244
  • Wu, Y., Zhang, F., Chan, A. P. C., & Li, D. (2025). Sentiment Evolution of Online Public Opinion of Emergency Situations in Railway Stations: A Case Study of Wuhan Railway Stations. Sustainability 17(2). https://doi.org/10.3390/su17020613
  • Xiang, L., Keck, J. W., Gallimore, J., Sakhaei, A., Loh, E., & Berry, S. M. (2025). Wastewater Infrastructure as a Public Health Tool: Agent-Based Modeling of Surveillance Strategies in a COVID-19 Context. Systems, 13(12), 1093.
  • Xiaoxing Yu (2025). An Agent-based Approach to the Gravity Model of Linguistic Diffusion, SIAM Undergraduate Research Online.
  • Yang, R., Wang, J., Zhou, S., & Wang, A. (2025). From individual intention to network diffusion: investigating the spread of green travel behavior intention within a scale-free network based on TPB. Journal of the Operational Research Society. DOI: 10.1080/01605682.2025.2566324.
  • Yang, X., Lv, W., Yang, T., & Baesens, B. (2025). Collaborative governance of cyber violence: A two-phase, multi-scenario four-party evolutionary game and SBI1 I2R public opinion dissemination. Information Processing & Management, 62(6), 104242.
  • Yang, Y., Xu, X., Luo, Y., Liu, y., Xiang, Y., Hu, W., & Liu, J. (2025). Enhancing Resilience of Urban Distribution Networks: Integrating Agent-Based V2G Response Model for Extreme Event Restoration. IEEE Transactions on Transportation Electrification 11(5).
  • Yao, J., & Liu, Y. (2025). An assisted teaching simulation experiment of swarm intelligence algorithm based on NetLogo. In Proceedings of the 2025 2nd International Symposium on Artificial Intelligence for Education, 241-248.
  • Yaqi, L., Guisheng, H., Xiujuan, J. (2025). Multi-agent simulation of team stability evolution: A complexity science perspective. Journal of Infometrics 19(2), https://doi.org/10.1016/j.joi.2025.101655
  • Yin, S., Zhang, C., Teitelbaum, C. S., Si, Y., Zhang, G., Wang, X., Mao, D., Huang, Z. Y. X., de Boer, W. F., Takekawa, J., Prosser, D. J., & Xiao, X. (2025). Landscape changes elevate the risk of avian influenza virus diversification and emergence in the East Asian–Australasian Flyway. In A. Hastings (Ed.), Proceedings of the National Academy of Sciences 122(34). https://doi.org/10.1073/pnas.2503427122
  • Yousef, K., Yuce, B., & He, N. (2025). Agent-Based Modeling of Renewable Energy Management in the UAE. Energies 18(24). https://doi.org/10.3390/en18246494
  • Yousef, Khaled and Yuce, Baris and He, Naihui, Renewable Energy Strategic Planning and Management: An Agent-Based Modeling and Simulation. Available at SSRN: https://ssrn.com/abstract=5279357
  • Youssefi, K.A., Gojkovic, M., & Schranz, M. (2025). Enhancing Job-Shop Scheduling Performance with a Refined Bottom-Up Variant of the Artificial Bee Colony Algorithm. In G. Wagner, F. Werner, & F.D. Rango (Eds.), Simulation and Modeling Methodologies, Technologies and Applications: 14th International Conference, SIMULTECH 2024 Dijon, France, July 9—11, 2024 Revised Selected Papers, 94-113, https://doi.org/10.1007/978-3-032-04777-9_6
  • Yu, X. SUPPLEMENTARY MATERIALS: An agent-based approach to the gravity model of linguistic diffusion.
  • Yuan, J., Shao, Q., Sun, J., Zhang, J., & Peng, X. (2025). Research on prevention and control strategies for passenger group events due to flight delays based on multi-agent modelling. Travel Behaviour and Society, 41, 101077.
  • Yue, Z., Ma, Z., Yao, D., He, Y., Gu, L., & Jing, S. (2025). Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts. Applied Sciences 15(12). https://doi.org/10.3390/app15126813
  • Yusuf, M., Sophan, M. K., & Satoto, B. D. (2025). Analisa Proses Bisnis. Media Nusa Creative (MNC Publishing).
  • Zaccagnino, R., Lettieri, N., Malandrino, D., Lomasto, L., Camoia, A., & Guarino, A. (2025). Turning AI into a regulatory sandbox: exploring information disorder mitigation strategies with ABM and deep reinforcement learning. Neural Computing and Applications, 1-42.
  • Zaljey, A., Dasanayake, L., Kodithuwakku, T., Herath, T., & Silva, N. (2025). Agent-Based Modeling of the Egg Supply Chain: Dynamics, Challenges, and Optimization Strategies. 2025 International Research Conference on Smart Computing and Systems Engineering (SCSE), pp. 1-6, doi: 10.1109/SCSE65633.2025.11031034.
  • Zamperini, M. B., & Schweha, D. J. (2025). Simulating Collaboration in Small Modular Nuclear Reactor Cybersecurity with Agent-Based Models. Journal of Cybersecurity and Privacy 5(4), 83, https://doi.org/10.3390/jcp5040083.
  • Zandbergen, B., Quist, J., Blanksma, N., de Waart, W., Warnier, M. (2025). Enhancing Textile Reuse in the Netherlands: Agent- Based Modelling for Reuse, Rebound Effects and Value Changes in Textiles.
  • Zandee, T. (2025). Towards modelling the cultural evolution of sustainable societies [Master's thesis, Utrecht University]. Utrecht University Student Theses Repository. https://studenttheses.uu.nl/handle/20.500.12932/50218
  • Zapata-Celis, J. A., Ceballos, Y. F., & Castillo-Grisales, J. A. (2025). Modelo de simulación del crecimiento del parque automotor para revisión de políticas públicas en gestión de demanda. Revista Facultad De Ingeniería Universidad De Antioquia, (116), 90–99.
  • Zellner, M.L., Massey, D., Laboy, M., O'Brien, D.T., Mueller, A., & Engelberg, D. (2025). Enhancing digital twin technology with community-led, science-driven participatory modeling: A case in green infrastructure planning. Environment and Planning B: Urban Analytics and City Science. https://doi.org/10.1177/23998083251323671
  • Zeng, H., & Lei, X. (2025). An Integrated Cybersecurity Defense Framework for Attack Intelligence Analysis, Counteraction, and Traceability in Complex Network Architectures. EAI Endorsed Transactions on Scalable Information Systems, 12(6), 18. https://doi.org/10.4108/eetsis.9770
  • Zeng, J., Lei, S., Qian, Y., & Wei, X. Multi-person Group Walking Behavior Simulation in Urban Pedestrian Street.
  • Zhang, C., Zhang, F., Yin, S., Fu, Y., Liu, J., & Duan, P. (2025). Study on the optimization of duration for repetitive projects considering spatial interference effects in operations. Journal of Asian Architecture and Building Engineering, 1-26.
  • Zhang, J. (2025). Resilience-Inclusive Capacity Assessment for Railway Networks Under Virtual Coupling via Simulation. Journal of Applied Mathematics. https://doi.org/10.1155/jama/9923804
  • Zhang, J., Laili, Y., Gong, J., Zhang, L., Ren, L. (2025). A Quantitative Learning Method for Simulation Model Evaluation Using L-SHADE Optimized Structured Regression. In: Cai, W., Low, M., Tan, G., D'Angelo, G., Ta, D. (Eds.) Methods and Applications for Modeling and Simulation of Complex Systems. Communications in Computer and Information Science 2727, (pp. 3-15). Springer, Singapore. https://doi.org/10.1007/978-981-95-4472-1_1
  • Zhang, J., Zhuge, C., Huang, Q., Wang, B., Li, Y., & Oosterveer, P. (2025). Farmers' decisions on crop residues utilization, greenhouse gases reduction and subsidy of crop residue-based bioenergy: An agent-based life cycle model. Sustainable Production and Consumption 55, 24-36. https://doi.org/10.1016/j.spc.2025.02.001
  • Zhang, P., Huang, A., Wu, L., Li, R., & Fu, Z. (2025). A multi-agent simulation-based decision support tool for Sustainable Tourism Land Use Planning in rural China. Land, 14(12), 2342. https://doi.org/10.3390/land14122342
  • Zhang, S., Zou, H., Yao, Y., Feng, K., Tian, J., Liu, D., & Du, H. (2025). From Demonstration to Diffusion: Community Dynamics and Incentives Analysis in Household Solar Photovoltaic Adoption. Energy, 137302.
  • Zhang, X., Zhang, H., Yan, S., Xie, M., & Wang, Y. (2025). Parking pricing strategies in the era of autonomous vehicles. Transport Policy 166, 87-100. https://doi.org/10.1016/j.tranpol.2025.03.003
  • Zhang, Y. (2025). A Hybrid Fuzzy-IGA-APSO Framework for Real-Time Urban Landscape Optimization in Smart Cities. Informatica, 49(35). https://doi.org/10.31449/inf.v49i35.9973
  • Zhang, Y., Ding, Z., Sun, J., Zhao, X., Hu, X., & Yang, Z. (2025). Optimizing AI strategies in e-commerce customer service: An agent-based simulation. Electronic Markets, 35(1), 1-22.
  • Zhang, Y., Song, P., & Yang, Q. (2025). A Risk-Aware Dynamic Credit Allocation Mechanism in Green Supply Chains: An Agent-Based Model with ESG Metrics. Risks 13(12). https://doi.org/10.3390/risks13120236
  • Zhang, Y., Yu, X., & Luo, S. (2025). Misleading air quality reports lower the public’s perception of pollution and increase travel behavior. Communications Earth & Environment, 6(1), 689.
  • Zhang, Z., Jalali, M. S., & Ghaffarzadegan, N. (2025). Behavioral Dynamics of Epidemic Trajectories and Vaccination Strategies: A Simulation-Based Analysis. Journal of Artificial Societies and Social Simulation 28 (1) 3.
  • Zhao, X., & Gopalappa, C. (2025). A Modeling Framework for Evaluating the Synergistic Impact of Structural Interventions on Related Diseases: HIV and Cervical Cancer as Case Study. medRxiv.
  • Zhao, Y., Qiao, T., Chen, Y., Kuang, M., Bai, W., Yi, Y., ... & Wang, W. (2025). Attention on social media depends more on how you express yourself than on who you are. Nature Human Behaviour, 1-15.
  • Zhao, Y., Qin, X., & Su, X. (2025). Construction and Simulation Study of a Threshold Model for the Diffusion of Agricultural Technology Innovation Based on Cognitive Utility. In Y. Sun (Ed.), Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025), Advances in Computer Science Research, 122. https://doi.org/10.2991/978-94-6463-823-3_112
  • Zhenhai, X., Minggang, Y., Ming, H., Guoyou, C., Zheng, Z., Ziyu, W., & Lu, L. (2025). Evolution Mechanism of Unmanned Cluster Cooperation Oriented Toward Strategy Selection Diversity. Journal of Systems Engineering and Electronics, 36(2), 462-482.
  • Zhu, W., Han, W., Lu, R., & Lei, J. (2025). The impact of enterprises’ scientific capabilities on innovation performance: evidence from an empirical analysis and simulation model. Humanities and Social Sciences Communications, 12(1), 1-18.
  • Zimdahl, R. & Knöös Franzén, L. (2025). An Inclusive Method for Connecting System-of-Systems Architectures with Stakeholders. Systems, 13(6).
  • ZOTO, E., KOWALSKI, S. J., MEINICH, A. H., LINDSTØL, A., & ENGESVIK, S. L. Addressing agents’ strategies in a cyberwar game–CyberAIMs 2.0. Business, Innovation and Technology ICEBIT 2024, 1397.
  • Алексеева, Е. А. (2024). Методологическое взаимодействие экспериментальной и компьютерной философии.
  • Бумажкина, Н. Ю. (2025). Подход к разработке архитектуры программного комплекса поддержки процесса переразмещения виртуальных машин виртуализированного центра обработки данных. Известия высших учебных заведений. Поволжский регион. Технические науки, (1 (73)), 16-28.
  • Дзігора, К. Р. (2025). Дисертація «Соціальні мережі як форми репрезентації історичної пам’яті» (Doctoral dissertation, Київський столичний університет імені Бориса Грінченка).
  • Карпенко, В. Р. (2025). Розробка паралельного вебсерверу для чисельного моделювання.
  • Неділько, І. М. (2025). Загальний огляд платформи для агентного моделювання як альтернатива NetLogo.
  • Патаракин, Е. Д., & Шишков, М. С. (2025). Проектирование образовательных практик на основе многоагентного подхода.
  • Чернявський, О. Ю. ВИКОРИСТАННЯ СУЧАСНИХ ТЕХНОЛОГІЙ ДЛЯ ВІДНОВЛЕННЯ НЕСПРАВНОЇ ТЕХНІКИ. Рекомендовано до друку вченою радою Національного університету «Чернігівська політехніка»(протокол № 6 від 26.05. 2025), 303.
  • مظهر قراملکی, الهی, & ناصر. (2024). بررسی اثر مالیات بر پول بر متغیرهای کلان اقتصادی در یک اقتصاد بسته با استفاده از مدل تعادل عمومی عامل محور. سیاست گذاری اقتصادی.‎
  • 김현우. (2025). 행위자기반모델링을 통해 본 통일신라토기의 생산과 유통 생산자와 소비자의 선택 기제에 대한 가설적 접근. 인문논총, 82(2), 187-218.
  • 안철휘, & 윤봉규. (2025). 국방 시설 사업 최적화를 위한 감독 업무 프로세스 분석 모형. 로지스틱스연구, 33(1), 43-58.
  • 刘嘉, 袁欣, 阮伟乔, & 白晋宇. (2025). 呼吸道传染病疫情下地铁站行人感染风险控制. 中国管理科学, 33(5), 236-246.
  • 贾宏刚, 王伟, & 承楠. (2025). 基于 NetLogo 的多智能体空战模拟. Command Control & Simulation/Zhihui Kongzhi yu Fangzhen, 47(2).

2024

  • Abd Hashim, Y. J., & Hasson, S. T. (2024, July). A Proposed Approach to Improve the WSN Performance Based on LEACH Algorithm. In 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA) (pp. 168-173). IEEE.
  • Abd Hashim, Y. J., & Hasson, S. T. (2024). Proposed Approach to Increase the Reliability of Wireless Sensor Networks through Dijkstra’s Algorithm. In SHS Web of Conferences (Vol. 204, p. 04002). EDP Sciences.
  • Abdelaziz, D. E., & Kadri, O. (2024). Enhancing epidemic management: agent-based simulation and remote diagnosis. Brazilian Journal of Technology, 7(2), e70355-e70355.
  • Abdidizaji, S., Yalabadi, A. K., Yazdani-Jahromi, M., Garibay, O. O., & Garibay, I. (2024). Agent-Based Modeling of C. Difficile Spread in Hospitals: Assessing Contribution of High-Touch vs. Low-Touch Surfaces and Inoculations' Containment Impact. arXiv preprint arXiv:2401.11656.
  • Abdo, J. B., & Hossain, L. (2024). Modeling the Invisible Internet. In Complex Networks & Their Applications XII: Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3 (p. 359). Springer Nature.
  • Abos, P. (2024). Computational Platforms and Toolkits: There are a variety of software platforms, libraries, and tools available for developing and simulating agent-based models.
  • Abos, P. (2024). Participatory Modeling and Stakeholder Engagement: Agent-based models can be used as tools for engaging stakeholders and the public in collaborative modeling exercises.
  • Adair, A. (2024). AI-Driven Assessment and Scaffolding for Mathematical Modeling and Explanations During Science Investigations (Doctoral dissertation, Rutgers The State University of New Jersey, School of Graduate Studies).
  • Adam, C., & Gaudou, B. (2024). An agent-based model of modal choice with perception biases and habits. arXiv preprint arXiv:2406.02063.
  • Adam, C., Gaudou, B. (2024). A survey about perceptions of mobility to inform an agent-based simulator of modal choice. Cornell University. https://doi.org/10.48550/arXiv.2502.12058
  • Adday, G. H., Subramaniam, S. K., Zukarnain, Z. A., & Samian, N. (2024). Investigating and Analyzing Simulation Tools of Wireless Sensor Networks: A Comprehensive Survey. IEEE Access, 12, 22938-22977.
  • Adderley, T., Concepcion, S. M., Ramirez, M. F., Tannis, D., & Browne, J. Pile Bay Agent-Based Modeling Exercise Proposal PPUA 5246: Participatory Modeling for Collaborate Decision Making.
  • Agarwal, A., & Canfield, C. (2024). Analysis of rural broadband adoption dynamics: A theory-driven agent-based model. PloS one, 19(6), e0302146.
  • Agarwal, R. (2024). AIoT‐Based Smart Bin for Real‐Time Monitoring and Management of Solid Waste. Reshaping Intelligent Business and Industry: Convergence of AI and IoT at the Cutting Edge, 295-303.
  • Aghaabbasi, M., & Sabri, S. (2025). Potentials of digital twin system for analyzing travel behavior decisions. Travel Behaviour and Society, 38, 100902.
  • Ahmed, M., Kazar, O., & Harous, S. (2024). Cyber‐physical system model based on multi‐agent system. IET Cyber‐Physical Systems: Theory & Applications.
  • Ahmed, R., Osman, R. A., & Amer, M. (2024). Navigating urban congestion: A Comprehensive strategy based on an efficient smart IoT wireless communication for PV powered smart traffic management system. PloS one, 19(10), e0310002.
  • Ahrweiler, P., Gilbert, N., Bicket, M., Coll, A. S., Capellas, B. L., Wurster, D., & Siqueiros, J. M. Gamification and Simulation for Innovation. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 121). Springer Nature.
  • Ainurohmah, S. (2024). Desain Ui/ux Aplikasi Sistem Lampu dan Suhu Otomatis pada Rumah Cerdas untuk Lansia (Doctoral dissertation, Universitas Islam Indonesia).
  • Ainurohmah, S. (2024). Desain Ui/ux Aplikasi Sistem Lampu dan Suhu Otomatis pada Rumah Cerdas untuk Lansia (Doctoral dissertation, Universitas Islam Indonesia).
  • Aizstrauts, A., & Ginters, E. (2024). Easy Communication Environment for Heterogeneous and Distributed Simulation Models Design.
  • Akbar, Chairil, Omar Alsawafy, Nabeel Alanbar, Ahmed Attia, and Productivity Improvement. "Simulation-Based Optimization for Multi-Echelon Petrochemical Supply Chain."
  • Akman, O., Bhumpelli, S., & Hay-Jahans, C. (2024). Agent-Based Modeling for the Neophyte: An Application of NetLogo. Spora: A Journal of Biomathematics, 10(1), 37-49.
  • Al Aziz, R. R., & Oktavianto, D. (2024). Perancangan Model Simulasi Sistem Parkir Berbasis Agen Untuk Peningkatan Efisiensi Dan Pengalaman Pengguna. Jurnal Gembira: Pengabdian Kepada Masyarakat, 2(03), 700-708.
  • Al-Sukhni, M. (2024). Blockchain Technology for Information Sharing to Mitigate the Bullwhip Effect. In Disruptive Technologies and Optimization Towards Industry 4.0 Logistics (pp. 95-121). Cham: Springer International Publishing.
  • Alaminos, A., & Alaminos-Fernández, A. (2024). Teoría de juegos y Modelo Bifactorial-Inercia Incertidumbre.
  • Albati, M., Sakurahara, T., Reihani, S., Kee, E., Yang, J., von Thaden, T., ... & Mohaghegh, Z. (2024). Uncertainty-Based Validation Methodology and Experimental Analysis for External Control Room Human Performance Simulation: Application to Fire Probabilistic Risk Assessment of Nuclear Power Plants. Nuclear Science and Engineering, 1-20.
  • Aldahlawi, R. Y., Akbari, V., & Lawson, G. (2024). A Systematic Review of Methodologies for Human Behavior Modelling and Routing Optimization in Large-scale Evacuation Planning. International Journal of Disaster Risk Reduction, 104638.
  • Alexandridis, K., Sabri, S., Smith, J., Logan, B., Bartfai-Walcott, K., & Migliori, D. (2024). Distributed AI Modeling and Simulation for Smart Airport Digital Twin Applications. Digital Twins, Simulation, and the Metaverse: Driving Efficiency and Effectiveness in the Physical World through Simulation in the Virtual Worlds, 195.
  • Almenar Vicente, B. (2024). Diseño y programación de bots de inteligencia artificial para juegos de gestión de recursos (Doctoral dissertation, Universitat Politècnica de València).
  • Alooeff, E., & Adzinets, D. (2024). Multi-agent system for intelligent scheduling.
  • Alteri, S. P. (2024). A COMPREHENSIVE FRAMEWORK FOR ENHANCING LARGE-SCALE EVENT SECURITY (Doctoral dissertation, Purdue University Graduate School).
  • Altmann, P., Schönberger, J., Illium, S., Zorn, M., Ritz, F., Haider, T., ... & Gabor, T. (2024). Emergence in Multi-Agent Systems: A Safety Perspective. arXiv preprint arXiv:2408.04514.
  • Alulema, D., Paredes-Velasco, M., & de Arriba Lasso, R. (2024). LESCA: Scaffolding and its impact on the higher cognitive levels and emotions of the student. Journal of Universal Computer Science, 30(7), 935.
  • Alvarado Maldonado, Z. K. (2024). Seguridad y Salud ocupacional de los trabajadores de obras en una empresa constructora de la región San Martín–2024.
  • Alves, R. B. D. O., Tomasiello, D. B., Almeida, C. M. D., Rosalen, D. L., Pereira, L. H., Silva, H. P. D., & Rodrigues, C. L. (2024). Agent-Based Spatial Dynamic Modeling of Diatraea saccharalis and the Natural Parasites Cotesia flavipes and Trichogramma galloi in Sugarcane Crops. Remote Sensing, 16(15), 2693.
  • Amakama, N. J., Duserre, G., Cadiere, A., & Schuette, R. W. (2024). Predicting the Impact of Mobile Field Hospital Deployment in Reducing Patients Mean Wait Times in Emergency Departments: An Agent-Based Simulation Approach.
  • Amakama, N. J., Dusserre, G., Cadiere, A., Schuette, R. W., & Zacharewicz, G. (2024). An agent-based simulation study on the effect of ambulance dispatch policies on patient mortality in hospital systems: a disaster response test case. International Journal of Emergency Management, 19(1), 25-50.
  • Amakama, N. J., Dusserre, G., Cadiere, A., Schuette, R. W., & Zacharewicz, G. (2024). How Effective are Tabletop Role-Playing (Serious) Games in Understanding and Validating the Predictive Capabilities of Disaster Response Agent-based Models?. American Journal of Multidisciplinary Research and Innovation, 3(4), 49-61.
  • Ametys, C. M. S. (2024). MASTER Géomatique-Parcours ScIences Géomatiques en environneMent et Aménagement (SIGMA).
  • Amiri, M., Radfar, R., & Faezy Razi, F. (2025). Designing a Social Banking Model to Reduce Conflict of Financial Interest between Banks and Manufacturing Firms through Agent-Based Modeling Simulation. International Journal of Finance & Managerial Accounting, 10(37), 47-60.
  • Angeloni¹, M. P. C., & Grislin-Le Strugeon, E. (2024). Using Agent-Based Modeling and Simulation for Quality in Use. In Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024) (p. 151). Springer Nature.
  • Apetrei, C. I., Strelkovskii, N., Khabarov, N., & Rincón, V. J. (2024). Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning. Ecological Modelling, 489, 110609.
  • Aqib, M., & Ukil, A. (2024). Battery degradation model of electric vehicle with grid integration. Journal of Energy Storage, 97, 112709.
  • Ardyantha, D. R., & Abidin, M. R. I. (2024). Perancangan Identitas Visual Sebagai Representasi Cv Mulya Tempe Surabaya. Barik-jurnal S1 Desain Komunikasi Visual, 6(1), 192-206.
  • Arnold, E. G., Burroughs, E. A., Burroughs, O., & Carlson, M. A. International Journal of Mathematical Education in Science and Technology.
  • Arora, K., Gupta, V., Rajan, A., Kumar, L., Munjal, P., Jain, R., & Srivastava, A. (2025). Electrochemical immunosensor based on NiO for CA 15-3 detection and agent-based modelling for treatment optimization. Electrochimica Acta 515. https://doi.org/10.1016/j.electacta.2025.145708
  • Arshad, J., Talha, M., Saleem, B., Shah, Z., Zaman, H., & Muhammad, Z. (2024). A Survey of Bug Bounty Programs in Strengthening Cybersecurity and Privacy in the Blockchain Industry. Blockchains, 2(3), 195-216.
  • Arvizu, C. C. Modelo complejo de la cadena de suministro en NetLogo.
  • Ashrafi, B., Kim, G., Naseri, M., Barabady, J., Dhar, S., Heo, G., & Baek, S. (2024). An agent-based modelling framework for performance assessment of search and rescue operations in the Barents Sea. Safety in Extreme Environments, 1-18.
  • Aslan, U., Horn, M., & Wilensky, U. (2024). Why are some students “not into” computational thinking activities embedded within high school science units? Key takeaways from a microethnographic discourse analysis study. Science Education, 1–28. https://doi.org/10.1002/sce.21850
  • Assogba, G. G. C., Speelman, E. N., Berre, D., Adam, M., & Descheemaeker, K. Exploring Opportunities to Improve Crop-Livestock Integration and Production in Mixed Farms with a Serious Game: The Case of Semi-Arid Burkina Faso. Agricultural Systems 227. https://doi.org/10.1016/j.agsy.2025.104364
  • Astuti, R. N. M., & Ramdhan, D. H. (2024). Factors Contributing to the Successful Implementation of an OHS Management System in the Informal/SME Sector. Media Publikasi Promosi Kesehatan Indonesia (MPPKI), 7(6), 1458-1466.
  • Azamuke, D., Katarahweire, M., & Bainomugisha, E. (2024). MoMTSim: A Multi-agent-based Simulation Platform Calibrated for Mobile Money Transactions. IEEE Access.
  • Babkin, E., Golov, V., & Malyzhenkov, P. Check for updates Multi-agent Simulations of Mutual Trust Management Strategies as a Base of Innovative Organizational Forms Engineering. In Advances in Enterprise Engineering XVII: 13th Enterprise Design and Engineering Working Conference, EDEWC 2023, Vienna, Austria, November 28–29, 2023, Revised Selected Papers (p. 18). Springer Nature.
  • Baccile, S., Cornacchia, G., & Pappalardo, L. (2024). Measuring the Impact of Road Removal on Vehicular CO2 Emissions. In Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference. CEUR-WS.
  • Bai, L., Pan, T., Liu, S., Xie, Q., Zhang, Z., & Ma, Y. (2024). Dynamic measurement for the impact of project portfolio synergy. Heliyon.
  • Baker, M., & Reimann, P. (2024). CSCL: a learning and collaboration science?. International Journal of Computer-Supported Collaborative Learning, 1-9.
  • Bakkula, O., Wibisono, D., Basri, M. H., & Siallagan, M. (2024). Towards resilience in the value chain of coal mining upstream: an agent-based modeling and simulation to improve coal discrepancy. Discover Applied Sciences, 7(1), 22.
  • Baldo, H. (2024). Towards a Quantitative Theory of Digraph-Based Complexes and its Applications in Brain Network Analysis (Doctoral dissertation, Universidade de São Paulo).
  • Ballesteros Bahamón, A. L. (2024). Análisis de las dinámicas de interacción en la intermediación y su incidencia en las Capacidades de Absorción Potenciales en un Sistema de Innovación Agrícola.
  • Bandari, M., Azar, A., & Fathi Hafshejani, K. (2024). Design of An Agent-Based Simulation Model of Service Supply Chain. Journal of Industrial and Systems Engineering.
  • Bansal, R. (2024). Challenges in Large Language Model Development and AI Ethics. In Challenges in Large Language Model Development and AI Ethics (pp. 25-81). IGI Global.
  • Barbara, K., & Katarzyna, S. W. (2024). Impact of Cognitive Dissonance on Social Hysteresis: Insights fromthe Expressed and Private Opinions Model. arXiv preprint arXiv:2410.16934.
  • Barbrook-Johnson, P., van Voorn, G., Wang, H. H., Zare, F., Grant, W. E., Posnik, Z., & Lippe, M. (2024). Cross-scale feedbacks and tipping points in aggregated models of socio-ecological systems. Socio-Environmental Systems Modelling, 6, 18616-18616.
  • Barone¹, D. A., Wickboldt¹, J. A., Cavalcanti, M. C., de Carvalho, L. F., Gomes¹, J. E., & dos Anjos, J. C. (2024). Integrated Multi-agent System Simulator. In Simulation and Modeling Methodologies, Technologies and Applications: 13th International Conference, SIMULTECH 2023 Rome, Italy, July 12–14, 2023 Revised Selected Papers (p. 9). Springer Nature.
  • Barton, C. M., Aura-Tortosa, J. E., Garcia-Puchol, O., Riel-Salvatore, J., & Ullah, I. (2024). Lessons for an invisible future from an invisible past: Risk and resilience in deep time. The Holocene, 09596836241259788.
  • Basha, S. M., de Albuquerque, V. H. C., Chelloug, S. A., Elaziz, M. A., Mohisin, S. H., & Pathan, S. P. (2024). Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images. CMES-Computer Modeling in Engineering & Sciences, 138(2).
  • Batzke, M. C. L. (2024). Dynamics of Norms in Decision-Making (Doctoral dissertation, University of Kassel).
  • Bauer, R. (2024). Computational Modelling of Cell Migration. In Cell Migration in Development, Health and Disease (pp. 223-243). Cham: Springer Nature Switzerland.
  • Bazzanella, E., Barros, M. M., & Santos, F. (2024). Refatoraçao da Extensao NetLogo de Aprendizagem por Reforço para Integraçao com a Biblioteca BURLAP. In Este documento reúne os trabalhos apresentados nas sessões técnicas da 18ª edição do Workshop-Escola de Sistemas de Agentes, Seus Ambientes e Aplicações–Wesaac 2024, realizado na Universidade de Brasília, entre 14 e 16 de agosto de 2024. Durante as sessões técnicas, foram apresentados 21 artigos que (p. 51).
  • BEAUCHAMP-MUSTAFAGA, N. A. T. H. A. N., GREEN, K., MARCELLINO, W., LILLY, S., & SMITH, J. (2024). Dr. Li Bicheng, or How China Learned to Stop Worrying and Love Social Media Manipulation.
  • Becher, M. A., Twiston‐Davies, G., Osborne, J. L., & Lander, T. A. (2024). Resource gaps pose the greatest threat for bumblebees during the colony establishment phase. Insect Conservation and Diversity.
  • Beddiaf, F., & Schumann, R. (2024). Modeling Coopetition Dynamics Using Agent-Based Approaches. Matrix, 2(2), 0.
  • Beek, M. V., Lopate, M. Z., Goodhart, A., Peterson, D. A., Edgerton, J., Xiong, H., ... & Braumoeller, B. F. (2024). Hierarchy and war. American Journal of Political Science.
  • Beghdad, R., Benmenzer, F., & Eddine Khalfoune, A. (2024). Cloud Survivability Scenarios Under Attacks With and Without Countermeasures. Acta Informatica Pragensia.
  • Beghdad, R., Benmenzer, F., & eddine Khalfoune, A. Survivability Scenarios of the Cloud under Attacks With/Without Countermeasures.
  • Belfrage, M., Lorig, F., & Davidsson, P. (2024). Simulating Change-A Systematic Literature Review of Agent-Based Models for Policy-Making. In Annual Modeling and Simulation Conference (ANNSIM 2024), Washington DC, USA, May 20-23, 2024. Society for Modeling and Simulation International (SCS).
  • Benedicto-Rodriguez, G., Zorrilla-Muñoz, V., Fernandez, E., & Ferrandez, J. M. (2024, August). Technological Influence on the Measurement of Quality of Life in Persons with Autism Spectrum Disorder. In The International Conference on Innovations in Computing Research (pp. 691-698). Cham: Springer Nature Switzerland.
  • Berceanu, C., & Patrascu, M. Comparative Analysis of Agent-based Modeling Frameworks for Signal Propagation in Complex Networks: Netlogo and Python Mesa. system, 13(14), 15.
  • Berger, U., Bell, A., Barton, C. M., Chappin, E., Dreßler, G., Filatova, T., ... & Grimm, V. (2024). Towards reusable building blocks for agent-based modelling and theory development. Environmental Modelling & Software, 106003.
  • Bergman, D. R., Jackson, T. L., Jain, H. V., & Norton, K. A. (2024). SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters. bioRxiv, 2024-09.
  • Bernigaud, N., Bondeau, A., Guiot, J., Bertoncello, F., Ouriachi, M. J., Bouby, L., ... & Isoardi, D. (2024). The impact of climate change on the agriculture and the economy of Southern Gaul: New perspectives of agent-based modelling. Plos one, 19(3), e0298895.
  • Bertorello, S., Cei, F., Fink, D., Niccolai, E., & Amedei, A. (2024). The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations. Microorganisms, 12(9), 1828.
  • Berz, C. (2024). A Game-Theoretic Assessment of Anti-Discrimination Measures in Scientific Collaboration. KRITERION–Journal of Philosophy, (0).
  • Beutel, A., Xiao, K., Heidecke, J., & Weng, L. Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning.
  • Bhatia, V., & Jaglan, V. (2024). Integrating Artificial Intelligence and IoT for Smart Cities: Applications and Challenges. Reshaping Intelligent Business and Industry: Convergence of AI and IoT at the Cutting Edge, 267-278.
  • Bhatt, S., Verbert, K., & Van Den Noortgate, W. (2024). A Method for Developing Process-Based Assessments for Computational Thinking Tasks. Journal of Learning Analytics, 1-17.
  • Bijlard, H. M. Open vs Micro; comparing different agent populations and their impacts.
  • Bijli, M. K., Verma, P., & Singh, A. P. (2024). A systematic review on the potency of swarm intelligent nanorobots in the medical field. Swarm and Evolutionary Computation, 101524.
  • Bilge, U., Olgan, S., Ciplak, B., Ozbek, M., Kayikci, D., & Uysal, H. (2024). A Neanderthal Extinction Hypothesis: Agent Based Simulation of Birth Complications Theory.
  • Blanco, R., Patow, G., & Pelechano, N. (2024). Simulating real-life scenarios to better understand the spread of diseases under different contexts. Scientific Reports, 14(1), 2694.
  • Blasco Sevilla, E. (2024). Integración de Godot y SPADE para la construcción de entornos 3D para la navegación de robots (Doctoral dissertation, Universitat Politècnica de València).
  • Blee, J. A., Gorochowski, T. E., & Hauert, S. (2024). Optimization of periodic treatment strategies for bacterial biofilms using an agent-based in silico approach. Journal of the Royal Society Interface, 21(213), 20240078.
  • Blonder, A. (2024). An Agent-Based Simulation of Preferential Aggregation Based on Past Experience in Fission-Fusion Societies (Doctoral dissertation, UC Davis).
  • Bodine, E. N. (2024). BODINE: Modeling the Impacts of an Invasive Weevil on the Evolutionary Dynamics and Population Viability of a Florida Endangered Bromeliad.
  • Boetttcher, L., Fonseca, L. L., & Laubenbacher, R. (2024). Control of Medical Digital Twins with Artificial Neural Networks. bioRxiv, 2024-03.
  • Bommel, P., & Le Page, C. (2024). Rapport de mission à Parakou, Bénin, du 2 au 12 janvier 2024. Formation à la modélisation multi-agent pour des applications à la gestion intégrée des ressources naturelles (Doctoral dissertation, CIRAD (Montpellier; France)).
  • Borah, D. K., Zhang, H. X., Zellner, M., Ahmadisharaf, E., Babbar-Sebens, M., Quinn, N. W., ... & Lott, C. (2024). Advances in Total Maximum Daily Load Implementation Planning by Modeling Best Management Practices and Green Infrastructures. Journal of Environmental Engineering, 150(7), 03124003.
  • Borowsky, A. T., & Bailey-Serres, J. (2024). Rewiring gene circuitry for plant improvement. Nature Genetics, 1-9.
  • Bouaziz, N., Bettayeb, B., Sahnoun, M. H., & Yassine, A. (2024). Incorporating uncertain human behavior in production scheduling for enhanced productivity in Industry 5.0 context. International Journal of Production Economics, 109311.
  • Bourgais, M., Saval, A., Tranouez, P., Gillet, O., & Daudé, É. (2024). Designing a Serious Game of crisis management on top of an Agent-Based Simulation of population evacuation.
  • Bowden, S. A. (2024). Musical Visuality: Embodying Meter and Overcoming Noise in the Marching Arts (Doctoral dissertation, Northwestern University).
  • Bowers, J., & Eidin, E. (2024). Analyzing students’ systems thinking in-situ through screencasts in the context of computational modeling: a case study. Disciplinary and Interdisciplinary Science Education Research, 6(1), 24.
  • Boz, Z., Kiker, G., Haase, H., Orr, R., Vignesh, A., Campbell, C., ... & Clemen, T. Modeling Waste: An Agent-Based Model to Support the Measurement of Household Food Waste. Available at SSRN 5017237.
  • Bozzi, A., Graffione, S., Jimenez, J. F., Sacile, R., & Zero, E. (2024). A Platoon-Based Approach for AGV Scheduling and Trajectory Planning in Fully Automated Production Systems. IEEE Transactions on Industrial Informatics.
  • Braccini, M., De Filippo, A., Lombardi, M., & Milano, M. (2024). Swarm Intelligence: A Novel and Unconventional Approach to Dance Choreography Creation.
  • Brady, C., Ramírez, P., & Lesh, R. (2024). Problem Posing and Modeling: Confronting the Dilemma of Rigor or Relevance. In Problem Posing and Problem Solving in Mathematics Education: International Research and Practice Trends (pp. 33-50). Singapore: Springer Nature Singapore.
  • Brainard, J. S., Lake, I. R., & Hunter, P. R. (2024). Evaluation of three control strategies to limit mpox outbreaks in an agent based model. medRxiv.
  • Bravo, R. R. (2024). 3D Organ-Scale Models of Tumor Growth and Treatment (Doctoral dissertation, University of South Florida).
  • Brennan, R., & Goldsmith, P. (2024). Unlocking Engineering Threshold Concepts Through Stakeholder Engagement. Proceedings of the Canadian Engineering Education Association (CEEA).
  • Brooks, J. D. (2024). Epidemiologic, Social, and Economic Dimensions of Chronic Wasting Disease Management in Indiana (Doctoral dissertation, Purdue University Graduate School).
  • Brouillet, M. J., & Georgiev, G. Y. (2024). Why and How do Complex Systems Self-Organize at All? Average Action Efficiency as a Predictor, Measure, Driver, and Mechanism of Self-Organization. arXiv preprint arXiv:2408.10278.
  • Brouillet, M., & Georgiev, G. Y. (2024). Modeling and Predicting Self-Organization in Dynamic Systems out of Thermodynamic Equilibrium: Part 1: Attractor, Mechanism and Power Law Scaling. Processes, 12(12), 2937.
  • Brown, T. L., Morgan, E. R., & Caplaet, P. Nemasim: Using an Agent Based Model to Explore the Role of Landscape Fragmentation and Deer Ecology in Transmitting Drug Resistant Nematodes between Sheep Pastures. Available at SSRN 4783624.
  • Bryant, G. A., & Smaldino, P. E. (2024). The cultural evolution of distortion in music.
  • Buchsbaum, K. (2024). From the Ground Up: A Complex Systems Approach to Climate Change Adaptation in Agriculture.
  • Burghardt, M., Ruth, N., & Niekler, A. Einleitung: Zur Rolle von Tools in den Digital Humanities.
  • Burillo, F., Lambán, M. P., Royo, J. A., Morella, P., & Sánchez, J. C. (2024). Real-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots. Applied Sciences, 14(5), 1890.
  • Buteau, C., Namukasa, I., & Sardella, J. (2024). CCMEME 2023 Proceedings.
  • Butler, M., Doughty, A., Lifton, N., Kelley, S., Moucheron, M., O’Brien, C., ... & Warren, G. (2024). Looking Up for prehistoric hunter-gatherer archaeological sites in mountain landscapes in Europe: A case study from the Cairngorms, Scotland. Hunter Gatherer Research, 1-30.
  • Buzato, D., & Cunha, E. (2024, May). Agent-based Modeling of Language Change in a Small-world Network. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 594-599).
  • Cabrera-Revuelta, E., Tavolare, R., Buldo, M., & Verdoscia, C. (2024). Planning for terrestrial laser scanning: Methods for optimal sets of locations in architectural sites. Journal of Building Engineering, 85, 108599.
  • Cabuya-Padilla, D. E., & Castaneda-Marroquin, C. A. (2024). Marco de referencia para el modelamiento y simulación de la ciberdefensa marítima-MARCIM: estado del arte y metodología. DYNA, 91(231), 169-179.
  • Cai, C., Tian, S., Shi, Y., Chen, Y., & Li, X. (2024). Influencing Factors Analysis in Railway Engineering Technological Innovation under Complex and Difficult Areas: A System Dynamics Approach. Mathematics, 12(13), 2040.
  • Canales, M., Castilla-Rho, J., Rojas, R., Vicuña, S., & Ball, J. (2024). Agent-based models of groundwater systems: A review of an emerging approach to simulate the interactions between groundwater and society. Environmental Modelling & Software, 175, 105980.
  • Canals, C., Maroulis, S., Mizala, A., Canessa, E., & Chaigneau, S. (2024). Mechanisms Behind Public School Enrollment Trends in School Choice Systems: The Case of Chile. Journal of Artificial Societies and Social Simulation, 27(4), 1-2.
  • Cao, Z., Zhu, J., & Wang, Z. (2024). Analysis of HFE impact of COVID-19 on OHS in construction enterprises. Heliyon.
  • Caprioli, C. The Integration of Multi-Agent System and Multicriteria Analysis for Developing Participatory Planning Alternatives in Urban Contexts. Available at SSRN 4918496.
  • Caraiani, P., Cotfas, L. A., Darie, F. C., Delcea, C., Iglesias, C. A., & Prodan, R. (2024). JobKG: A Knowledge Graph of the Romanian Job Market based on Natural Language Processing.
  • Carbo, J., Pedraza, J., & Molina, J. M. (2024). Agents preserving privacy on intelligent transportation systems according to EU law. Artificial Intelligence and Law, 1-34.
  • Cárdenas Benavides, E. S. Optimizando el diseño instruccional mediante algoritmos genéticos (Doctoral dissertation, Universidad Nacional de Colombia).
  • Carley, K. M. (2024). Unveiling Transport Dynamics in Developing Countries, a Data-Driven Agent-Based Modeling Approach.
  • Carpenter, C. J., Cruz, S. M., & Reynolds, R. M. (2024). Expansion and Exploration of the Superdiffuser Model With Agent-Based Modeling. Communication Research, 00936502241285574.
  • Casadei, R., Fornari, F., Mariani, S., & Savaglio, C. Fluidware Meets Digital Twins Check for updates. Fluidware: Novel Approaches for Large-Scale IoT Systems, 137.
  • Castellani, B., & Gerrits, L. (2024). Cartography and constructing the atlas. In The Atlas of Social Complexity (pp. 55-61). Edward Elgar Publishing.
  • Castellani, B., & Gerrits, L. (2024). Mapping the new methodological terrain. In The Atlas of Social Complexity (pp. 416-445). Edward Elgar Publishing.
  • Castellani, B., & Gerrits, L. (2024). Thirteen situations. In The Atlas of Social Complexity (pp. 35-54). Edward Elgar Publishing.
  • Castillo Grisales, J. A., Ceballos, Y. F., Bastidas-Orrego, L. M., Jaramillo Gómez, N. I., & Chaparro Cañola, E. (2024). Development of an Agent-Based Model to Evaluate Rural Public Policies in Medellín, Colombia. Sustainability, 16(18), 8185.
  • Castillo Losada, L. T. (2024). La educación ambiental y las TIC en la educación secundaria y media en Colombia: una revisión de literatura.
  • Castillo Losada, L. T. (2024). La educación ambiental y las TIC en la educación secundaria y media en Colombia: una revisión de literatura.
  • Castillo Orellana, M. N. (2024). Medición de la nerviosidad en un plan maestro de producción mediante el uso de sistema multi agentes. Dirección de Bibliotecas, Universidad de Concepción.
  • Cavallaro, C., Crespi, C., Cutello, V., Pavone, M., & Zito, F. (2024). Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem. Algorithms, 17(2), 63.
  • Cavique, L. (2024). Networks and Connectivity: Metrics and. The Agent ID Model: A Multidisciplinary Framework of Evolutionary Human Behavior, 49.
  • CAZA, O., & RUDY, R. (2024). MODELO DE DIRECCIONAMIENTO VIAL MEDIANTE ALGORITMO DE COLONIA DE HORMIGAS Y TEORIA DE GRAFOS EN LA TRANSITABILIDAD VIAL DE EXTRANCA RIO SECO (Doctoral dissertation).
  • Cerdá, M., Hamilton, A. D., Hyder, A., Rutherford, C., Bobashev, G., Epstein, J. M., ... & Keyes, K. M. (2024). Simulating the simultaneous impact of medication for opioid use disorder and naloxone on opioid overdose death in eight New York counties. Epidemiology.
  • Černohorský, J., Šafránek, S., & Štekerová, K. (2024). Game Theory and Agent-Based Models in Epidemiology: Exploration of Strategies with NetLogo. doi: 10.36689/uhk/hed/2024-01-009.
  • Černý, J. H. K. (2024). Agentově orientované modelování revolučních procesů1. Sociológia, 56(3), 189-219.
  • Chae, S. J., Kim, D. W., Igoshin, O. A., Lee, S., & Kim, J. K. (2024). Beyond microtubules: The cellular environment at the endoplasmic reticulum attracts proteins to the nucleus, enabling nuclear transport. bioRxiv, 2024-01.
  • Chakraborty, S., Goyal, S., Rieder, A., Onuchowska, A., & Berndt, D. J. (2024). Freedom of speech or freedom of reach? Strategies for mitigating malicious content in social networks. Decision Support Systems, 114235.
  • Chang, X., Chen, J., & Ye, L. (2024). Trend prediction of farmers’ spontaneous land transfer behavior: evidence from China. Applied Economics, 1-15.
  • Chao, S., Tao, Y., & Zhou, H. (2024). Technological Progress and its Job Market Impacts: A Basic Simulation Framework for Human-Technology Collaboration. Available at SSRN 4747459.
  • Chattoe-Brown, E., Elsenbroich, C., & Gilbert, N. Reculer Pour Mieux Sauter: Resurrecting a Household Budgeting ABM for Deteriorating Economic Conditions. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 35). Springer Nature.
  • Chen, F., & Chen, G. (2024). Technology-Enhanced Collaborative Inquiry in K–12 Classrooms: A Systematic Review of Empirical Studies. Science & Education, 1-43.
  • Chen, I., & Cheng, K. L. (2024). Agent-Based Modeling Framework for Predicting Regional Electricity Consumption Considering Occupant Behavior Shift and Exogenous Policy Impact. Karly, Agent-Based Modeling Framework for Predicting Regional Electricity Consumption Considering Occupant Behavior Shift and Exogenous Policy Impact.
  • Chen, J., Lotsos, A., Zhao, L., Hullman, J., Sherin, B., Wilensky, U., & Horn, M. (2024). A Computational Method for Measuring" Open Codes" in Qualitative Analysis. arXiv. https://doi.org/10.48550/arXiv.2411.12142
  • Chen, J., Lu, X., Du, Y., Rejtig, M., Bagley, R., Horn, M. S., & Wilensky, U.(2024). Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/3613904.3642377
  • Chen, J., Zhao, L., Li, Y., Xie, Z., Wilensky, U., & Horn, M. S.(2024). Oh My God! It’s Recreating Our Room!” Understanding Children’s Experiences with A Room-Scale Augmented Reality Authoring Toolkit. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/3613904.3642043
  • Chen, K., Wang, X., Guo, Z., & Liu, W. (2024). The Disappearing Winners: An MAS Study of Community-Type Rivalry in Construction Markets. Buildings, 14(12), 3710.
  • Chen, M., Liu, R. X., & Hao, J. (2024). An agent-based real-time game model for forecasting the market penetration of vehicles in China. IEEE Access, 12, 24631-24643.
  • Chen, S., Du, X., & Wang, J. (2024). A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems. arXiv preprint arXiv:2401.10300.
  • Chen, W., Yuan, Y., & Lv, M. Educators' Networking Interacts with Digital Competence Heterogeneity to Enhance the Implementation of AIEd: A Mixed-Methods. In Big Data and Social Computing: 9th China National Conference, BDSC 2024, Harbin, China, August 8–10, 2024, Proceedings (p. 151). Springer Nature.
  • Chen, Y. W., Ruan, X., & Jain, R. K. (2024). Anchorfree Logo Detection Framework. In Recent Advances in Logo Detection Using Machine Learning Paradigms: Theory and Practice (pp. 65-82). Cham: Springer International Publishing.
  • Chen, Y., Du, T., Zhang, Q., & Zhang, N. (2024). Analysis and Evaluation of Species Invasion. Advances in Engineering Technology Research, 9(1), 687-687.
  • Chen, Y., Zhao, Y., & Wu, Y. (2024). Recent progress in air traffic flow management: A review. Journal of Air Transport Management, 116, 102573.
  • Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., ... & He, X. (2024). Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects. arXiv preprint arXiv:2401.03428.
  • Cherny-Shahar, T., & Yehudai, A. (2024). Multi-Lingual Development & Programming Languages Interoperability: An Empirical Study. arXiv preprint arXiv:2411.08388.
  • Chesney, T., Gruchman, T., Pasley, R., Dawson, A., & Gold, S. (2024). Agent modelling, statistical control, and the strength of middle knowledge. arXiv preprint arXiv:2409.17196.
  • Chesney, T., Jaffer, A., & Pasley, R. (2024). Examining inverse generative social science to study targets of interest. arXiv preprint arXiv:2407.13474.
  • Chesney, T., Pasley, R., & Jaffar, A. (2024). RIGSS-Inverse Generative Social Science using R. Software Impacts, 100689.
  • Choi, M. (2024). Navigating the Complexities of Pandemic Dynamics: An Integrated Agent-Based Modeling Framework for Spatially Explicit Risk Assessment and Mitigation at Multiple Scale (Doctoral dissertation, The University of Utah).
  • Chopra, A., Subramanian, J., Krishnamurthy, B., & Raskar, R. (2024, May). flame: A Framework for Learning in Agent-based ModEls. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (pp. 391-399).
  • Chueca Del Cerro, C. (2024). The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model. Applied Network Science, 9(1), 69.
  • Civil, R. D. M. I., & de Inversión, A. D. T. Presentación del Programa de Investigación 70593, la Red sobre Respuestas a la Corrupción Asociada al Crimen Organizado Transnacional y su Grupo de Derecho Privado. HÉCTOR OLASOLO, 41.
  • Cloude, E. B., Kiili, K., & Dindar, M. The Intra-and Inter-Personal Effects of Emotions on Collaborative Problem-Solving Efficiency between Competitive and Non-Competitive Teams in a Multiplayer Digital Game. Available at SSRN 5021794.
  • Coates, G., Dugdale, J., & Hanachi, C. (2024). Simulation for crisis and disaster management. SIMULATION, 100(4), 333-334.
  • Coco, E., & Iovita, R. Agent-based simulations reveal multiple rapid northern routes for the second Neanderthal dispersal from Western to Eastern Eurasia.
  • Collard, P. (2024). Processionary Caterpillars at the Edge of Complexity. Artificial Life, 1-22.
  • Collignon, J. (2024). Multiscale Modeling and Deep Learning for Complex Microbial Colonies (Doctoral dissertation, University of California, Merced).
  • Collins, A. J., & Grigoryan, G. (2024). ABMSCORE: a heuristic algorithm for forming strategic coalitions in agent-based simulation. Journal of Simulation, 1-25.
  • Collins, A., Koehler, M., & Lynch, C. (2024). Methods that support the validation of agent-based models: An overview and discussion. Journal of Artificial Societies and Social Simulation, 27(1), 11.
  • Colon, S. A. (2024). Simulating the Spread of Information Among Non-Playable Characters in Video Games.
  • Contreras, M., Sobrino, I., & de la Fuente, J. (2024). Paratransgenic quantum vaccinology. Trends in Parasitology.
  • Cooksey, R. W. (2024). The Evolving Landscape of Choices for Navigating the ‘Data Triangle’. In Unity from Diversity: Pluralist Systemic Thinking for Social and Behavioural Research (pp. 67-164). Singapore: Springer Nature Singapore.
  • Corrêa Angeloni, M. P., Duque Medina, R., Marçal de Oliveira, K., Strugeon, G. L., & Tirnauca, C. (2024). A tertiary study on quality in use evaluation of smart environment applications.
  • Correia, L. G. A influência de autocitações de periódicos no Fator de Impacto: uma análise a partir da simulação de redes sociais acadêmicas.
  • Correia, L. G., & Mena-Chalco, J. P. A INFLUÊNCIA DE AUTOCITAÇÕES NO FATOR DE IMPACTO: UMA COMPARAÇÃO DE DUAS POLÍTICAS DE CÁLCULO.
  • Costa, C. J., Aparicio, J. T., & Aparicio, M. (2024). Socio-Economic Consequences of Generative AI: A Review of Methodological Approaches. arXiv preprint arXiv:2411.09313.
  • Cottineau, C., Batty, M., Benenson, I., Delloye, J., Hatna, E., Pumain, D., ... & Ubarevičienė, R. (2024). The role of analytical models and their circulation in urban studies and policy. Urban Studies, 00420980241237410.
  • Cox, S. J., Manavi, K. M., & Guerin, S. Developing a Computational Geospatial Curriculum for Community Decision. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 231). Springer Nature.
  • Crabtree, S. A., Wren, C. D., Dixit, A., & Levin, S. A. (2024). Influential individuals can promote prosocial practices in heterogeneous societies: a mathematical and agent-based model. PNAS Nexus, 3(7).
  • Crispim-Mendes, T., Roos, D., Ferreira, C. M., Paupério, J., Silva, J. P., Godinho, S., ... & Pita, R. (2024). Patch spatial attributes and time to disturbance affect the emergence of source local populations within ephemeral habitats. Ecological Modelling, 496, 110839.
  • Crosland, P., Marshall, D. A., Hosseini, S. H., Ho, N., Vacher, C., Skinner, A., ... & Hickie, I. B. (2024). Incorporating Complexity and System Dynamics into Economic Modelling for Mental Health Policy and Planning. PharmacoEconomics, 1-15.
  • Cui, P., Lu, J., Wu, Y., Tang, J., & Jiang, J. (2024). Effect of urban morphology on microclimate and building cluster energy consumption in cold regions of China. Sustainable Cities and Society, 105838.
  • Cukla, A. R., & Mota, F. P. Visão computacional em ação e sistemas multiagentes.
  • Curry, J. (2024). ENERGY LAW.
  • Cyril—Bios, P. (2024). Using the ODD protocol and NetLogo to replicate agent-based models.
  • Czuba, M., Nurek, M., Serwata, D., Qiu, Y. X., Jia, M., Musial, K., ... & Bródka, P. (2024). Network Diffusion--Framework to Simulate Spreading Processes in Complex Networks. arXiv preprint arXiv:2405.18085.
  • D'Amico, A., Sparvoli, G., Bernardini, G., Bruno, S., Fatiguso, F., Currà, E., & Quagliarini, E. (2024). Behavioural-based risk of the built environment: Key performance indicators for sudden-onset disaster in urban open spaces. International Journal of Disaster Risk Reduction, 104328.
  • Dahshan, M., & Galanti, T. (2024). Teachers in the Loop: Integrating Computational Thinking and Mathematics to Build Early Place Value Understanding. Education Sciences, 14(2), 201.
  • Dai, L., Han, Q., & de Vries, B. (2024). Simulating dynamical evolution of citizen participation leveraging agent-based modeling: Experiences from nature-based solutions in China. Cities, 151, 105145.
  • Danan, C. (2024). Differences in commercial brand promotion due to Chinese and Russian culture.
  • Darly, S. S., Kadhiravan, D., Hemachandran, K., & Rege, M. (2024). Simulation Strategies for Analyzing of data. Handbook of Artificial Intelligence and Wearables, 27-64.
  • Das, K., Dhal, G. C., & Kalamdhad, A. S. (2024). Integrated assessment for groundwater quality and flood vulnerability in coal mining regions. Environmental Science and Pollution Research, 1-16.
  • Das, S. (2024). Network-based modeling of infectious disease spillover (Doctoral dissertation).
  • DaSilva, D., & Yokley, K. A. (2024). Mathematical Approaches of Modeling Obesity Trends. The North Carolina Journal of Mathematics and Statistics, 10(1), 20-39.
  • Daugherty, M. K., & Carter, V. (2024). 19 Ways of thinking and STEM-based problem solving. Ways of Thinking in STEM-based Problem Solving: Teaching and Learning in a New Era, 263.
  • Davey, T. (2024). Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and Simulations. Entropy, 26(8), 683.
  • Davis, N., Dermody, B. J., Koetse, M., & van Voorn, G. A. (2024). Identifying personal and social drivers of dietary patterns: An agent-Based model of Dutch consumer behavior. Journal of Artificial Societies and Social Simulation, 27(1).
  • de Azevedo, G. T. (2024). Sólidos de Revolução e Produção de Sorvetes Geométricos: Formação em Matemática e Pensamento Computacional. REMATEC, 19(47), e2024038-e2024038.
  • de Borba, J. S., & Gonçalves, S. (2024). One predator and two prey: Coexistence of pumas, guanacos and sheep in Patagonia. arXiv preprint arXiv:2412.02936.
  • De Carvalho, L. F. B. S., de Souza, V. S., Bonatto, A. N., Perez, T. J. A. M., de Freitas, E. P., Barone, D. A. C., ... & de Araujo Fernandes, R. Q. (2024, July). A Multi-agent System Approach for Blue Force Tracking C2 Application Modeling. In Intelligent Systems Conference (pp. 161-181). Cham: Springer Nature Switzerland.
  • DE CASTRO, C. J. X. B. CONCEIÇÃO JACQUELINE XAVIER BARBOSA DE CASTRO MERCADO DE CAPITAIS ARTIFICIAL: UMA SIMULAÇÃO BASEADA EM AGENTES.
  • de França, B. B. N., Pfahl, D., Neto, V. V. G., & Ali, N. B. (2024). Teaching Simulation as a Research Method in Empirical Software Engineering. Handbook on Teaching Empirical Software Engineering, 363.
  • DE JESUS, V. S. COMUNICABILIDADE E MOBILIDADE ENTRE SISTEMAS MULTIAGENTES EMBARCADOS E DISTRIBUÍDOS.
  • De Luque-Villa, M. A., & González-Méndez, M. (2024). Water Management as a Social Field: A Method for Engineering Solutions.
  • de Paulo, K. P., Estombelo-Montesco, C. A., & Tejada, J. (2024). New memory-one strategies of the Iterated Prisoner’s Dilemma: a new framework to programmed human-AI interaction. Discover Psychology, 4(1), 20.
  • de Tutorías, H., Miras, R. M. S., Morales, J. M. R., Calderón, C. A., Stanek, M., & Riviére, J. Guía docente de Epistemología y ciencias sociales computacionales (MD6/56/1).
  • Deb, L. C., & Lanzas, C. In-feed antimicrobial treatment duration on liver abscess prevalence and gut enteric bacteria resistance level in feedlot cattle: An individual-based model.
  • Dehkordi, M. A. E. (2024). Simulating Dynamics of Institutions (Doctoral dissertation, Delft University of Technology).
  • Demuth, S., Ed‐Driouch, C., Dumas, C., Laplaud, D., Edan, G., Vince, N., ... & Gourraud, P. A. (2024). Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data. European Journal of Neurology, e16363.
  • Derigent, W., David, M., André, P., Cardin, O., & Najjar, S. (2024). WSN Energy Control by Holonic Dynamic Reconfiguration: Application to the Sustainability of Communicating Materials. Sustainability, 16(18), 8193.
  • Dey, A., & Pasupuleti, R. S. (2024). Interpreting the relevance of Prohibited and Regulated Areas: The case of Protected Monuments of Delhi. Advancing Heritage Innovations in India, 29.
  • Di Lucchio, L., & Modanese, G. (2024). Generation of Scale-Free Assortative Networks via Newman Rewiring for Simulation of Diffusion Phenomena. Stats, 7(1), 220-234.
  • Diallo, A. O., Doniec, A., Lozenguez, G., & Mandiau, R. (2024). Approche centrée agent pour l’intermodalité basée sur des données réelles. Revue Ouverte d'Intelligence Artificielle, 5(1), 95-129.
  • DIARRA, A., KONE, M., & BOUHALOUAN, D. (2024). Outil Intelligent Distribué pour l’élaboration de pronostics médicaux-MedProMas (Doctoral dissertation).
  • Diaz-Mena, K. S., Kogan, L., & Stucchi, L. (2024). On how breaking traffic rules affects vehicle flow. Journal of Computational Social Science, 1-21.
  • Dieguez Cameroni, F. J., Varela Casadey, F., Boscana, M., Schinatto, F., & Bussoni, A. (2024). Advancing carbon neutrality in Silvopastoral systems: a case study applying agent-based modeling. Agroforestry Systems, 1-16.
  • Dindar, M., Cloude, E. B., & Kiili, K. (2024, September). An Experimental Study of Facial Expressions in Collaborative Teams that Quit
  • Ding, H., & Xie, L. (2024). The applicability of positive information in negative opinion management: An attitude-laden communication perspective. Physica A: Statistical Mechanics and its Applications, 129839.
  • Diouf, E. G., Brévault, T., Ndiaye, S., & Piou, C. (2024). Exploration of the potential of a boosted sterile insect technique to control fruit flies in mango orchards. Pest Management Science.
  • Dodd, E., & Van Limergen, D. (2024). Methods in Ancient Wine Archaeology: Scientific Approaches in Roman Contexts. Bloomsbury Publishing.
  • Dohn, N. B. (2024). Philosophical presuppositions in “Computational thinking”–old wine in new bottles?. Journal of Philosophy of Education, qhae016.
  • Domino, K., Sochan, A., & Miszczak, J. A. (2024). Analytical assessment of workers' safety concerning direct and indirect ways of getting infected by dangerous pathogen. arXiv preprint arXiv:2409.16809.
  • Dong, Q., Yan, R., Xiong, R., Xie, W., Pang, M., & Liang, L. (2024). A study on the decision-making mechanism and promotion policy of urban residents’ domestic waste-classification behavior: evidence from Sichuan province. Environment, Development and Sustainability, 1-37.
  • Dong, S., Xu, L., Lan, Z. Z., Bu, F., Hua, W., Chunlai, Q., ... & Kai, G. (2024). Multilingual SEIR public opinion propagation model with social enhancement mechanism and cross transmission mechanisms. Scientific Reports, 14(1), 31081.
  • dos Santos Filho, E. C. (2024). Avaliação de Políticas Públicas de Segurança no Brasil: aplicando o Modelo Baseado em Agentes. Editora Dialética.
  • Dragomir, O. E., & Dragomir, F. (2025). A Decentralized Hierarchical Multi-Agent Framework for Smart Grid Sustainable Energy Management. Sustainability, 17(12), 5423.
  • Dubey, P. K., Singh, B., Gautam, A. K., Singh, D., & Tripathi, M. N. (2025). A State of the Art of Recent Trends in Electric Vehicles Planning. Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling, 159-176.
  • Duschl, R. A., Bateman, K. M., & Maeng, S. (2024). and Scott McDonald. Handbook of Research on Science Learning Progressions, 233.
  • Eakin, H., Bojórquez-Tapia, L. A., Miquelajauregui, Y., Grave, I., Aguilar, B. H., & Janssen, M. A. (2024). Using exploratory modeling to challenge narratives of risk governance in Mexico City. Proceedings of the National Academy of Sciences, 121(36), e2313191121.
  • Ege, E., & Ajibade, F. (2024). Evaluating the Impact of Barging Activity on Road Decongestion in Apapa Lagos Nigeria. UNILAG Journal of Transport and Logistics, 1(1), 73-82.
  • Ehret, M., Johnston, W. J., & Ritter, T. (2024). From buying centers to buying ecosystems: Advancing the B2B research journey. Industrial Marketing Management.
  • Ekanayake‐Weber, M., Mathew, N., Cunha, D., Payen, N., Grimm, V., & Koenig, A. (2024). It's about time: Feeding competition costs of sociality are affected more by temporal characteristics than spatial distribution. Ecology and Evolution, 14(4), e11209.
  • Ekström, H., Droste, N., & Brady, M. (2024). Modelling forests as social-ecological systems: A systematic comparison of agent-based approaches. Environmental Modelling & Software, 105998.
  • El‐Dalahmeh, M., El‐Dalahmeh, A., & Adeel, U. (2024). Analysing the performance of AODV, OLSR, and DSDV routing protocols in VANET based on the ECIE method. IET Networks.
  • Eloy, A., Wagh, A., Fuhrmann, T., de Deus Lopes, R., & Blikstein, P. (2024, July). Towards Convergence: Characterizing Students’ Design Moves in Computational Modeling Through Log Data with Video and Cluster Analysis. In International Conference on Artificial Intelligence in Education (pp. 413-421). Cham: Springer Nature Switzerland.
  • Elsheikh, A. (2024). Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences. arXiv preprint arXiv:2403.04417.
  • Emami, S., & Dehghanisanij, H. (2024). Fault Tree Analysis of Trade-Offs between Environmental Flows and Agricultural Water Productivity in the Lake Urmia Sub-Basin Using Agent-Based Modeling. Water, 16(6), 844.
  • Emami, S., Dehghanisanij, H., & Hajimirzajan, A. (2024). Agent-based simulation model to evaluate government policies for farmers’ adoption and synergy in improving irrigation systems: A case study of Lake Urmia basin. Agricultural Water Management, 294, 108730.
  • Encinas, D. O., Matto, J., Stella, J., Maccallini, L., & Romero, F. (2024). Dengue Dynamics: Modelling Spread and Environmental Interactions. Journal of Computer Science and Technology, 24(2), e14-e14.
  • Engel, A. (2024). Systems Science for Engineers and Scholars. John Wiley & Sons.
  • Engesvik, S. L., Lindstøl, A., & Meinich, A. H. (2024). Addressing agents' strategies in a cyberwar game (CyberAIMs) (Bachelor's thesis, NTNU).
  • Eramo, R., Nolletti, M., Pomante, L., Pasquale, L., & Pascucci, D. (2024). Model‐driven engineering for simulation models interoperability: A case study in space industry. Software: Practice and Experience.
  • Eshrati, B., Rimaz, S., Yaghoobi, M., Effati, S., Jabbari Nooghabi, M., & Tajzadeh, P. (2024). Modeling and Simulation of the possible routes of transmission of Acinetobacter spp. in the Intensive Care Units: An Agent-Based Computational Study. Iranian Journal of Medical Microbiology, 5-5.
  • Espinoza, F. (2024). Time as a Field: Envisioning Its Qualitative and Quantitative Aspects. In Educational Research and the Question (s) of Time (pp. 559-579). Singapore: Springer Nature Singapore.
  • Estrada-Jimenez, L. A., Kalateh, S., Nikghadam-Hojjati, S., & Barata, J. (2024). An Altruistic-based Framework to Support Collaborative Healing of Manufacturing Resources in a Self-organized Shop-floor. IEEE Access.
  • Fabrin, B. H., Ferrari, D. B., Arraut, E. M., & Neumann, S. (2024). Towards balancing efficiency and customer satisfaction in airplane boarding: An agent-based approach. Operations Research Perspectives, 100301.
  • Faccenda, G., Bogani, A., & Sacchi, S. (2024.) Modelli basati su agenti per lo studio del cambiamento climatico: L’apporto della ricerca psicologica. Sistemi intelligenti, 1-17.
  • Fan, G., Zhang, C., Wang, K., Li, Y., Chen, J., & Xu, Z. (2024). CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System. arXiv preprint arXiv:2406.19720.
  • Fan, W., Shan, C., Wang, S., Liu, T., Wang, F., Meng, X., ... & Liu, Y. (2024). Major Research Fruits. In Unconventional Emergency Management Research (pp. 41-135). Springer, Singapore.
  • Fard, G. G. (2024). An Integrated Experimental-Modeling Approach to Communication and Adaptation in Honeybees (Doctoral dissertation, University of Colorado at Boulder).
  • Farrand, J., Mulyatno, I. P., & Adietya, B. A. (2024). ANALISA EVAKUASI PENUMPANG DENGAN METODE PENDEKATAN SIMULASI BERDASARKAN ATURAN IMO MSC. 1/CIRC. 1533 PADA KAPAL PENYEBRANGAN PENUMPANG 5000 GT. Jurnal Teknik Perkapalan, 12(2).
  • Farris, A. V., & McLaughlin, G. (2024). Getting a grip on how we talk about computational practices in science in settings of teacher learning. Journal of Computer Assisted Learning.
  • Fernandes, R. S., & Miranda, J. G. V. (2024). An agent-based model for studying the temperature changes on environments exposed to magnetic fluid hyperthermia. Computers in Biology and Medicine, 170, 108053.
  • Fernandes, Y., & Abosata, N. (2024). Analysing India's Cyber Warfare Readiness and Developing a Defence Strategy. arXiv preprint arXiv:2406.12568.
  • Ferrando, A., Gatti, A., & Mascardi, V. (2024). Geometric and Spatial Reasoning in BDI Agents: a Survey.
  • Ferreira12, E., Rete, O., & de Janeiro–RJ–Brasil, R. (2024). Simulaçao de Rotatitividade de Pessoal de TI através de um Sistema Multiagente: Desenvolvimento e Aplicaçoes. In Este documento reúne os trabalhos apresentados nas sessões técnicas da 18ª edição do Workshop-Escola de Sistemas de Agentes, Seus Ambientes e Aplicações–Wesaac 2024, realizado na Universidade de Brasília, entre 14 e 16 de agosto de 2024. Durante as sessões técnicas, foram apresentados 21 artigos que (p. 73).
  • Finger, R., Henningsen, A., Höhler, J., Huber, R., Rommel, J., & Grebitus, C. (2024). Open Science in Agricultural Economics. Q Open, qoae029.
  • Finnemann, A., Borsboom, D., Waldorp, L., Marsman, M., & van der Maas, H. L. (2024). A theory construction methodology for network theories in psychology.
  • Flandre, N. Y., & Giabbanelli¹, P. J. (2024). Can Large Language Models Learn Conceptual Modeling by Looking at Slide. In Advances in Conceptual Modeling: ER 2024 Workshops, AISA, CMLS, EmpER, QUAMES, JUSMOD, LLM4Modeling, Pittsburgh, PA, USA, October 28–31, 2024, Proceedings (p. 198). Springer Nature.
  • Flores Briceño, F. (2024). Variantes del modelo de Deffuant para dinámica de opinión en poblaciones estructuradas (Master's thesis).
  • Fluri, L., Yilmaz, A., Bieri, D., Ankenbrand, T., & Perucca, A. (2024). Simulating Liquidity: Agent-Based Modeling of Illiquid Markets for Fractional Ownership. arXiv preprint arXiv:2411.13381.
  • Fogo, G. (2024). Investigating the Interplay of Mitochondrial Dynamics and Proteostasis during Neuronal Ischemia/Reperfusion Injury using Integrated Imaging and Modeling Approaches (Doctoral dissertation).
  • Fonseca, L. L., Böttcher, L., Mehrad, B., & Laubenbacher, R. C. (2024). Metamodeling and Control of Medical Digital Twins. arXiv preprint arXiv:2402.05750.
  • Fortu, Z. J. T. F., Guevarra, L. M. L., Ang, M. R. C. O., & Vergara, K. A. P. (2024). AGENT-BASED MODELING OF UP DILIMAN INTRACAMPUS PEDESTRIAN MOBILITY USING NETLOGO AND GIS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 235-241.
  • Foster, T., Clegg, R. J., Alden, K., & Smets, B. F. (2024). Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0.
  • Franzén, L. K., Lovaco, J., & Villas, F. (2024). SOTA Holistic SoS Engineering. Delft University of Technology (TUD), 23, 01.
  • French, M., Patel, A., Qureshi, A., Saxena, D., & Sengupta, R. (2024a). Agent-based modeling of COVID-19 transmission: A case study of housing densities in Sankalitnagar, Ahmedabad. ISPRS International Journal of Geo-Information, 13(6), 208.
  • Frydenlund, E., Martínez, J., Padilla, J. J., Palacio, K., & Shuttleworth, D. (2024). Modeler in a box: how can large language models aid in the simulation modeling process?. SIMULATION, 00375497241239360.
  • Fuentes, N., Schmutzler, J., & Vargas, A. M. (2024). Unpacking the Multilayered Nature of Entrepreneurial Ecosystems: A Conceptual Complex Adaptive System Model. International Journal of Innovation Studies.
  • Fuhrmann, T., Rosenbaum, L., Wagh, A., Eloy, A., Wolf, J., Blikstein, P., & Wilkerson, M. Right but wrong: How students' mechanistic reasoning and conceptual understandings shift when designing agent‐based models using data. Science Education.
  • Gadsden, G. I., Ferraro, K. M., & Harris, N. C. (2024). Energy efficient homes for rodent control across cityscapes. Environmental Research Letters, 19(8), 084027.
  • Gajewski, Z., McElmurray, P., Wojdak, J., McGregor, C., Zeller, L., Cooper, H., ... & Hopkins, S. (2024). Nonrandom foraging and resource distributions affect the relationships between host density, contact rates and parasite transmission. Ecology Letters, 27(3), e14385.
  • García-Bouza García, L. A. (2024). Causas del hundimiento de Albert Rivera tras las elecciones del 10-N (2019): ideología, discurso y estrategia política del líder de ciudadanos en twitter.
  • Garcia, J. M. V. (2024). Modelagem baseada em agentes (ABM) para estudo dos efeitos da fecundidade e da longevidade na diversidade genética de populações biológicas (Doctoral dissertation, Universidade de São Paulo).
  • Garibay, O. O., & Garibay, I. Agent-Based Modeling of C. Difficile Spread in Hospitals: Assessing Contribution of High-Touch vs. Low-Touch Surfaces and Inoculations’ Containment Impact. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 173). Springer Nature.
  • Garzón López, M. M. Metaheurística inspirada en Inteligencia Colectiva humana para problemas de optimización numérica continua.
  • Garzón, M., Álvarez-Pomar, L., & Rojas-Galeano, S. A. (2024). From Collective Intelligence to Global Optimisation: An Agent-based Model Approach.
  • Gaudou, B., & Adam, C. (2024, November). Une enquête sur les perceptions de la mobilité pour informer un simulateur multi-agent de choix modal. In Les 32èmes Journées Francophones sur les Systèmes Multi-Agents (JFSMA 2024).
  • Gavidia-Calderon, C., Kordoni, A., Bennaceur, A., Levine, M., & Nuseibeh, B. (2024). The IDEA of Us: An Identity-Aware Architecture for Autonomous Systems. ACM Transactions on Software Engineering and Methodology.
  • Gehlen, M. A., & Ronzani, G. M. R. (2025). Setting Airport Boarding Strategies Based on Passengers’ Operational Data through Machine Learning Techniques. Journal of Aerospace Technology and Management, 17.
  • Gervasi, V., & Guberti, V. (2024). The Effect of Partial and Temporary Vaccination on African Swine Fever Eradication Rates. Transboundary and Emerging Diseases, 2024.
  • Ghaitaranpour, A., Koocheki, A., & Mohebbi, M. (2024). Multi-agent simulation of doughnut deep fat frying considering two domain heating media and sample flipping. Current Research in Food Science, 100751.
  • Ghashghaei, M. T., Abad, A. A. M., & Taleghani, M. (2024). Presenting a model for explaining the effect of internal and external organizational decision components on the final price of industrial products with a factor-based approach. Journal of Value Creating in Business Management, 3(4), 225-270.
  • Ghoreishi, M., & Lindenschmidt, K. E. (2024). Unlocking effective ice-jam risk management: Insights from agent-based modeling and comparative analysis of social theories in Fort McMurray, Canada. Environmental Science & Policy, 157, 103731.
  • Ghoreishi, M., Das, A., & Lindenschmidt, K. E. (2024). Advancement in ice-jam flood risk management: Integrating dynamic adaptive behavior by an agent-based modeling in Fort McMurry, Canada. Journal of Hydrology, 131236.
  • Giabbanelli, P. J. (2024). Fuzzy Cognitive Maps: Best Practices and Modern Methods. Springer Nature.
  • Ginters, E., Dumpis, U., Liñán, L. C., Eroles, M. A. P., Nazemi, K., Matvejevs, A., & Estrada, M. A. R. (2024). A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks.
  • Gispert Segués, S. (2024). Modelització i estudi de patrons de mobilitat amb agents. Cas aplicat a Terrassa (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Glick, A. R., Jones, C., Martignetti, L., Blanchette, L., Tova, T., Henderson, A., ... & Li-Jessen, N. Y. (2024). An integrated empirical and computational study to decipher help-seeking behaviors and vocal stigma. Communications Medicine, 4(1), 228.
  • Golmaryami, S., Nunes, M. L., & Ferreira, P. (2024). The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach. Smart Energy, 100138.
  • Gomes, B. D. C. M., & Wehrmeister, M. A. (2024). Análise de Escalabilidade de Método de Exploração e Mapeamento Bioinspirado no Formato das colmeias de Abelha. Anais do Computer on the Beach, 15, 270-276.
  • Gomes, O. The Agent ID Model: A Multidisciplinary Framework of Evolutionary Human Behavior. Springer Nature.
  • Gomes, O., de Moraes, M. L., & Fagandini, P. The Agent ID Model.
  • Gomez, P., & Swarts, M. Spatiotemporal Modeling Vertically Integrated Project.
  • Gonzalez-Redin, J., Gordon, I. J., Polhill, J. G., Dawson, T. P., & Hill, R. (2024). Navigating Sustainability: Revealing Hidden Forces in Social–Ecological Systems. Sustainability, 16(3), 1132.
  • González-Silva, M. I., & González-Silva, R. A. (2024). Cooperation Dynamic through Individualistic Indirect Reciprocity Mechanism in a Multi-Dynamic Model. Computation, 12(2), 20.
  • Gooijer, N. J. C. Entity-based System Dynamics for Bridge Asset Management.
  • Graefe, C. M. (2024). New Spaces: On Territoriality in an Expansive Era (Doctoral dissertation, University of Minnesota).
  • Grande Avalos, F. J. Modelado basado en agentes para el estudio de la dinámica poblacional de Ctenocephalides felis en ambientes domésticos. Renałi.
  • Greybosh, C. (2024). Bringing Computational Modeling into the Classroom with Custom Block-Based Programming Languages in StarLogo Nova (Doctoral dissertation, Massachusetts Institute of Technology).
  • Grgicevic, L., Coates, E. M., Bye, R. T., Fossen, T. I., & Osen, O. L. (2024). Towards Decision Support in Vessel Guidance Using Multi-Agent Modelling.
  • Grieg, C., Berea, A., Hammer, M., Arguello, E., Petri, C., Prather, C., & Carter, S. (2024, November). An Exploration of Mars Settlement with Agent Based Modeling. In 2024 World Conference on Complex Systems (WCCS) (pp. 1-5). IEEE.
  • Grisales, J. A. C., Ceballos, Y. F., Bastidas-Orrego, L. M., Gómez, N. I. J., & Cañola, E. C. (2024). Development of an Agent-Based Model to Evaluate Rural Public Policies in Medellín, Colombia. Sustainability, 16(18), 1-19.
  • Gritter, K., Dobbin, M., Merrill, E., & Lewis, M. (2024). An individual-based movement model for contacts between mule deer (Odocoileus hemionus). Ecological Complexity, 58, 101082.
  • Groeneveld, J., Martinovič, T., Rossi, T., Salamon, O., & Grimm, V. (2024). Prototype Biodiversity Digital Twin: Honey Bees in Agricultural Landscapes. ARPHA Preprints, 5, e124639.
  • Groeneveld, J., Odemer, R., & Requier, F. (2024). Brood indicators are an early warning signal of honey bee colony loss—a simulation-based study. PloS one, 19(5), e0302907.
  • Grospietsch, F., Stinken-Rösner, L., Renger, A., Krell, M., & Lenzer, S. (2024). Barrieren beim Betreiben naturwissen-schaftlicher Erkenntnisgewinnung und wie ihnen mit Konzepten partizipations-förderlicher Digitalisierung begegnet werden kann. MNU Journal, 4.
  • Guesmi, H. A. (2024). A Trust-Driven Optimization of Role-Based Access Control in E-Health Cloud Systems. Journal of Computing & Biomedical Informatics.
  • Güngör, Ö., Günneç, D., Salman, S., & Yücel, E. (2024). Simulation of migration paths using agent-based modeling: The case of Syrian refugees en route to Turkey. Socio-Economic Planning Sciences, 102089.
  • Guo, Q., He, Y., Chen, Y., & Qiao, C. (2024). Design and application of computational modeling in science education research: a systematic review. Interactive Learning Environments, 1-24.
  • Guo, Z. (2024). Exploring urban mobility and sustainability from a complexity perspective.
  • Gürcan, Ö., Szczepanska, T., & Antosz, P. (2024). A Guide to Re-Implementing Agent-based Models: Experiences from the HUMAT Model. arXiv preprint arXiv:2405.03994.
  • Gwon, N., Jeong, W., Kim, J. H., Oh, K. H., & Jun, J. K. (2024). Effects of Intervention Timing on Health-Related Fake News: Simulation Study. JMIR Formative Research, 8(1), e48284.
  • Hahn, U., Assaad, L., & Burton, J. W. (2024). Opinion Averaging versus Argument Exchange. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).
  • Hahn, U., Merdes, C., & von Sydow, M. (2024). Knowledge through social networks: Accuracy, error, and polarisation. Plos one, 19(1), e0294815.
  • Haki, K., Tanriverdi̇, H., Safaei, D., Schmid, M., Aier, S., & Winter, R. (2024). Generativity and Profitability on B2B Innovation Platforms: A Simulation-based Theory Development. Management Information Systems Quarterly, 48(2), 583-612.
  • Hakim, G., Braun, R., & Lipman, J. (2024). Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks. Electronics, 13(11), 2072.
  • Hamann, H. The Two-Bridge Ant Experiment as an Interactive NetLogo Library Model. LNCS 14987 Swarm Intelligence, 255.
  • Han, J., Tan, Q., Ding, Y., & Liu, Y. (2024). Exploring the diffusion of low-carbon power generation and energy storage technologies under electricity market reform in China: An agent-based modeling framework for power sector. Energy, 133060.
  • Han, M., & Uhm, J. (2024). Enhancing Students’ Emotional, Epistemic, and Conceptual Aspects in Scientific Practice through Computational Modeling on a Food Web. International Journal of Science and Mathematics Education, 1-21.
  • Han, S., Lim, J., Hwang, M., & Heo, G. (2024). Enhancing Radiological Emergency Response through Agent-Based Model Case 1: Effectiveness of staged Evacuation.
  • Hardjana, A. C. (2024). Analisis Tipografi Dalam Konten Instagram@ Disbudparkotasemarang Sebagai Penguatan City Branding Semarang Simpul Ekonomi Jawa. Nirmana, 43-49.
  • Hare, M. P., Roxburgh, N., Salt, D., & Polhill, G. (2024). Barriers and Model Curation Issues Associated with Rapid Adaptation of Empirical Legacy ABM in Response to Urgent Policy Maker Queries. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 271). Springer Nature.
  • Hartman, C. R. A., Wilkinson, G. S., Razik, I., Hamilton, I. M., Hobson, E. A., & Carter, G. G. (2024). Hierarchically embedded scales of movement shape the social networks of vampire bats. Proceedings of the Royal Society B, 291(2021), 20232880.
  • Haryanto, F. T., Koshimura, S., & Mas, E. (2024). Effectiveness of tsunami temporary evacuation sites (TES) in pangandaran village using agent-based modeling. Synopsis of IISEE-GRIPS Master’s Thesis, Bulletin of IISEE, 59.
  • Hashemi, S. M., Kazemi, M. A. A., Ashlaghi, A. T., & Minooie, M. (2024). The combination of genetic algorithm in the optimization of the stock portfolio in the financial decision of investors. Journal of Value Creating in Business Management, 3(4), 72-88.
  • Hassanpour, S., González, V. A., Zou, Y., Liu, J., & Cabrera-Guerrero, G. (2024). Agent-based post-earthquake evacuation simulation to enhance early-stage architectural layout and non-structural design. Automation in Construction, 165, 105541.
  • Hatlauf, J., Kunz, F., Griesberger, P., Sachser, F., & Hackländer, K. (2024). A stage-based life cycle implementation for individual-based population viability analyses of grey wolves (Canis lupus) in Europe. Ecological Modelling, 491, 110700.
  • Hay, T., & Signer, J. (2024) Simulationsbasierte Evaluierung von Dichteschätzern für unmarkierte Tiere Simulation-based evaluation of density estimators for unmarked animals. Wildbiologische Forschungsberichte, 215.
  • Hazari, T. A., Khalil Ullah, M. I., Pareno, S. I., & Assuad, C. S. A. (2024). A Study on Circular Fashion: Profitability and Environmental Impact Analysis. In EcoDesign for Sustainable Products, Services and Social Systems II (pp. 59-73). Singapore: Springer Nature Singapore.
  • Hazra, I., Weiner, M., Yang, R., Chattejee, A., Southgate, J., Groth, K., & Azarm, S. (2024). Prognostics and Health Management of Unmanned Surface Vessels: Past, Present, and Future. Journal of Computing and Information Science in Engineering, 1-19.
  • He, S., Chen, J., Zhang, P., & Fu, Z. (2024, October). Multi-source Trust Model Based on Blockchain and IoT Edge Task Collaboration. In 2024 IEEE 49th Conference on Local Computer Networks (LCN) (pp. 1-7). IEEE.
  • Head, B. (2024). Agents Modeling Agents: The Design and Analysis of Multi-level Agent-Based Models (Doctoral dissertation, Northwestern University).
  • Held, F., & Wilkinson, I. (2024). Collaborating with Managers to Develop Flight Simulators to Address Complex Problems Using Agent-Based Models. Collaborative Research Design: Working with Business for Meaningful Results, 367.
  • Henriques, J. E. (2024). Sophisticated Agent Modeling: Language Design and Implementation for Large-Scale Simulation (Master's thesis, Universidade do Porto (Portugal)).
  • Herbillon, F., Diouf, E. G., Brévault, T., Haramboure, M., Fellous, S., & Piou, C. (2024). Life history traits of the target pest and transmission routes of the biocide are critical for the success of the boosted Sterile Insect Technique. Current Research in Insect Science, 100101.
  • Herrera Salgado, C. A. (2024). Impacto del liderazgo y transformacional en seguridad y el coaching sobre la cultura de seguridad en el sector minero en Antioquia.
  • Hidalgo, Á. L. N., Valbuena, S. B., & Robayo, D. Á. (2024). Las organizaciones como proceso evolutivo de conocimiento: perspectivas para su aplicación en el estudio del narcotráfico. Revista Criminalidad, 66(2).
  • Higuera, J. R. B., Higuera, J. B., Montalvo, J. A. S., & Crespo, R. G. (2024). Unsupervised Approaches in Anomaly Detection. In Machine Learning for Cyber Physical System: Advances and Challenges (pp. 57-83). Cham: Springer Nature Switzerland.
  • Hofmann, H., & Deutsch, S. Web and App Engineering (T4INF1201). Modulhandbuch.
  • Hong, P. G. (2024). Analysis of Consumer Transaction Behavior on Secondhand Goods Trading Platforms Using an Agent-Based Model. 디지털콘텐츠학회논문지 (J. DCS), 25(7), 1823-1831.
  • Hong, Q. (2024). Development of planning support systems: A systematic review of the Chinese language literature. Transactions in Urban Data, Science, and Technology, 27541231241251409.
  • Hoskinson, J. S., Davison, D. R., Sanders, H., Jiménez-Marín, B., & Michod, R. E. (2024). Translating research on evolutionary transitions into the teaching of biological complexity. II. A NGSS-aligned framework for teaching the hierarchy of life. Evolution: Education and Outreach, 17(1), 1-20.
  • Hossain, M. R., Abou-Senna, H., Amon, M. J., & Knox, D. (2024). Contextual Effects to Modulate Sign Adherence: Moderating Driver Behavior in Response to Creative versus Traditional Dynamic Message Signs under Varying Driving Conditions. Interdisciplinary Journal of Signage and Wayfinding, 8(2), 21-35.
  • Hosseini, S. Z., Radfar, R., Nasiripour, A. A., & Ghatary, A. R. (2024). Machine Learning Algorithms to Prevent the Spread of Infectious Diseases based on Effective Features in the Diagnosis of Covid-19. Iranian Journal of Information Processing and Management, 39(2), 657-698.
  • Howell, W. J., Dong, Z., & Rojas-Cessa, R. (2024). EOS: Impact Evaluation of Electric Vehicle Adoption on Peak Load Shaving Using Agent-Based Modeling. Energies, 17(20), 5110.
  • Hrab, M. Action Using Agent-Based Modeling. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 56). Springer Nature.
  • Hsu, H. M., & Gourbesville, P. Cellular Automata and Agent-Based Modelling for Inundation Simulation and Disaster Management. Advances in Hydroinformatics—SimHydro 2023 Volume 1: New Modelling Paradigms for Water Issues, 333.
  • Hsu, Y. Y., & Han, H. (2024). Toward volumetric urbanism: Analysing the spatial-temporal dynamics of 3D floor space use in the built environment. Environment and Planning B: Urban Analytics and City Science, 23998083241286592.
  • Hu, L., & Wang, H. (2024). Unplugged Activities in the Elementary School Mathematics Classroom: The Effects on Students’ Computational Thinking and Mathematical Creativity. Thinking Skills and Creativity, 101653.
  • Hu, X., Hong, Y., Cui, Z., Xie, T., & Fu, W. (2024). Evaluating Classical Airplane Boarding Methods Focusing on Higher-Risk Passengers during Post-Pandemics. Transportation Research Record, 03611981241247179.
  • Hu, X., Yang, Z., Sun, J., & Zhang, Y. (2024). When should capital-constrained swap service providers partner with battery lessors?. Transportation Research Part E: Logistics and Transportation Review, 190, 103692.
  • Hua, L. (2024). The impact of environmental taxation on the structure and performance of industrial symbiosis networks: An agent-based simulation study. Heliyon, 10(3), e25675.
  • Huanachin Ttito, H. L. (2024). Implementación de comunidades de aprendizaje profesionales en CETPRO de la UGEL 06: percepciones y desafíos.
  • Huang, H., Sun, B., & Hu, L. (2024). A task Offloading Approach Based on Risk Assessment to Mitigate Edge DDoS Attacks. Computers & Security, 103789.
  • Huang, J., Wang, J., Gong, Y., Xu, N., Zhou, Y., Zhu, L., ... & Zhou, Y. Identification of Optimum Scopes of Environmental Drivers for Schistosome-Transmitting Oncomelania Hupensis Using Agent-Based Model in Dongting Lake Region, China.
  • Huang, K., De Long, J. R., Yan, X., Wang, X., Wang, C., Zhang, Y., ... & Guo, H. (2024). Why are graminoid species more dominant? T rait‐mediated plant–soil feedbacks shape community composition. Ecology, e4295.
  • Huber, R., Kreft, C., Späti, K., & Finger, R. (2024). Quantifying the importance of farmers' behavioral factors in ex-ante assessments of policies supporting sustainable farming practices. Ecological Economics, 224, 108303.
  • Hui, H., Feng, L., & Zhang, R. Chongqing University of Technology, Chongqing, China zrj@ cqut. edu. cn. In Spatial Data and Intelligence: 5th China Conference, SpatialDI 2024, Nanjing, China, April 25–27, 2024, Proceedings (p. 199). Springer Nature.
  • Huo, F. Y., Manrique, P. D., Restrepo, D. J., Woo, G., & Johnson, N. F. (2024). Simple fusion-fission quantifies Israel-Palestine violence and suggests multi-adversary solution. arXiv preprint arXiv:2409.02816.
  • Hussain, I., Elomri, A., Kerbache, L., & El Omri, A. (2024). Smart city solutions: Comparative analysis of waste management models in IoT-enabled environments using multiagent simulation. Sustainable Cities and Society, 105247.
  • Iapăscurtă, V. (2024). Agent-based modeling: revolutionizing tissue engineering. Cells and tissues transplantation. Actualities and perspectives. The 2-nd edition. Chisinau, March 29-30th 2024.
  • Iapascurta, V. (2024). Modeling the human circulatory system using system dynamics: possibilities, benefits, and practical applications.
  • IAPĂSCURTĂ, V. (2024). NetLogo integrated development environment for modeling physiological processes.
  • Ibrahimli, U., Hemmrich, S., Zauke, S., & Winkelmann, A. Overcoming Lemon Markets with Business Reputation Ecosystem–A Multi-agent Simulation on Monetary Ratings.
  • Idrissa, S. M., Kadri, C., Bachir, M. I., & Naroua, H. (2024). Computer Simulation models of infectious diseases: Advantages and Limits. International Journal of Science and Research Methodology, 27(7), 10–40.
  • Imanian Ardabily, M., Noghani Dokht Bahmani, M., & Asgharpour Masouleh, A. R. (2024). science production. Ferdowsi University of Mashhad Journal of Social Sciences.
  • Ismail, S. (2024). Universities as Complex Systems: Data, Power Laws, and an Agent-Based Model (Doctoral dissertation, George Mason University).
  • Ismail, S., Mostafa, S. A., Baharum, Z., Erianda, A., Jaber, M. M., Jubair, M. A., & Adiya, M. H. (2024). Software Agent Simulation Design on the Efficiency of Food Delivery. JOIV: International Journal on Informatics Visualization, 8(1), 190-197.
  • Ivanjek, L., Perl-Nussbaum, D., Solvang, L., Yerushalmi, E., & Pospiech, G. (2024). Enhancing Mathematization in Physics Education by Digital Tools. In Physics Education Today: Innovative Methodologies, Tools and Evaluation (pp. 35-53). Cham: Springer Nature Switzerland.
  • Izmirlioglu, Y., Pham, L., Son, T. C., & Pontelli, E. (2024). A Survey of Multi-Agent Systems for Smartgrids. Energies (19961073), 17(15).
  • Izquierdo Millán, L. R., Izquierdo Millán, S. S., & Sandholm, W. H. (2024). Agent-Based Evolutionary Game Dynamics.
  • Jackson, M. H. (2024). Modeling of Small Groups in Computational Sciences: A Prospecting Review. Small Group Research, 10464964241279164.
  • Jager, W., & Wang, S. Simulations as a Dialogue Tool: Strengthening Community Engagement and Local Democratic Processes. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 107). Springer Nature.
  • Jalal, J. K., Al-sofy, K. M., Fadhil, F. F., & Mahmood, B. Adaptive routing strategy for emergency applications in smart campus.
  • Jalali, H., Aghamohammadi, H., Vahidnia, M. H., & Mirzahosseini, A. Modeling the effects of PM pollutant on people using a spatio-temporal dynamic model and GIS. Journal of Geomatics Science and Technology, 0-0.
  • Jamshidnejad, A., & De Schutter, B. (2024). A combined probabilistic-fuzzy approach for dynamic modeling of traffic in smart cities: Handling imprecise and uncertain traffic data. Computers and Electrical Engineering, 119, 109552.
  • Janssen, S. D., Viaene, K. P., Van Sprang, P., & De Schamphelaere, K. A. (2024). Modeling Full Life‐Cycle Effects of Copper on Brook Trout (Salvelinus fontinalis) Populations. Environmental Toxicology and Chemistry.
  • Jaramillo, M. L., & Andrade, M. C. Herramientas digitales para la enseñanza de la programación en el ámbito educativo.
  • Jebelli, N. B. (2024). Computational Modeling of the Social Complexity in GÖBekli Tepe and Earliest Neolithic Communities (Doctoral dissertation, George Mason University).
  • Jetton, C., Hoyle, C., & Campbell, M. I. (2024). Overcoming Design Challenges in Coupled System Device Problems Using Agent-Based. Design Computing and Cognition’24: Volume 2, 259.
  • Jiang, J., Wu, H., Xu, R., Yang, H., Zhang, Y., & Li, J. A Node Protection Method For Improving The Resilience Of Combat System Under Continuous Attack.
  • JIMÉNEZ, N. H., AGUDELO, A. M. M., & BOTERO, S. L. HÉCTOR OLASOLO BEATRIZ GOENA VIVES.
  • Johnson, K., Vermeer, W., Hills, H., Chin-Purcell, L., Barnett, J., Burns, T., ... & Brown, C. H. (2024). Model-Driven Decision Support: A Community-Based Meta-Implementation Strategy to Predict Population Impact. Annals of Epidemiology.
  • Jonnala, R., Liang, G., Yang, J., & Alsmadi, I. (2024). Using Large Language Models in Public Transit Systems, San Antonio as a case study. arXiv preprint arXiv:2407.11003.
  • Joshi, A. R., Chappin, E. J. L., & Doorn, N. (2024). Equity, Urgency, Affordability.
  • Josserand, M., Allassonnière‐Tang, M., Pellegrino, F., Dediu, D., & de Boer, B. (2024). How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents. Cognitive Science, 48(4), e13439.
  • Jun, W., & Ran, X. Q. (2024). Dynamics in Digital Finance and Its Impact on SME Financing. Heliyon, 10(9), E30586.
  • Jung, Y., Qian, C., Barnett-Neefs, C., Ivanek, R., & Wiedmann, M. (2024). Developing an Agent-Based Model that Predicts Listeria spp. Transmission to Assess Listeria Control Strategies in Retail Stores. Journal of Food Protection, 100337.
  • Junior, G. T., de Macedo, W. C., Santos, B. L., Coelho, J. V. R., & Neto, V. V. G. Modelagem e Simulaçao de Evacuaçoes de Emergência em Estádios: Uma Abordagem Baseada em Agentes utilizando NetLogo.
  • Juretić, D., & Bonačić Lošić, Ž. (2024). Theoretical Improvements in Enzyme Efficiency Associated with Noisy Rate Constants and Increased Dissipation. Entropy, 26(2), 151.
  • Kafai, Y., & Morales-Navarro, L. (2024). Twenty Constructionist Things to Do with Artificial Intelligence and Machine Learning. arXiv preprint arXiv:2402.06775.
  • Kamali, A., Niksokhan, M. H., & Ardestani, M. (2024). Multi-agent system simulation and centralized optimal model for groundwater management considering evaluating the economic and environmental effects of varied policy instruments implemented. Journal of Hydroinformatics, jh2024152.
  • Kang, B., Kumar, H., Lee, M., Chakraborty, B., & Mukhopadhyay, S. (2024). Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction. arXiv preprint arXiv:2404.06460.
  • Kapadia, K., Yongsatianchot, N., Marsella, S., John, Richard. (2024). Integrating Actual Human Behavior into an Agent-Based School Shooting Simulation. Proceedings of the 2024 Winter Simulation Conference. H. Lam, E. Azar, D. Batur, S. Gao, W. Xie, S. R. Hunter, and M. D. Rossetti (Eds.).
  • Kappenberger, J., & Stuckenschmidt, H. (2024). A framework for human-centered AI-based public policies. Human-Centered AI, 287.
  • Karalidis, K., Roumpos, C., Servou, A., Paraskevis, N., & Pavloudakis, F. (2024). Post-mining planning under the prism of land use alternatives: a cellular automata model. Environmental Earth Sciences, 83(13), 413.
  • Karalla, S. A., & Petropoulou, I. (2024). A review on educational robotics and creativity. Global Journal of Engineering and Technology Advances, 20(01), 078-084.
  • Karolemeas, C., Tsigdinos, S., Moschou, E., & Kepaptsoglou, K. (2024). Shared autonomous vehicles and agent based models: a review of methods and impacts. European Transport Research Review, 16(1), 1-45.
  • Karrenberg, C., Berglund, E. Z., & Edwards, E. C. An Agent-Based Modeling Approach to Assess the Social Equity Impacts of Dynamic Pricing Forurban Water Management. Available at SSRN 5002637.
  • Kassa, Y. W., James, J. I., & Belay, E. G. (2024). Cybercrime Intention Recognition: A Systematic Literature Review. Information, 15(5), 263.
  • Katsamakas, E., & Sanchez-Cartas, J. M. (2024). Congestion, network effects and platform competition. Journal of Economic Interaction and Coordination, 1-25.
  • Kaur, M., & Verma, V. K. (2024). Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things. Journal of Network and Computer Applications, 103872.
  • Kaur, P., Ciuti, S., Salter-Townshend, M., & Farine, D. R. (2024). Using an agent based model to inform sampling design for animal social network analysis. bioRxiv, 2024-05.
  • Kazieva, V., Große, C., & Larsson, A. (2024). Towards a unified regional vision: simulation to foster cross-municipal cooperation and planning. Insights into Regional Development, 6(4), 54-81.
  • Kazymyr, V. (2024). Modeling of Defensive Drone Swarms with NetLogo. Mathematical Modeling and Simulation of Systems, 377.
  • Kemei, Z., Rotich, T., & Bitok, J. Modelling Population Dynamics Using Age-Structured System Of Partial Differential Equations.
  • Khairunnisa, G., Dewi, O. C., & Alkadri, M. F. (2024, September). Implementation of Green Behavior in Educational Building Through Human-based Retrofits Based on Agent-Based Modeling (ABM). In IOP Conference Series: Earth and Environmental Science (Vol. 1395, No. 1, p. 012023). IOP Publishing.
  • Khajeem Moghadam, A., Saghafian, B., Najarchi, M., & Delavar, M. (2024). Optimizing Stakeholder Behavior for Groundwater Management in Arid and Semi-arid Climates: A Multi-agent System Approach in Karaj Plain, Iran. Iranian Journal of Science and Techno
  • Khanna, A., Rousell, N., Davis, T., Zhang, Y., Sheeler, D., Cioe, P., ... & Kahler, C. (2024). Social network dynamics of tobacco smoking and alcohol use among persons involved with the criminal legal system (PCLS): A modeling study. International Journal of Alcohol and Drug Research.
  • Khasraghi, G. S., & Nejat, A. (2024). Utilizing Agent-Based Modeling for Optimization of Wayfinding in Hospital: A Case Study. HERD: Health Environments Research & Design Journal, 19375867241248593.
  • Khatami, S., & Frantz, C. (2024). Prompt Engineering Guidance for Conceptual Agent-based Model Extraction using Large Language Models. arXiv preprint arXiv:2412.04056.
  • Khazaei, S., & Najafiani, M. (2024). Evaluation of Emergency Evacuation in Residential High-Rise Buildings Communities (Case Study: Punak Town of Zanjan). Emergency Management.
  • Khazaii, J., Khazaei, A., Khayyam, H., & Jazar, R. N. (2024). Evolutionary Game Theory and Innovative Building Strategies. In Nonlinear Approaches in Engineering Application: Automotive Engineering Problems (pp. 283-294). Cham: Springer Nature Switzerland.
  • Kheirinejad, S., Bozorg-Haddad, O., Savic, D., Singh, V. P., & Loáiciga, H. A. (2024). Developing a National-Scale Hybrid System Dynamics, Agent-Based, Model to Evaluate the Effects of Dietary Changes on the Water, Food, and Energy Nexus. Water Resources Management, 1-26.
  • Kickmeier-Rust¹, M. D., Pasieka, M., & Baumgartner¹, M. Herding Cats: Assessing Complex Procedural and Tacit Knowledge with Multi-agent. In Methodologies and Intelligent Systems for Technology Enhanced Learning, 14th International Conference (p. 255). Springer Nature.
  • Kilic, O., & Tsang, A. (2024, May). Catfished! Impacts of Strategic Misrepresentation in Online Dating. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (pp. 1011-1019).
  • Kim, G., Park, J., & Heo, G. (2024). Enhancing Radiological Emergency Response through Agent-Based Model Case 2: Time Required for Staff Assemble.
  • Kim, H. (2024). Exploring mobile OTT service user's psychological resistance toward wireless mobile caching networks and telecommunication operator's incentive policy, using agent-based model scenarios.
  • Kim, J., Conte, M., Oh, Y., & Park, J. (2024). From Barter to Market: an Agent-Based Model of Prehistoric Market Development. Journal of Archaeological Method and Theory, 1-40.
  • Kim, S., Fattori, G., & Park, Y. (2024). A simple and efficient Distributed Trigger Counting algorithm based on local thresholds. ICT Express.
  • Kimberly, J. (2024). Simulation Software and Frameworks: There are various software tools and frameworks, such as NetLogo, Mesa.
  • Kimpton, L., Challenor, P., & Salter, J. (2024). Uncertainty Quantification for Agent Based Models: A Tutorial. arXiv preprint arXiv:2409.16776.
  • King, B. (2024) Using Systems Human Factors and Ergonomics to Identify and Mitigate the Risks Associated with Future Invasive Brain-Computer Interfaces.
  • Kirac, E., Shaltayev, D., & Wood, N. (2024). Evaluating the Impact of Citizen Collaboration with Government Agencies in Disaster Response Operations: An Agent-Based Simulation Study. International Journal of Disaster Risk Reduction, 104469.
  • Knapp, A. C., Cruz, D. A., Mehrad, B., & Laubenbacher, R. C. (2024). Ensemble Kalman filter methods for agent-based medical digital twins. bioRxiv, 2024-05.
  • Kongkhunthod, N., Praneetpolgrang, P., & Sirinam, P. (2024). ระบบ ตรวจ จับ การ บุกรุก ทาง ไซเบอร์ แบบ เรี ยล ไท ม์ ด้วย การ เรียน รู้ ของ เครื่อง สำหรับ เสริม สร้าง ความ แข็งแกร่ง และ มี ประสิทธิภาพ ด้าน การ รักษา ความ มั่นคง ปลอดภัย ไซเบอร์ ใน องค์กร. Journal of Engineering and Digital Technology (JEDT), 12(2), 95-110.
  • Koning, E., & Gropp, W. Proposal for a Flexible Benchmark for Agent Based Models.
  • Kooijman, S. A. L. M. (2024). Ways to reduce or avoid juvenile-driven cycles in individual-based population models. Ecological Modelling, 490, 110649.
  • Kopels, M. C., & Ullah, I. I. (2024). Modeling post-Pleistocene megafauna extinctions as complex social-ecological systems. Quaternary Research, 1-14.
  • Kosak, O., Kastenmüller, P., Wanninger, C., & Reif, W. (2024, October). An Approach for Extended Swarm Formation Flight with Drones: PROTEASE 2.0. In International Symposium on Leveraging Applications of Formal Methods (pp. 263-280). Cham: Springer Nature Switzerland.
  • Köster, T., Reinhardt, O., Hinsch, M., Bijak, J., & Uhrmacher, A. M. (2024). A Fast Embedded Language for Continuous-Time Agent-Based Simulation. Journal of Artificial Societies and Social Simulation, 27(1), 10.
  • Kraeuter, K., Reith, C., Kolber, B. J., & Miller Neilan, R. (2024). Improved Efficiency and Sensitivity Analysis of 3-D Agent-based Model for Pain-related Neural Activity in the Amygdala. Spora: A Journal of Biomathematics, 10(1), 65-82.
  • Kramberger, T., & Abdelshafie, A. (2024) Simulation-optimization Model In Managing The Empty Container Movements Problem Through Repositioning Strategies.
  • Krasilenko, V. G., Nikitovich, D. V., & Lazarev, A. A. (2024). Modeling nodes and cells of neuron-equivalentors as accelerators of equivalental-convolutional self-learning neural structures. Modeling, control and information technologies.№ 7: 247–250.
  • Kreig, J. A., Lenhart, S., Ponce, E., & Jager, H. I. (2024). Agent-based modeling to evaluate the effects of harvesting biomass and hunting on ring-necked pheasant (Phasianus colchicus) populations. Ecological Modelling, 492, 110705.
  • Kremer-Herman, N., Gupta, A., & Severson, E. R. (2024). Blueprints for Machine Ethics: A Digital Terrarium for Socio-Ethical Artificial Agent Decisionmaking. IEEE Access.
  • Kritikou, A. I., Nikolaidi, M. E., & Chavianidis, T. (2024). The role of mobile applications in managing everyday stress through breathing. GSC Advanced Research and Reviews, 20(1), 283-299.
  • Kruijning, D. (2024). From policy to action: residents’ responses to a top-down rainwater retention policy in Groningen (Doctoral dissertation).
  • Kuipers, E. (2024). Integrated field-& agent-based modelling: understanding the dynamics of the common barbel in the Meuse river (Master's thesis).
  • Kuljanin, G., Braun, M. T., Grand, J. A., Olenick, J. D., Chao, G. T., & Kozlowski, S. W. (2024). Advancing Organizational Science With Computational Process Theories. The Leadership Quarterly, 101797.
  • Kumar, H., Chakraborty, B., Kang, B., & Mukhopadhyay, S. (2024). Studying the Impact of Stochasticity on the Evaluation of Deep Neural Networks for Forest-Fire Prediction. arXiv preprint arXiv:2402.15163.
  • Kumar, L. (2024). RISK MITIGATION IN CLOUD-BASED IDENTITY MANAGEMENT SYSTEMS: BEST PRACTICES.
  • Kumar, V., Mistri, A., Jain, V., & Ghosh, M. (2024). Computational Engineering for 3D Bioprinting: Models, Methods, and Emerging Technologies. 3D Bioprinting from Lab to Industry, 301-322.
  • Kumari, A. (2024). Fashion Business and Digital Transformation (Impact of Digital Marketing When Styles Meet Screens). BFC Publications.
  • Kuo, P. F., Wen, T. H., Chuang, T. W., Chiu, C. S., Ye, Y. J., & Putra, I. G. B. (2024). Comparing micro-level and macro-level models for epidemic diffusion in the metro system. Journal of Simulation, 1-14.
  • Kurchyna, V. (2024). Check for updates Can (and Should) Automated Surrogate Modelling Be Used for Simulation Assistance? In Multi-Agent-Based Simulation XXIV: 24th International Workshop, MABS 2023, London, UK, May 29–June 2, 2023, Revised Selected Papers (p. 3). Springer Nature.
  • Kürschner, T., Scherer, C., Radchuk, V., Blaum, N., & Kramer‐Schadt, S. (2024). Resource asynchrony and landscape homogenization as drivers of virulence evolution: The case of a directly transmitted disease in a social host. Ecology and Evolution, 14(2), e11065.
  • Kyrychok, T., Klymenko, T., & Bardovskyi, B. (2024, January). Nanoscale fractal analysis of watermarked paper surface topography studied by atomic force microscopy. In Sixteenth International Conference on Correlation Optics (Vol. 12938, pp. 174-177). SPIE.
  • Laili, Y., Gong, J., Hu, K., Zhang, L., & Wang, F. (2024, August). Model Calibration for Agent-Based Simulation Using a Pattern Clustering Network. In Asia Simulation Conference (pp. 152-164). Singapore: Springer Nature Singapore.
  • Lais, R. S., Fitzner, J., Lee, Y. K., & Struckmann, V. (2024). Open-sourced modeling and simulating tools for decision-makers during an emerging pandemic or epidemic–Systematic evaluation of utility and usability: A scoping review update. Dialogues in Health, 100189.
  • Lakmali, R. G. N., Genovese, P. V., & Abewardhana, A. A. B. D. P. (2024). Evaluating the Efficacy of Agent-Based Modeling in Analyzing Pedestrian Dynamics within the Built Environment: A Comprehensive Systematic Literature Review. Buildings, 14(7), 1945.
  • Lamrani Alaoui, Y., El Fakir, A., & Tkiouat, M. (2024). A New Crowdfunding Model for Financial Inclusion Based on Donations and Interest-Free Debt: A Fuzzy Agent-Based Simulation Approach. International Journal of Islamic Finance and Sustainable Development, 16(4), 63-90.
  • Lan, H., Yang, Y., Fu, H., Liao, H., Liao, L., Huang, S., & Li, X. (2024). Dynamic disaster risk assessment of urban waterlogging on pedestrian flow by intelligent simulation of hydrodynamics coupled with agent-based models in Chao-yang river basin of Nanning, China. Stochastic Environmental Research and Risk Assessment, 1-24.
  • Langbeheim, E., Saba, J., & Levy, S. T. (2024). Invented models–relating students’ constructions of computational models to their learning gains. Interactive Learning Environments, 1-16.
  • Lanza, E. Predicción de Latencia en Microservicios con Modelos de Deep Learning. CACIC2024.
  • Laouar, W., Redjimi, K., & Redjimi, M. (2024). Towards an agent-based wireless sensor network simulator for hierarchical routing protocols. South Florida Journal of Development, 5(12), e4776-e4776.
  • Larson Jr, J. R., Cornell, C. A., & Aramovich, N. P. (2024). Building Better Theories: Prediction Intervals as a Tool for Theory Testing and Improvement. Basic and Applied Social Psychology, 1-23.
  • Lavaud, R., Marn, N., Domingos, T., Filgueira, R., Lika, K., Rakel, K., & Klanjšček, T. (2024). Metabolic organization across scales of space and time. Ecological Modelling, 110951.
  • Leal Martinez, D. (2024). Design and optimization of a decentralized multi-robot exploration behavior taking into account energy constraints.
  • Ledda, M., Pluchino, A., & Ragusa, M. (2024). Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach.
  • Lee, H. S., Gweon, G. H., & Pallant, A. (2024). SupportingSimulation-MediatedScientific InquirythroughAutomatedFeedback. Uses of Artificial Intelligence in STEM Education, 198.
  • Lee, I., Perret, B., DeLisi, J., Minocha, S., Peterson, K., & Malyn-Smith, J. (2024). The Decoding Approach For Integrating Computational Thinking Into High School Science Classrooms. In Edulearn24 Proceedings (pp. 5058-5067). IATED.
  • Lee, L. Y. (2024). Taxing Tobacco Companies To Close The Interracial Wealth Gap: An Alternative Policy to Address Compensation for Racial Discrimination.
  • Lemmen, C., Hokamp, S., Örey, S., & Scheffran, J. (2024). Viable North Sea (ViNoS): A NetLogo Agent-based Model of German Small-scale Fisheries. Journal of Open Source Software, 9(95), 5731.
  • Lengruber, H. (2024). Estratégias pedagógicas e recursos educacionais para o ensino de computação para alunos com deficiência visual: uma revisão sistemática de literatura.
  • León, J., Martínez, C., Inzunza, S., Ogueda, A., & Urrutia, A. (2024). Improving Tsunami Risk Analysis by Integrating Spatial Resolution and the Population’s Evacuation Capacities: A Case Study of Cartagena, Chile. International Journal of Disaster Risk Science, 1-16.
  • León, J., Ogueda, A., Hurtado, L., Gubler, A., & Zamora, N. (2024). An integrated framework for analysing horizontal and vertical tsunami evacuation. A case study of Iquique, Chile. International Journal of Disaster Risk Reduction, 105137.
  • Leonard-Duke, J., Agro, S. M., Csordas, D. J., Bruce, A. C., Eggertsen, T. G., Tavakol, T. N., ... & Peirce, S. M. (2024). Multi-Scale Computational Model of Microvascular Remodeling in Idiopathic Pulmonary Fibrosis. bioRxiv, 2024-03.
  • Li, J., Feng, Z., Zhang, W., Zhu, D., & Huang, Z. (2024). Evolution of green travel behaviour on dynamic social networks. Travel Behaviour and Society, 37, 100866.
  • Li, J., Niu, J., & Wang, J. (2024). Agent-based modelling and simulation of elderly residents’ dynamic usage patterns in neighbourhood squares during summer. Building and Environment, 111598.
  • Li, J., Yuan, P., Liang, L., & Cao, J. (2024). Enhancing Supply Chain Resilience in Prefabricated Buildings: The Role of Blockchain Technology in Volatile, Uncertain, Complex, and Ambiguous Environments. Buildings, 14(9), 3006.
  • Li, N. (2024). Analyzing the Complexity of Public Opinion Evolution on Weibo: A Super Network Model. Journal of the Knowledge Economy, 1-36.
  • Li, X., Xu, Y., Zhang, Y., & Malthouse, E. C. (2024). Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures. arXiv preprint arXiv:2410.13909.
  • Liang, H., Qian, Y., Zhu, M., & Chen, Y. (2024). Exploring the role of social networks in modeling drivers’ route choice behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 104, 154-169.
  • Liang, L., Phan, H., & Giabbanelli, P. J. (2024). Experimental evaluation of a machine learning approach to improve the reproducibility of network simulations. SIMULATION, 00375497241229753.
  • Liang, Y., Wang, X., & Zheng, Q. (2024, December). Establishment and Research of Multi-agent Based Multi-Departmental Collaboration Model. In 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024) (pp. 455-461). Atlantis Press.
  • Lim, J., & Johnson, C. (2024). Judgmental Forecasting Uses Agent-Based Modeling and Simulation to Minimize Risks and Losses in Decision-Making. Jurnal Ilmiah Teknik Industri, 278-285. https://doi.org/10.23917/jiti.v23i02.4486
  • Lim, J., Vogel-Heuser, B., & Kovalenko, I. (2024). Large Language Model-Enabled Multi-Agent Manufacturing Systems. arXiv preprint arXiv:2406.01893.
  • Lin, C. Y., OA, M., Dhakal, S., Zipper, S., & Marston, L. (2024). Pychamp: A Crop-Hydrological-Agent Modeling Platform for Groundwater Management. Available at SSRN 4814225.
  • Lin, H., Zheng, H., & Wang, F. (2024). The influence of school climate on Chinese primary school students’ bystander behavior in bullying: A variable-and person-centered dual-perspective analysis. Psychology of Violence.
  • Lin, J. H., Quan, Y. J., & Han, B. P. (2024). MetaIBM: A Python-based library for individual-based modelling of eco-evolutionary dynamics in spatial-explicit metacommunities. Ecological Modelling, 492, 110730.
  • Lin, L., Wei, X., & Chen, Y. (2024). Evolution of Policy-Driven Ecosystem of Original Innovation Talents. International Journal of Innovation Studies.
  • Lin, S., Wang, K., & Liu, X. Y. (2024, November). Analyzing Cascading Outbreak of GameStop Event: A Practical Approach Using Network Analysis and Large Language Models. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 428-436).
  • Listopad, S., Matsoula, V., & Luchko, A. (2024). Modeling reflection in artificial intelligence systems: state of art and prospects. In ITM Web of Conferences (Vol. 59, p. 04005). EDP Sciences.
  • Liu, J. (2024). Marketing strategy matching algorithm under Artificial Intelligence. 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE).
  • Liu, K., Jiang, G., Cheng, X., Hu, X., & Che, C. (2024). Strategic Cooperation in Ride-Hailing: Analyzing Operational Efficiency in San Francisco.
  • Liu, S. H., Weber, E. S., Manz, K. E., McCarthy, K. J., Chen, Y., Schüffler, P. J., ... & Tracy, M. (2024). Assessing the Impact and Cost-Effectiveness of Exposome Interventions on Alzheimer’s Disease: A Review of Agent-Based Modeling and Other Data Science Methods for Causal Inference. Genes, 15(11), 1457.
  • Liu, W., Meng, Q., Zhi, H., Li, Z., & Hu, X. (2024). A review of agent-based modeling in construction management: an analytical framework based on multiple objectives. Journal of Civil Engineering and Management, 30(3), 200-219.
  • Liu, X., Tan, Z., Liang, L., & Li, G. (2024). A Multidimensional Trust Evaluation Mechanism for Improving Network Security in Fog Computing. IEEE Transactions on Industrial Informatics.
  • Liu, X., Zhang, L., Zhen, J., & Wang, W. (2024). Planning for service space of medium-and long-term shelters based on multi-agent evacuation simulation. Natural Hazards, 1-28.
  • Liu, Y., Zhou, Y., Yang, L., & Xin, Y. (2024). Simulating staff activities in healthcare environments: An empirical multi-agent modeling approach. Journal of Building Engineering, 108580.
  • Liu, Z., Gearty, Z., Richard, E., Orrill, C. H., Kayumova, S., & Balasubramanian, R. (2024). Bringing computational thinking into classrooms: a systematic review on supporting teachers in integrating computational thinking into K-12 classrooms. International Journal of STEM Education, 11(1), 51.
  • Liu, Z., Wang, Z., Chen, L., Tang, T., & Shen, Z. (2024). An Agent-Based Approach to Simulating Evacuation in Elderly Care Facilities with Disabled Older Adults. International Journal of Disaster Risk Reduction, 104815.
  • Lopate, M. Z. (2024). Complexity and Great Power Decline (Doctoral dissertation, The Ohio State University).
  • López Rivera, Y. M. Un modelo estocástico en cadenas de suministro agroalimentaria resilientes con alcance de exportación. Caso de estudio aguacate Hass (Doctoral dissertation, Universidad Nacional de Colombia).
  • López-Vargas, J. C., Meisel, J. D., Cárdenas-Aguirre, D. M., & Medina, P. (2024). Coordination mechanisms applied to logistical systems for local disaster preparedness: a Latin American case. Journal of Humanitarian Logistics and Supply Chain Management.
  • López, A. B., Pastor-Galindo, J., & Ruipérez-Valiente, J. A. (2024). Frameworks, Modeling and Simulations of Misinformation and Disinformation: A Systematic Literature Review. arXiv preprint arXiv:2406.09343.
  • Lopolito, A., Caferra, R., Nigri, A., & Morone, P. (2024). An evaluation of the impact of mitigation policies on health and the economy by managing social distancing during outbreaks. Evaluation and Program Planning, 103, 102406.
  • Louis, V., Page, S. E., Tansey, K. J., Jones, L., Bika, K., & Balzter, H. (2024). Tiger Habitat Quality Modelling in Malaysia with Sentinel-2 and InVEST. Remote Sensing, 16(2), 284.
  • Lovaco, J. (2024). Wildfire Management as a System of Systems: Systems Engineering Considerations Using Agent-Based Simulation and Large Language Models (Doctoral dissertation, Linköping University Electronic Press).
  • Lovaco, J., Munjulury, R. C., Staack, I., & Krus, P. (2024). LARGE LANGUAGE MODEL-DRIVEN SIMULATIONS FOR SYSTEM OF SYSTEMS ANALYSIS IN FIREFIGHTING AIRCRAFT CONCEPTUAL DESIGN. In 34th Congress of the International Council of the Aeronautical Sciences, 2024.
  • Lu, H. C., & Lee, H. W. (2024). Agents of Discord: Modeling the Impact of Political Bots on Opinion Polarization in Social Networks. Social Science Computer Review, 08944393241270382.
  • Lu, J., Tan, T., Sun, Z., & Jiang, C. (2024). Passenger flow simulation of hub airports incorporating interactive chain-based guidance. Journal of Simulation, 1-20.
  • Lu, X. S., Guo, R. Y., Huang, H. J., & Wang, S. H. (2024). Potential-based dynamic parking navigation for autonomous vehicles: Near-priority vs. distant-priority. Transport Policy.
  • Luo, X., & Du, L. (2024, July). Energy consumption simulations of rural residential buildings considering differences in energy use behavior among family members. In Building Simulation (pp. 1-24). Tsinghua University Press.
  • Lynch, A. R., Bradford, S., Zhou, A. S., Oxendine, K., Henderson, L., Horner, V. L., ... & Burkard, M. E. (2024). A survey of chromosomal instability measures across mechanistic models. Proceedings of the National Academy of Sciences, 121(16), e2309621121.
  • Ma, Y., Hu, S., Li, X., Wang, Y., Liu, S., & Cheong, K. H. (2024). Students Rather Than Experts: A New AI For Education Pipeline To Model More Human-Like And Personalised Early Adolescences. arXiv preprint arXiv:2410.15701.
  • Mack, D. October Surprises: Threshold Dynamics. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 77). Springer Nature.
  • Maddah, N., & Heydari, B. (2024). Building back better: Modeling decentralized recovery in sociotechnical systems using strategic network dynamics. Reliability Engineering & System Safety, 110085.
  • Magallanes, J. M. (2025). 10 Computational Representation of Social Complexity for Decision-Making. AI, Analytics and Strategic Decision-Making, 286.
  • Mahajan, S. (2024). Navigating the cohesion-diversity trade-off: understanding the role of facilitators in co-creation using agent-based modelling. Philosophical Transactions A, 382(2285), 20240093.
  • Mahajana, S. Navigating the Cohesion-Diversity Tradeoff: Understanding the Role of Facilitators in Co-Creation Using ABM.
  • Maharani, S. N., & Rahmawati, S. A. (2024). Financial Revolution through Agent-based Artificial Simulation Computational Models for Predicting Market Behavior. KnE Social Sciences, 624-640.
  • Maiwald, J., & Schütte, T. (2024). Prosumer Impact on Cellular Power Systems. Energies, 17(9), 2195.
  • Makarenya, T. A., Mannaa, A. S., Kalinichenko, A. I., & Petrenko, S. V. Cognitive Modeling as a Forecasting Tool. International Journal on Smart Sensing and Intelligent Systems, 17(1).
  • Malanson, G. P., Dullinger, S., Pauli, H., Winkler, M., Saccone, P., & Jiménez‐Alfaro, B. (2024). Species richness responds to buffer effectiveness and competitive priorities in simulations of alpine microrefugia. Journal of Biogeography.
  • Malikov, M. Rationality in Mixed Environments. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 243). Springer Nature.
  • Malliaros, P., Pacheco-Jaramillo, A., & Robertson-Dean, M. (2024). Are in-kind transfer vouchers as flexible and efficient as cash?.
  • Mangold-Döring, A., Buddendorf, W. B., van den Brink, P. J., & Baveco, J. M. (2024). How relevant are temperature corrections of toxicity parameters in population models for environmental risk assessment of chemicals?. Ecological Modelling, 498, 110880.
  • Mannan, S., Fukuda, M., Stiber, M., & Si, D. (2024). Analysis and Improvement of MASS-based GIS.
  • Mansury, Y. (2024). I Won’t Get Caught: An Agent-Based Model of Corruption with Incomplete Information. In The Indonesian Economy and the Surrounding Regions in the 21st Century: Essays in Honor of Iwan Jaya Azis (pp. 277-298). Singapore: Springer Nature Singapore.
  • Manzi, D., & Calderoni, F. (2024). The resilience of drug trafficking organizations: Simulating the impact of police arresting key roles. Journal of Criminal Justice, 91, 102165.
  • Manzo, G. (2024). Antecedents of generative thinking in analytical sociology: the contribution of Tom Fararo1. The Journal of Mathematical Sociology, 1-22.
  • Marcum-Dietrich, N. I., Bruozas, M., Becker-Klein, R., Hoffman, E., & Staudt, C. (2024). Precipitating Change: Integrating Computational Thinking in Middle School Weather Forecasting. Journal of Science Education and Technology, 1-18.
  • Marino Carvalho, V. (2024). Towards a Reconciliation Between Simulational, Ludic, and Historiographical Practices in Research. Simulation & Gaming, 10468781241248705.
  • Mariz, M. (2024). Combining System Dynamics and Agent-Based Approaches to Model Urban Mobility (Master's thesis, Universidade do Porto (Portugal)).
  • Marques, A. T., Crispim-Mendes, T., Palma, L., Pita, R., Moreira, F., & Beja, P. (2024). Using individual-based demographic modelling to estimate the impacts of anthropogenic mortality on territorial predators. Ecological Modelling, 493, 110752.
  • Masaeli, Y., & Ahmadi, A. (2024). Assessing the suitability of groundwater resources based on farmers’ predicted intentions and human–water feedbacks. Journal of Hydrology, 132057.
  • Mashood, K. K., & Chandrasekharan, S. (2024). The learning of modeling. In The Routledge Handbook of Philosophy of Scientific Modeling (pp. 412-426). Routledge.
  • Maslova, I. I., Manolov, A. I., Glushchenko, O. E., Kozlov, I. E., Tsurkis, V. I., Popov, N. S., ... & Ilina, E. N. (2024). Limitations in creating artificial populations in agent-based epidemic modeling: a systematic review. Journal of microbiology, epidemiology and immunobiology, 101(4), 530-545.
  • Matsuda, M., & Nishi, T. (2024). Enterprise E-Profiles for Construction of a Collaborative Network in Cyberspace. In Enterprise Interoperability X: Enterprise Interoperability Through Connected Digital Twins (pp. 75-86). Cham: Springer International Publishing.
  • Matsui¹, K., & Hashimoto, T. Construction of a Simulation Model Regarding People's Reactions to the Stay-at-Home Request Policy During the COVID-19 Pandemic in Japan and Evaluation Using Linear Regression Analysis. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 89). Springer Nature.
  • Matyjaszkiewicz, A., & Sharpe, J. (2024). LimbNET: collaborative platform for simulating spatial patterns of gene networks in limb development. bioRxiv, 2024-08.
  • Maupin, C. K., Mohan, G., Choudhury, A., & Deepak, P. Network-based approaches to leadership: An organizing framework, review, and recommendations.
  • Mazhar Gharamaleki, M., & Elahi, N. (2024). Investigating the effect of tax on money on macroeconomic variables in a closed economy using the agent-based general equilibrium model. The Journal of Economic Policy.
  • Mazzetto, S. (2024). Interdisciplinary Perspectives on Agent-Based Modeling in the Architecture, Engineering, and Construction Industry: A Comprehensive Review. Buildings, 14(11), 3480.
  • Medina-Labrador, M., Marroquin-Ciendua, F., Bonelo, J. G., Gómez, D. M., & Betancourt, M. V. P. Obstacles, Social Norms and Communication to Improve Service in Mass Transport. An Analysis in Bogotá’s Bus Rapid Transit System.
  • Meister, H., Meister, G., & Grycko, E. (2024). Does the Nash Solution Concept persist for Intelligent Agents in a Dynamic Game Environment?.
  • Meseguer, G. O., & Serrano, J. L. (2024). Implementation and training of primary school teachers in computational thinking: a systematic review. Revista Iberoamericana de Educación a Distancia, 27(1), 255-281.
  • Meskini, F. Z., & Aboulaich, R. (2024). Insurance Based on Waqf and Blockchain Technology: A Strong Social Impact & Efficiency. WSEAS Transactions on Business and Economics, 21, 741-752.
  • Mesquita, R. P., Leal, F., & De Queiroz, J. A. (2024). DIGITAL TWINS IN THE RETAIL INDUSTRY: A SYSTEMATIC LITERATURE REVIEW.
  • Metzcar, J. (2024). Toward a General Framework of Multicellular Systems Biology with Application to Cellular Motility and Phenotype Control (Doctoral dissertation, Indiana University).
  • Micheletti, T., Wimmler, M. C., Berger, U., Grimm, V., & McIntire, E. J. (2024). Beyond guides, protocols and acronyms: Adoption of good modelling practices depends on challenging academia's status quo in ecology. Ecological Modelling, 496, 110829.
  • Michelini, M., Polizzi, E., & Vilone, D. (2024). Bad Polarization in Structurally Cohesive Communities.
  • Miller Neilan, R. (2024). BODINE: Enhancing NetLogo3D Simulations: Computational Efficiency and Online Accessibility of a Pain Model.
  • Milne, R. J., Cotfas, L. A., Delcea, C., Crăciun, L., & Molănescu, A. G. (2024). Airplane Seating Assignment Greedy Algorithms that Separate Passengers Likely to be Susceptible to Infectious Disease from those Likely to be Infectious. IEEE Access.
  • Miranda, R. D. S. V. (2024). Moderates and consensus formation in the Deffuant model (Doctoral dissertation, Universidade de Sã o Paulo).
  • Mlika, F., Karoui, W., & Romdhane, L. B. (2024). Blockchain solutions for trustworthy decentralization in social networks. Computer Networks, 110336.
  • Mo, C. Y. J. (2024). Design of Reinforcement Learning Control in Agent-Based Modeling: An Investigation of Systemic Risks in Interbank Lending Market (Doctoral dissertation, Stevens Institute of Technology).
  • Mobinizadeh, M., Mohammadshahi, M., Aboee, P., Fakoorfard, Z., Olyaeemanesh, A., & Mohamadi, E. (2024). The Application of System Simulation in the Health Sector: A Rapid Review. Decision Making in Healthcare Systems, 11-17.
  • Moehler, M., & Thrasher, J. (2024). New Approaches to Social Contract Theory: Liberty, Equality, Diversity, and the Open Society. Oxford University Press.
  • Mohamed, Y. I., Khaleed, M., Ehab, S., Hassan, O. A., Fares, H. M., & Mohamed, O. A. Modeling and simulation of tumor growth.
  • Mohammadi, M., & Gharakhani, S. (2024). Simulating Institutional Change for Governing Natural Common-Pool Resources: An Agent-Based Model Approach. Research Square.
  • Mohands, N., Bayhan, S., Sanfilippo, A., & Rub, H. A. (2024). Decentralized PV Energy Trading: A Case Study of Residential Households in Qatar. IEEE Access.
  • Moncada, J. A., Sanjab, A., & Delarue, E. The framing of decision-making under uncer-tainty may shape the adoption patterns of solar PV panels.
  • Montgomery, V. A., Wood‐Yang, A. J., Styczynski, M. P., & Prausnitz, M. R. (2024). Feasibility of engineered Bacillus subtilis for use as a microbiome‐based topical drug delivery platform. Bioengineering & Translational Medicine, e10645.
  • Morales, D., Canessa, E., & Chaigneau, S. E. (2024). An Agent-Based Model of Foraging in Semantic Memory. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).
  • Moreira, F., Dias, F. S., Dertien, J., Hasse, A. C., Borda-de-Água, L., Carvalho, S., ... & Fernández, N. (2024). Guidelines for connectivity conservation and planning in Europe. ARPHA Preprints, 5, e129021.
  • Mosiakova, I., Levchenko, F., Kalinina, L., Rogoza, V., & Pelekh, V. (2024). Transforming education for the STEM era. Amazonia Investiga, 13(79), 123-140.
  • Mudrinić, V. (2024). Primjena sustava Replit u osnovnoškolskoj nastavi informatike u domeni računalnog razmišljanja i programiranja (Doctoral dissertation, University of Pula. Faculty of Informatics in Pula).
  • Muhammad, R. F., & Kasahara, S. (2024). Agent-based simulation of fake news dissemination: the role of trust assessment and big five personality traits on news spreading. Social Network Analysis and Mining, 14(1), 75.
  • Mühlenbernd, R. (2024). How to use Evolutionary Game Theory to study evolutionary aspects of grammar. Language change for the worse, 281.
  • Muir, E. J. (2024). Exploring the Swimming Behavior of a Hydromechanical Copepod in the Context of Positive Density Dependence (Doctoral dissertation, University of South Florida).
  • Mukaimine, W. Causal Relationship between Larval Resource Competition and Imaginal Reproductive Interference (Doctoral dissertation, School of Science and Technology, University of Tsukuba).
  • Mulder, K., Heierhoff, H., Lee, S. M., Tsou, J. J. Y., & Chen, W. (2024). Simulation of Polymer Fractal Formation Using a Triangular Network Growth Model. Langmuir.(2024). S
  • Murgatroyd, P., & Gaffney, V. Modelling the Logistics of Mantzikert.
  • Murphy, D., Duncan, R. G., Chinn, C. A., Danish, J., Hmelo-Silver, C. E., Ryan, Z., ... & Stiso, C. Students’ Prioritization of Initial and Direct Causal Mechanisms During Model Evaluation.
  • Musaeus, L. H., & Musaeus, P. (2024). Computational Thinking and Modeling: A Quasi-Experimental Study of Learning Transfer. Education Sciences, 14(9), 980.
  • Muttaqien, I. Y., Tanugraha, S., Puspitaningrum, J. E., & Manik, H. A. W. (2024). Evaluation of Emergency Access Evacuation Routes Using Agent-Based Model Application. Journal of Artificial Intelligence in Architecture, 3(2), 71-80.
  • Nabinejad, S., & Schüttrumpf, H. (2024). Agent-Based Modeling for Household Decision-Making in Adoption of Private Flood Mitigation Measures: The Upper Kan Catchment Case Study. Water, 16(14), 2027.
  • Nadieh Barnard, L. (2024). Using agent based modelling to determine behavioural rules in host location by Cotesia parasitoids (in environments with competitors and nonhosts). [Master's Thesis], Wageningen University & Research.
  • Nallur, V., Aghaei, P., & Finlay, G. (2024). Different Facets for Different Experts: A Framework for Streamlining The Integration of Qualitative Insights into ABM Development. arXiv preprint arXiv:2408.15725.
  • Nallur, V., Aghaei, P., & Finlay, G. (2024). Systematic Experimentation Using Scenarios in Agent Simulation: Going Beyond Parameter Space. arXiv preprint arXiv:2407.16294.
  • Naraghi, N., Feng, Z., Lovreglio, R., Vishnupriya, V., Wilkinson, S., & Daemei, A. B. (2024). Simulating and visualising indoor seismic damage: A systematic literature review. International Journal of Disaster Risk Reduction, 104979.
  • Neef, N. E., Egner, L. E., & Klöckner, C. A. (2024). Subsidy free-riding is positively correlated to the development of energy efficiency in the housing stock. Environment, Development and Sustainability, 1-17.
  • Nemichel, M., Timouyas, M., Regragui, Y., Bezza, H., & Laboratery, I. M. I. (2024). Dynamic Agent-Based Model for COVID-19 Epidemic Spread Using Social Network Information. In International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023): Advanced Intelligent Systems on Digital Health Technology, Volume 2 (p. 436). Springer Nature.
  • Neves, J. E. D. A. (2024). Mineração de dados aplicada a simulação de cenários complexos em sistemas multiagentes (Doctoral dissertation).
  • Nguyen, D. T. (2024). Hybrid Simulation-based Lean Management Methodology to Improve the Sustainability of the Construction Phase (Doctoral dissertation, University of Kassel).
  • Nica, I. (2024). Bibliometric mapping in the landscape of cybernetics: insights into global research networks. Kybernetes.
  • Nicholson, J., & Ridgway, J. (2024). New viruses are inevitable; pandemics are optional—Lessons for and from statistics. Teaching Statistics.
  • Niehorster-Cook¹, L., Marghetis¹, T., & Smaldino, P. Rational Voter Theory: An Agent-Based. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 258). Springer Nature.
  • Niki, M., & Kito, T. (2024). Evaluation of Micro-Level Circularity Indicators Using Agent-Based Modelling. In EcoDesign for Sustainable Products, Services and Social Systems II (pp. 205-222). Singapore: Springer Nature Singapore.
  • Nikounam Nezami, M., Toloie Eshlaghy, A., & Iranban, S. J. (2024). Modelling and simulation of supply chain resilience for unnecessary perishable foods using an agent-based modeling approach. Journal of Industrial Engineering and Management Studies, 84-97.
  • Nilsson Bruno, A., & Schüssler, M. (2024). Determining evacuation times of different stadium layouts using agent-based modelling.
  • Niu, F. Urban Spatial Evolution Simulation: Theory, Method, and Practice. Springer Nature.
  • Niu, T., Huang, H., Du, Y., Zhang, W., Shi, L., & Zhao, R. (2024). General Automatic Solution Generation of Social Problems. arXiv preprint arXiv:2401.13945.
  • Niu, T., Zhang, W., & Zhao, R. (2024). Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning. arXiv preprint arXiv:2402.02388.
  • Nourali, Z., Shortridge, J. E., Bukvic, A., Shao, Y., & Irish, J. L. (2024). Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding. Water, 16(2), 263.
  • Ntomora, P. I., & Petrakos, K. (2024). Leveraging social network analysis in education through social robots: A review. Global Journal of Engineering and Technology Advances, 20(01), 055-066.
  • Nurulloyev, F. (2024). МЕТОДИКА ОБУЧЕНИЯ ШКОЛЬНИКОВ СОВРЕМЕННЫХ ПРОГРАММНЫХ СРЕДСТВ. ЦЕНТР НАУЧНЫХ ПУБЛИКАЦИЙ, 45(45).
  • Oesterling, N., Ambrose, G., & Kim, J. (2024). Understanding the Emergence of Computational Institutional Science: A Review of Computational Modeling of Institutions and Institutional Dynamics. International Journal of the Commons, 18(1).
  • Ojha, N. K., Dubey, S. K., & Vaish, A. (2024). Computational Thinking Tools: Review and Current Status. Revolutionizing Curricula Through Computational Thinking, Logic, and Problem Solving, 151-166.
  • Oliveira, H. S. (2024). Optimizing Crowd Evacuation: Evaluation of Strategies for Safety and Efficiency.
  • Olmez, S., Heppenstall, A., Ge, J., Elsenbroich, C., & Birks, D. (2024). Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support. Journal of Simulation, 1-19.
  • Olsen, M., Kuhn, D. R., & Raunak, M. S. (2024). Explaining the impact of parameter combinations in agent-based models. Journal of Computational Science, 102342.
  • Onai, A., & Tanaka, N. (2024). Categorizing resident evacuation behaviors within a flood-protected area. International Journal of River Basin Management, 1–16.
  • Onsomu, O. N., Terciyanlı, E., & Yeşilata, B. (2024). Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources. Engineering Reports, e12995.
  • Opalski, M., Szufel, P., Kamiński, B., Mashatan, A., & Pralat, P. (2024). Optimal roadworks schedule in multi-agent transportation models. European Journal of Transport and Infrastructure Research, 24(4), 41-61.
  • Orduña Moncada, A. F. (2024). Assessing the influence of communication in an agglomeration bonus model as a strategy for land conservation (Doctoral dissertation, BTU Cottbus-Senftenberg).
  • Orrick, K., Sommer, N., Rowland, F., & Ferraro, K. Predator–prey interactions across hunting mode, spatial domain size, and habitat complexities. Ecology, e4316.
  • Osborne, J. M. (2024). An adaptive numerical method for multi–cellular simulations of tissue development and maintenance. Journal of Theoretical Biology, 111922.
  • Osorio, C., Fuster, N., Chen, W., Men, Y., & Juan, A. A. (2024). Enhancing Accessibility to Analytics Courses in Higher Education through AI, Simulation, and e-Collaborative Tools. Information, 15(8), 430.
  • Ouattara, Y., & Lang, C. (2024). Enhancing Connectivity and Energy Efficiency in Mobile Wireless Sensor Networks with SHEM. Journal of Advances in Computer Networks, 12(2).
  • Ouhssini, M., Afdel, K., Akouhar, M., Agherrabi, E., & Abarda, A. (2024). Advancements in detecting, preventing, and mitigating DDoS attacks in cloud environments: A comprehensive systematic review of state-of-the-art approaches. Egyptian Informatics Journal, 27, 100517.
  • Ouhssini, M., Afdel, K., Akouhar, M., Agherrabi, E., & Abarda, A. Egyptian Informatics Journal.
  • Palacios, J. L. D. (2024). MATHEMATICAL MODELING OF PHAGE-BACTERIA POPULATION DYNAMICS.
  • Palmeiro, L. L., Marín, A. A. L., & De las Heras Pérez, M. (2025). Strengths and Opportunities of Artificial Intelligence in Modeling, Argumentation, and Inquiry in the Experimental Science Classroom. In Utilizing ICT for Didactics of Social and Experimental Sciences (pp. 73-94). IGI Global.
  • Palmer, S., Ciubotariu, I., Ofori, R., Saenz, M., Ellison, B., & Prescott, M. P. (2024). School Nutrition Stakeholders Find Utility in MealSim: An Agent-Based Model. Journal of Nutrition Education and Behavior.
  • Pan, H., & Qian, J. (2024). Blockchain-Driven Music Copyright Distribution: An Agent-Based Modeling Perspective. International Journal of Global Economics and Management, 4(1), 594-600.
  • Pan, M., Zhao, X., & Li, Z. (2024). Diffusion mechanism of green building in industrial clusters: An agent-based modeling approach. Developments in the Built Environment, 100504.
  • Pang, J., Shi, Y., & Dai, J. (2024). An Intelligent Evaluation Method of Supply-Demand Matching Degree Based on ELECTRE III and VIKOR in Cloud Manufacturing Platform. IEEE Access.
  • Panizza, E. (2024). Modeling Flood Displacement Dynamics: Integrating Household Characteristics and Assessing Policy Implications. A study in the Khartoum State, Sudan.
  • Papadimitriou, F. (2024). Complexity, Non-Locality and Riddledness in Landscape Dynamics. In Modelling Landscape Dynamics: Determinism, Stochasticity and Complexity (pp. 119-133). Wiesbaden: Springer Fachmedien Wiesbaden.
  • Pardo, G. E., Cuevas, L. B., Pacheco‐Otalora, L. F., & Oruro, E. M. (2024). Altered Patterns of Maternal Behavior Transitions in Rats Exposed to Limited Bedding and Nesting Material Paradigm. Brain and Behavior, 14(10), e70113.
  • Park, D., Hong, J., & Ryu, D. (2024). Heterogeneous expectations in the housing market: a sugarscape agent-based model. Journal of Housing and the Built Environment, 1-25.
  • Parra-Sanchez, Á., Zorrilla-Muñoz, V., Martinez-Navarrete, G., & Fernandez, E. (2024, August). Agent-Based Modeling: Investigating Technological Perception’s Impact on Quality of Life in Older Adults with Age-Related Macular Degeneration. In The International Conference on Innovations in Computing Research (pp. 631-640). Cham: Springer Nature Switzerland.
  • Parviziomran, E., Elliot, V., & Bergqvist, R. (2024). Financing dynamics in sustainable heavy-duty road transport: An agent-based modeling approach. Transportation Research Part D: Transport and Environment, 132, 104258.
  • Patel, J. (2024). Algorithmic approaches to simulating animal movement using agent-based models.
  • Pathak, S., & Balakrishnan, P. V. (2024). The paradox of product scarcity: Catalyzing the speed of innovation diffusion. Journal of the Academy of Marketing Science, 1-21.
  • Patrick, S. M. (2024). Agent-Based Modelling for the Cost-Benefit Analysis of Adaptation Strategies: A Case Study from Inuit Nunangat. Journal of Computer Applications in Archaeology, 7(1).
  • Pecoraro, F., Accordino, F., Cecconi, F., & Paolucci, M. ABM for Simulating the Access to Elective Surgery Services: The Issue of Patient Mobility in Italy. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 73). Springer Nature.
  • Peel, A., & Arastoopour Irgens, G. (2024). Characterizing Natural Selection Contextual Transfer with Epistemic Network Analysis: A Case for Unplugged Computational Thinking. Journal of Science Education and Technology, 1-13.
  • Pektas, E. (2024). Second Grade Student Engagement in Computational Thinking.
  • Pessoa, P., Lopes, J. B., de Lima, J., Pinto, A., Søgaard Jørgensen, P., & Sá-Pinto, X. (2024). Evolutionary literacy as a catalyst for sustainable futures: connecting biological evolution education and education for sustainability. Evolution: Education and Outreach, 17(1), 1-18.
  • Pettrachin, A., Gabrielli, L., Kim, J., & Ludwig-Dehm, S. (2024). Did exposure to asylum seeking migration affect the. Computational Research in Ethnic and Migration Studies, 193.
  • Phadke, A., Medrano, F. A., Chu, T., Sekharan, C. N., & Starek, M. J. (2024). Modeling Wind and Obstacle Disturbances for Effective Performance Observations and Analysis of Resilience in UAV Swarms. Aerospace, 11(3), 237.
  • Phadke, A., Medrano, F. A., Sekharan, C. N., & Chu, T. (2024). An Analysis of Trends in UAV Swarm Implementations in Current Research: Simulation Versus Hardware. Drone Systems and Applications.
  • Pierron, M., Sueur, C., Shimada, M., MacIntosh, A. J., & Romano, V. (2024). Epidemiological Consequences of Individual Centrality on Wild Chimpanzees. American Journal of Primatology, e23682.
  • Pires, A. V., Moustapha, M., Marelli, S., & Sudret, B. (2024). Reliability analysis of non-deterministic models using stochastic emulators.
  • Plakantara, S. P., Karakitsiou, A., & Mantzou, T. Managing Risks in Smart Warehouses from the Perspective of Industry 4.0 Check for updates. Disruptive Technologies and Optimization Towards Industry 4.0 Logistics, 1.
  • Pobuda, P. (2024). El gemelo digital de la economía: Herramienta propuesta para el diseño y la evaluación de políticas. Propuestas para el Desarrollo, 8(VIII), 133-144.
  • Podworny, S., Dvir, M., Frischemeier, D., & Ben-Zvi, D. (2024). Reasoning with data models and modeling in the big data era. Universitätsbibliothek Paderborn.
  • Polhill, G., McCormick, B. J., Roxburgh, N., Assefa, S., & Matthews, K. A'Theory of the Middle Range to Support Food Security and Circular Economy Value Chain Scenario Analysis. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 177). Springer Nature.
  • Ponsiglione, A. M., Zaffino, P., Ricciardi, C., Di Laura, D., Spadea, M. F., De Tommasi, G., ... & Amato, F. (2024). Combining simulation models and machine learning in healthcare management: strategies and applications. Progress in Biomedical Engineering, 6(2), 022001.
  • Poostforoush, M. H., Monajemi, A., Daei-Karimzadeh, S., & Samadi, S. (2025). Comparative Comparison of the Efficiency of Hybrid Model of an Agent-based & Recursive Neural Network in Automating Algorithmic Trading Strategies in Global Financial Markets. International Journal of Finance & Managerial Accounting, 10(37), 209-234.
  • Popescu, D. C., & Dumitrache, I. (2024). Software Platform Based on the hLARM Formalism for Modeling Complex Systems. Romanian Journal of Information Science and Technology, 27(1), 65-80.
  • Popova, G., McCulloch, J., Ge, J., & Evans, P. (2024). D5. 3 Agent-based model at the European scale. ARPHA Preprints, 5, e144184.
  • Prajapati, S. P., Yadav, S., & Dhabolkar, S. Designing a constructionist epidemiology curriculum with Agent-based Models: An exploratory study.
  • Prasad, S. (2024). Investigating the Impact of Pandemic Scenarios on the Anesthesiology Department Healthcare Workers Availability: An Agent-Based Modeling Approach (Master's thesis, The University of North Carolina at Charlotte).
  • Prather, J., Leinonen, J., Kiesler, N., Benario, J. G., Lau, S., MacNeil, S., ... & Zingaro, D. (2024). Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools. arXiv preprint arXiv:2412.14732.
  • Prinz, A., Møller-Pedersen, B., Fischer, J., & Thalheim, B. (2024). More Cases. In Understanding Modelling and Programming (pp. 75-102). Cham: Springer Nature Switzerland.
  • Prisse, M. M. G. C. Coupling for Multi-Models.
  • Pujiono, E., Sadono, R., Imron, M. A., Sutomo, Januar, H. I., Kuswandi, R., ... & Hani, A. (2024). An agent-based model of agricultural land expansion in the mountain forest of Timor Island, Indonesia. Journal of Mountain Science, 21(7), 2263-2282.
  • Purwani, A., Budijati, S. M., & Asih, H. M. (2024). Management of battery waste recycling by electric motorbike workshops: A literature review. OPSI, 17(1), 216-233.
  • Qin, H., Liang, Q., Chen, H., & De Silva, V. (2024). A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences. Environmental Modelling & Software, 106166.
  • Qiu, J., Tan, H., Yuan, S., Lv, C., Wang, P., Cao, S., & Zhang, Y. (2024). Selection of Urban Flood Shelter Locations Based on Risk Assessment. Water-Energy Nexus.
  • Qolomany, B., Calay, T. J., Hossain, L., Mulahuwaish, A., & Bou Abdo, J. (2024). CCTFv2: Modeling Cyber Competitions. Entropy, 26(5), 384.
  • Qu, A., SHANG, Y., HAO, C., Tan, Q., & Yu, Z. The Effectiveness and Strategy Optimization of Air Pollution Control Policies Based on Abm. Available at SSRN 4974812.
  • Rabb, N. (2024). Modeling Media Influence on the Formation of Misinformed Attitudes (Doctoral dissertation, Tufts University).
  • Ragonis, N., Rosenberg-Kima, R. B., & Hazzan, O. (2024). A computational thinking course for all preservice K-12 teachers: implementing the four pedagogies for developing computational thinking (4P4CT) framework. Educational technology research and development, 1-29.
  • Rahn, S. A. (2024). Modeling and simulation of SARS-CoV-2 transmission in dynamic crowds (Doctoral dissertation, Technische Universität München).
  • Raimbault, J. (2024, July). Spatial sensitivity of the evolutionary swarm chemistry model. In ALIFE 2024: Proceedings of the 2024 Artificial Life Conference. MIT Press.
  • Rajasekaran, U., Malini, A., & Murugan, M. (2024). Artificial Intelligence in Autonomous Vehicles—A Survey of Trends and Challenges. Scrivener Publishing LLC.
  • Rajendran, V., & Ramasamy, R. K. (2024). Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things.
  • Rajkovic, K. (2024). Evaluating the performance of a self-organizing industrial symbiosis compared to a planned one: An agent-based modeling approach (Doctoral dissertation, University of Graz).
  • Raley, J. (2024). The Impact of Mathematical Justifications on Conceptual Understanding (Doctoral dissertation, University of South Carolina).
  • Ramadan, M. (2024). Solid Waste Processing Facility Improvement Using Lean Principles and Simulation Modeling.
  • Razakatiana, M., Kolski, C., Mandiau, R., & Mahatody, T. (2024). Human-Agent Team Based on Decision Matrices: Application to Road Traffic Management in Participatory Simulation. Human-Centric Intelligent Systems, 1-15.
  • Rebelato, J. G. B., Tacla, C. A., & Lugo, G. G. Abordagens multiagentes para resiliência e reestabelecimento do fornecimento de energia elétrica: Uma perspectiva logística na colaboração entre bases de atendimento durante eventos climáticos severos. In Este documento reúne os trabalhos apresentados nas sessões técnicas da 18ª edição do Workshop-Escola de Sistemas de Agentes, Seus Ambientes e Aplicações–Wesaac 2024, realizado na Universidade de Brasília, entre 14 e 16 de agosto de 2024. Durante as sessões técnicas, foram apresentados 21 artigos que (p. 27).
  • Reed, M., Berglund, E., & Montoya, B (2024). An Agent-Based Modeling Perspective of Bio-Mediated Ureolysis. In Geo-Congress 2024 (pp. 446-455).
  • Régis, C., Denis, J. L., Axente, M. L., & Kishimoto, A. (2024). Human-Centered AI: A Multidisciplinary Perspective for Policy-Makers, Auditors, and Users. CRC Press. https://doi.org/10.1201/9781003320791.
  • Ren, S. (2024). Optimization of English Classroom Interaction Models Incorporating Machine Learning. Journal of Electrical Systems, 20(6s), 1669-1681.
  • Rende, A. N. N., Yilmaz, T., & Ulusoy, Ö. (2024). Crowd: A Social Network Simulation Framework. arXiv preprint arXiv:2412.10781.
  • Rensink, J. (2024). What Are the Impacts of Leader Political Skill on Organizational Change? (Doctoral dissertation, Case Western Reserve University).
  • Reulier, R., Bonnet, G., & L’Homme, F. (2024). Comparison of execution times of three multi-agent platforms (Netlogo/GAMA/Repast simphony) based on the LASCAR model. Journal of Simulation, 1-11.
  • Reyna-González, J. E., Falcon, A. W. C., Huiman, L. V. U., Pozo, M. A. S., & Vilca, Y. M. C. (2024). Simulation of blueberry production in Lambayeque, Peru: An agent-based approach. Edelweiss Applied Science and Technology, 8(6), 8534-8544.
  • Ribeiro-Rodrigues, E., & Bortoleto, A. P. (2024). A systematic review of agent-based modeling and simulation applications for analyzing pro-environmental behaviors. Sustainable Production and Consumption.
  • Rich, P., & Genot, E. (2024). Mutual Expected Rationality in Online Sharing: An Agent-Based Model Study. Topoi, 1-15.
  • Rieder, A., Chakraborty, S., Goyal, S., & Berndt, D. J. (2024). A critical realist approach to agent-based modeling: Unlocking prediction in non-positivist paradigms. Journal of Information Technology, 02683962241280657.
  • Robayo, C. F. L., Albán, L. M. V., Sánchez, M. M. S., Paredes, J. P. S., Morales, M. A. T., Zavala, E. F. T., ... & Lagla, J. P. S. (2024). Transformación geométrica con Scratch: impacto en formación de profesores de Educación Básica. Polo del Conocimiento, 9(1), 1063-1082.
  • Roddeck, W. (2024). Wirtschaftssysteme und stochastische Prozesse. In Die Welt der Modelle und Simulationen: Wie der Mensch seit alters her versucht die Zukunft vorherzusagen (pp. 137-158). Wiesbaden: Springer Fachmedien Wiesbaden.
  • Roderick, S., Damelin, D., & Stephens, A. L. (2024). 7 Students’ systems modeling. Ways of Thinking in STEM-based Problem Solving: Teaching and Learning in a New Era, 87.
  • Rodríguez, R. A. L. (2024). Antropología y medición demográfica en el Estado multicultural colombiano. Tabula Rasa, (52), 13-43.
  • Roelofsen, R. (2025). The Influence of ACT-R’s Declarative Memory System on the HUMAT Agent-Based Model (Doctoral dissertation).
  • Rojas Scheffer, V. (2024). Application of hydrogeological models coupled with agent-based models to address sustainable groundwater management in Latin America. Hydrogeology Journal, 1-15.
  • Rojas-Domínguez, A., Martínez-Vargas, I. U., & Alvarado, M. (2024). Modeling and Simulation of Genotypic TMB and Phenotypic immunogenicity Biomarkers in Cancer Immunoediting with Ising-Hamiltonian Characterization.
  • Romano, V., Puga-Gonzalez, I., MacIntosh, A. J., & Sueur, C. (2024). The role of social attraction and social avoidance in shaping modular networks. Royal Society Open Science, 11(2), 231619.
  • Ronzani, M., & Sulis, E. (2024). Improve Hospital Management Through Process Mining, Optimization, and Simulation: the CH4I-PM Project. KI-Künstliche Intelligenz, 1-6.
  • Roos, M. (2024). Complexity Economics. In Principles of Complexity Economics: Concepts, Methods and Applications (pp. 115-146). Cham: Springer International Publishing.
  • Roos, M. (2024). Feedback, Circular Causality, and System Dynamics. In Principles of Complexity Economics: Concepts, Methods and Applications (pp. 253-338). Cham: Springer International Publishing.
  • Rouchier, J. (2024). L’éthique des modèles de simulation: une question d’interprétation?. L'Année sociologique, 74(2), 495-527.
  • Rusakov, A., & Maksimova, E. Assessment Dynamics Risks Infrastructural. In AISMA-2023: International Workshop on Advanced Information Security Management and Applications (p. 221). Springer Nature.
  • Russo, V., Andrighetto, G., Maretti, M., Polizzi, E., & Cecconi, F. (2024). Homophily and Insularity Dynamics in an Echo Chambers: Computational Models for the Study of the Conspiracy Subculture in Facebook. Social Indicators Research, 1-25.
  • Sagar, S. Towards Resilient Social IoT Sensors and Networks: A Trust Management Approach. Springer Nature.
  • Sagar, S., Mahmood, A., & Sheng, Q. Z. (2024). Understanding the Trustworthiness Management in the SIoT Network. Towards Resilient Social IoT Sensors and Networks: A Trust Management Approach, 11-49.
  • Sager, J., & Schrage, R. (2024). Mango. jl: A Julia-Based Multi-Agent Simulation Framework. Journal of Open Source Software, 9(102), 7098.
  • Saig, R., & Hershkovitz, A. (2024). Expanding Digital Literacies Beyond the Digital: Infusing Computational Thinking into Unplugged Pedagogical Tools-Two Case Studies from Mathematics Education. International Journal of Child-Computer Interaction, 100703.
  • Salas-Peña, A., & García-Palomares, J. C. (2024). Simulation of knowledge transfer in complex networks by coupling ABM and GIS: a local road freight transport system as a case study.
  • SALAS, D. U. LA LÚDICA Y SIMULACIÓN COMO HERRAMIENTAS PARA LA EDUCACIÓN AMBIENTAL ESTUDIO DE CASO: PROBLEMÁTICA DE PEQUEÑOS PRODUCTORES AGRÍCOLAS DE COLOMBIA.
  • Salazar-Serna, K., Cadavid, L., & Franco, C. (2024). Analyzing Transport Policies in Developing Countries with ABM. arXiv preprint arXiv:2404.19745.
  • Salazar-Serna, K., Cadavid, L., & Franco, C. J. (2024). Modeling Urban Transport Choices: Incorporating Sociocultural Aspects. arXiv preprint arXiv:2407.21307.
  • Salazar, A. T., Medrano, M., Medina, M. D., Roa, J., & Pesantez, J. E. (2024). Enhancing Evacuation Warning Responsiveness: Exploring the Impact of Social Interactions through an Agent-Based Model Approach.
  • Saleem, K., Wang, L., Bharany, S., Ouahada, K., Rehman, A. U., & Hamam, H. (2024). Intelligent multi-agent model for energy-efficient communication in wireless sensor networks. EURASIP Journal on Information Security, 2024(1), 9.
  • Saley, A., & Idrissa, M. (2024) Computer Simulation Models of Infectious Diseases: Advantages and Limits. International Journal of Science and Research Methodology, 27(7).
  • Salinas, Á. J. M. (2024). Algunas implicaciones en la lógica sociológica al usar la inteligencia artificial.
  • Samad, N. A., Osman, K., & Nayan, N. A. (2024). Computational Thinking Through the Engineering Design Process in Chemistry Education. International Journal of Educational Methodology, 9(issue-4-november-2023), 771-785.
  • Samhitha, J. S. S., Sagar, K. A., Yaswanth, J. S., & Haritha, K. (2024, March). Early Forest Fire Prediction System Using Wireless Sensor Network. In 2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) (pp. 232-237). IEEE.
  • Samimi, K., Zarei, E., & Pouyakian, M. (2024). Agent-Based Modeling and Simulation in System Safety and Risk Management. In Safety Causation Analysis in Sociotechnical Systems: Advanced Models and Techniques (pp. 405-432). Cham: Springer Nature Switzerland.
  • Samoylova, K., & Zamyatina, E. Design of Robust Business Processes Using Multimodel Approach. Available at SSRN 4801736.
  • Sampaio, F. F. (2024). A Modelagem Computacional no Ensino: Apresentação e Reflexões. Medi@ ções, 12(2), 147-157.
  • Sánchez-Maroño, I. N. EGAR: Environment Generator for Agent-Based Research. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection: 22nd International Conference, PAAMS 2024, Salamanca, Spain, June 26–28, 2024, Proceedings (p. 217). Springer Nature.
  • Sano, H. (2024). Impacts of public disclosure on tax compliance using agent-based modeling. Journal of Economic Interaction and Coordination, 1-33.
  • Santos, A. R., & Santos, F. Introductory Guide to Agent-Based Simulation Development on the GAMA Platform 2. In Este documento reúne os trabalhos apresentados nas sessões técnicas da 18ª edição do Workshop-Escola de Sistemas de Agentes, Seus Ambientes e Aplicações–Wesaac 2024, realizado na Universidade de Brasília, entre 14 e 16 de agosto de 2024. Durante as sessões técnicas, foram apresentados 21 artigos que (p. 96).
  • Santucci, J. F., Capocchi, L., Ören, T., Szabo, C., & Neto, V. V. G. Synergies of Soft Computing and M&S 12. Body of Knowledge for Modeling and Simulation, 287.
  • Sanyal, M., Sengupta, P., Banerjee, P., Dutta, S., & Ozacar, B. H. (2024). River crossings of our childhood: Nezuko and Yasmine dis/orient computing education.
  • Sapienza, A., & Falcone, R. (2024). Exploring the Dynamics of Learned, Pre-existing, and Partial Knowledge in Dependence Networks within Multi-Agent Systems.
  • Sapienza, A., & Falcone, R. (2024). Flood Risk and Preventive Choices: A Framework for Studying Human Behaviors. Behavioral Sciences, 14(1), 74.
  • Saranya, A., Naresh, R., Karuppiah, S., & Jenifer, M. (2024). Development of trust-based authorization and authentication framework for secure electronic health payment in cloud environment. Soft Computing, 1-16.
  • Saravia, L. A., Balza, U., & Momo, F. (2024). Why there are more species in several small patches versus few large patches: A multispecies modelling approach. Functional Ecology.
  • Sarkar, M., Kweon, O., Kim, B. I., Choi, D. G., & Kim, D. Y. (2024). Synergizing Autonomous and Traditional Vehicles: A Systematic Review of Advances and Challenges in Traffic Flow Management With Signalized Intersections. IEEE Transactions on Intelligent Transportation Systems.
  • Sasaki, M., Monaco, C. J., Booth, D. J., & Nagelkerken, I. (2024). Ocean warming and novel species interactions boost growth and persistence of range‐extending tropical fishes but challenge that of sympatric temperate species in temperate waters. Journal of Biogeography.
  • Saula, A. Y., Rowlatt, C., & Bowness, R. (2024). Use of Individual-Based Mathematical Modelling to Understand More About Antibiotic Resistance Within-Host. In Antibiotic Resistance Protocols (pp. 93-108). New York, NY: Springer US.
  • Savadatti, S. G., Srinivasan, K., & Hu, Y. C. (2024). A Bibliometric Analysis of Agent-Based Systems in Cybersecurity and Broader Security Domains: Trends and Insights. IEEE Access.
  • Scharf, I. (2024). Search patterns, resource regeneration, and ambush locations impact the competition between active and ambush predators. Annals of the New York Academy of Sciences.
  • Schnieder, M., Hinde, C., & West, A. (2024). Digital Twin Concept in Last Mile Delivery and Passenger Transport (A Systematic Literature Review). Enterprise Interoperability X: Enterprise Interoperability Through Connected Digital Twins, 135-145.
  • Schöppl, K., & Hahn, U. (2024). Exploring Effects of Self-Censoring through Agent-Based Simulation. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).
  • Secchi, D., Gahrn-Andersen, R., & Neumann, M. (2024). Complexity in Systemic Cognition: Theoretical Explorations with Agent-Based Modeling. Systems, 12(8), 287.
  • Seex, L. (2024). The Self-Organisation of Lemur Social Systems. (Doctoral dissertation, University of Groningen).
  • Sego, T. J., König, M., Fonseca, L. L., Fain, B., Knapp, A. C., Tiwari, K., ... & Malik-Sheriff, R. S. (2024). EFECT--A Method and Metric to Assess the Reproducibility of Stochastic Simulation Studies.
  • Selje, T., Islam, R., & Heinz, B. (2024). An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change.
  • Sen, S. (2024). Polarisation and Homophily: Experiments with a Contagion-Repulsion Combination Opinion Dynamics Model.
  • Senanayake, G. P., Kieu, M., Zou, Y., & Dirks, K. (2024). Agent-based simulation for pedestrian evacuation: A systematic literature review. International Journal of Disaster Risk Reduction, 104705.
  • Sergeyuk, A., Golubev, Y., Bryksin, T., & Ahmed, I. (2024). Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward. arXiv preprint arXiv:2406.07765.
  • Seuru, S., Burke, A., & Perez, L. (2024). Evidence of an age and/or gender-based division of labor during the Last Glacial Maximum in Iberia through rabbit hunting. Journal of Archaeological Science: Reports, 56, 104560.
  • Shadkam, E., & Irannezhad, E. A Comprehensive Review of Simulation Optimization Methods in Agricultural Supply Chains and Transition Towards an Agent-Based Intelligent Digital Framework for Agriculture 4.0. Available at SSRN 4791793.
  • Shah, D., Belshe, R., Speyer, G., & Yalim, J. (2024). Measuring the Impact of Centralized High-Performance Computing with Research Collaboration Networks. SN Computer Science, 5(5), 600.
  • Shahouni, R., Abbasi, M., Dibaj, M., & Akrami, M. (2024). Utilising Artificial Intelligence to Predict Membrane Behaviour in Water Purif
  • Sharmila, L., Shelke, M. P., Venkatesan, R., Ramya, R., Umadevi, G., & Ishwarya, M. V. (2024). Computer Modeling Using Visualization of Measuring Method. Wireless Personal Communications, 1-14.
  • Sharypin, A., Galushko, O., Stavroiani, S., Stavroyany, S., & Kravchenko, I. (2024). The prospective evolution of social networks from the perspective of neurophenomenology. Telos: Revista de Estudios Interdisciplinarios en Ciencias Sociales, 26(2), 595-613.
  • Shen, Y., Hu, X., Wang, X., Zhang, M., Deng, L., & Wang, W. (2024, July). Integrated framework for space-and energy-efficient retrofitting in multifunctional buildings: A synergy of agent-based modeling and performance-based modeling. In Building Simulation (pp. 1-22). Tsinghua University Press.
  • Shili, M., & Anwar, S. (2024). Leveraging Agent-Based Modeling and IoT for Enhanced E-Commerce Strategies. Information, 15(11), 680.
  • Shin, H. (2024). Quantifying Population Exposure to Long-term PM10: A City-wide Agent-based Assessment. arXiv preprint arXiv:2402.05029.
  • Shinde, S., P Kurhekar, M., Gulhane, M., & K Pikle, N. (2024). Design of a Novel Enhanced Machine Learning Model for Early Prediction of Cerebral Stroke. International Journal of Computing and Digital Systems, 16(1), 1-22.
  • Siebe, H. (2024). Modelling the prevalence of hidden profiles with complex argument structures. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).
  • Sihombing, D. J. C. (2024). Development of Membership Management Application for Fitness Center using Extreme Programming Methodology. Jurnal Ekonomi, 13(02), 736-744.
  • Silva, C., & Xue, S. (2024). Situating spatial determinism in urban design and planning for sustainable walkability: a simulation of street morphology and pedestrian behaviour. Discover Sustainability, 5(1), 1-20.
  • Silva, T. V. F. Jogos Sérios Com Temas Ambientais: Cenários, Formatos E Aplicações. Available at SSRN 5063745.
  • Simmonds, J., Gómez H, J. A., & Ledezma, A. (2024). Testing the Feasibility of an Agent-Based Model for Hydrologic Flow Simulation. Information, 15(8), 448.
  • Singh, S. (2024). A Qualitative Approach for Learning and Detection of Emergent Behaviors (Doctoral dissertation, Northeastern University).
  • Singh, S. K., Sharma, C., Mahadeva, R., Patole, S. P., & Maiti, A. (2024). Predicting forward osmosis performance with synthesized polyamide-based membrane: An integrated machine learning (MATLAB and ANN) and economic analysis framework. Journal of Cleaner Production, 141285.
  • Sinha, G. K., & Purwar, A. K. (2024). Optimization Models in Water Resources Management and Security: A Critical Review. International Journal of Mathematical, Engineering & Management Sciences, 9(1), 129-146.
  • Sinha, R., Swanson, H., & Chandrasekharan, S. (2024). Epistemic Games at the Frontier: A Characterization of Emerging Science and Engineering Practices. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 1822-1825. International Society of the Learning Sciences.
  • Smith, M. M., & Pauli, J. N. (2024). Small but connected islands can maintain populations and genetic diversity under climate change. Ecography, e07119.
  • Soler-Navarro, D. J., Giménez, A., Pérez-Ibarra, I., Janssen, M. A., & Tenza-Peral, A. Environmental Strategies Increase the Resilience of Extensive Livestock Systems to Adverse Climate Conditions. Available at SSRN 5002017.
  • Soliman, K. S. (Ed.). (2024). Artificial Intelligence and Machine Learning: 41st IBIMA International Conference, IBIMA-AI 2023, Granada, Spain, June 26-27, 2023, Revised Selected Papers (Vol. 2101). Springer Nature.
  • Song, S., Wang, S., Jiao, C., & Ibarra, E. J. M. (2024). ABSESpy: An agent-based modeling framework for social-ecological systems. Journal of Open Source Software, 9(96), 6298.
  • Sorel, M., Gay, P. E., Vernier, C., Cissé, S., & Piou, C. (2024). Upwind flight partially explains the migratory routes of locust swarms. Ecological Modelling, 489, 110622.
  • Sosa, A. Z., & Rodríguez, L. G. R. (2024). Las Redes Sociales No Digitales en la Acción Colectiva: Modelo Basado en Agentes. Psicumex, 14, 1-32.
  • Sosa, A. Z., & Rodríguez, L. G. R. (2024). Las Redes Sociales No Digitales en la Acción Colectiva: Modelo Basado en Agentes. Psicumex, 14, 1-32.
  • Souidi, M. E. H., Ledmi, M., Maarouk, T. M., Ledmi, A., & Laassami, F. (2024). IMAP-QL: an improved multi-agent pursuit path-planning based on Q-learning. International Journal of Systems, Control and Communications, 15(2), 159-178.
  • Spång, L. G., Neumann, W., Loeffler, D., & Ericsson, G. (2024). The Moose Trappers and Hunting Grounds of Vilhelmina: ABM-Simulation of Annual Cycles during the Stone Age. Current Swedish Archaeology, 32, 37-63.
  • Spring, N. (2024). Multi-Agent Systems in Vehicular Edge Computing: A Communication-Centric Approach to Task Handovers (Doctoral dissertation, Technische Universität Wien).
  • Squadrani, L., Wert-Carvajal, C., Müller-Komorowska, D., Bohmbach, K., Henneberger, C., Verzelli, P., & Tchumatchenko, T. (2024). Astrocytes enhance plasticity response during reversal learning. Communications Biology, 7(1), 1-10.
  • Stacy, L. (2024). Agent-Based Modeling and Air Sampling Methods for Studying Aerosol Transmission.
  • Stasiowska, D. (2024). The assessment of the usefulness of the biocybernetic model of a bee colony to predict the effects of giving the queen bee stress associated with space flight [Doctoral dissertation, AGH University of Krakow]. AGH University of Krakow.
  • Steinberg, S., Gresalfi, M., Vogelstein, L., & Brady, C. (2024). Coding choreography: Understanding student responses to representational incompatibilities between dance and programming. Journal of Research on Technology in Education, 56(3), 314-331.
  • Steinmann, P. (2024). Quantifying resilience under deep uncertainty (Doctoral dissertation, Wageningen University).
  • Stephan, S., Galland, S., Narsis, O. L., Shoji, K., Vachenc, S., Gerart, S., & Nicolle, C. (2024). Agent-based approaches for biological modeling in oncology: A literature review. Artificial Intelligence in Medicine, 102884.
  • Stine, A. A. K., & Crooks, A. (2024). Retention in Higher Education: An Agent-Based Model of Social Interactions and Motivated Agent Behavior.
  • Stolk, M. GroupRugs: Visualizing Group Motion.
  • Stöppler, F. How Abstract Mechanisms Come Alive: Modelling Network Path Dependence Using Qualitative Data. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 435). Springer Nature.
  • Strasburg, M., & Christensen, S. (2024). Evaluating the Interaction of Emerging Diseases on White-Tailed Deer Populations Using an Agent-Based Modeling Approach. Pathogens, 13(7), 545.
  • Suchak, K., Kieu, M., Oswald, Y., Ward, J. A., & Malleson, N. (2024). Coupling an Agent-Based Model and Ensemble Kalman Filter for Real-Time Crowd Modelling. Royal Society Open Science.
  • Sueur, C., Lombard, J., Capra, O., Beltzung, B., & Pelé, M. (2024). Exploration of the creative processes in animals, robots, and AI: who holds the authorship?. Humanities and Social Sciences Communications, 11(1), 1-12.
  • Sulaimany, S., Bidkhori, G., & Khoshnaw, S. H. (2024). Causal loop and Stock-Flow Modeling of Signal Transduction Pathways. arXiv preprint arXiv:2409.01267.
  • Sullivan, A. (2024). Designing Technology-Enriched Learning Environments: The Effects of an Agent-Based Computer Model on Biology Students' Understanding of the COVID-19 Pandemic and the Nature of Models in Science.
  • Sultan, H. M. K. M. M., & Abbas, A. F. Enhance Penetration Testing Techniques to Improve Cybersecurity with NetLogo, Nmap, and Wireshark. Journal of Natural and Applied Sciences URAL, 100.
  • Sun, C., Li, G., Zhou, K., Huang, D., & Luo, Q. (2024). Towards a Sustainable Structure of an Urban Water–Energy–Food Nexus: Based on Network and Hierarchy Analysis. Water, 16(15), 2074.
  • Sun, H., Han, G., Zhang, X., & Ruan, X. (2024). Grasping Emergency Dynamics: A Review of Group Evacuation Techniques and Strategies in Major Emergencies. Journal of Safety Science and Resilience.
  • Sun, H., Yan, Y., & Han, Y. (2024). The influencing factors of green technology innovation in renewable energy companies based on hyper-network. Energy Informatics, 7(1), 1-33.
  • Sun, Y. T., Lee, H. C., Yu, Y. C., Wu, T. F., Althamary, I., & Huang, C. W. (2024). A Multi-Layer Blockchain Simulator and Performance Evaluation of Social Internet of Vehicles with Multi-Connectivity Management. arXiv preprint arXiv:2411.14000.
  • Sunday, A. O., Agbo, F. J., & Suhonen, J. (2024). Co-design Pedagogy for Computational Thinking Education in K-12: A Systematic Literature Review. Technology, Knowledge and Learning, 1-56.
  • Suro, F., Michel, F., & Stratulat, T. (2024). Integration of memory systems supporting non-symbolic representations in an architecture for lifelong development of artificial agents. Artificial Intelligence, 104228.
  • Swanson, H. & Wilensky, U. (2024). Meta-theoretic competence for computational agent-based modeling. In Proceedings of the 2nd Annual ACM Symposium on Learning, Design and Technology, (pp. 38-46) New York: ACM. https://doi.org/10.1145/3663433.3663458
  • Sznajd-Weron, K., & Kamińska, B. (2024). Impact of Cognitive Dissonance on Social Hysteresis: Insights from the Expressed and Private Opinions Model. Available at SSRN 4814235.
  • Talebi, A. (2024). Simulation in discrete choice models evaluation: SDCM, a simulation tool for performance evaluation of DCMs. arXiv preprint arXiv:2407.17014.
  • Tan, F., Wang, J., De Steur, H., & Fan, S. (2024). Unveiling the predictive factors influencing consumers purchase intention towards biofortified products: A PLS-SEM model with agent-based simulation.
  • Tang, W., Yang, H., & Pi, J. (2024). Dynamics and Control Strategies for SLBRS Model of Computer Viruses Based on Complex Networks. International Journal of Intelligent Systems, 2024.
  • Tang, X., & Lira, M. (2024). Drawing and Talk Reveal Dynamic Shifts in Students' Developing Knowledge of Complex Systems. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 2123-2124. International Society of the Learning Sciences.
  • Tang, X., Lindgren, R., & Lira, M. (2024). Haptic Experiences Shape Student-Generated Gestures While Learning with Computational Environments. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 218-225. International Society of the Learning Sciences.
  • Thomas, A. (2024). Grasp planning techniques for harvesting in a novel greenhouse simulation environment (Doctoral dissertation, University of Guelph).
  • Thomas, S. R., & Aurora, S. R. (2024). The dynamic effects of transformational leadership on employee retention and employability over time: an agent-based model. Computational and Mathematical Organization Theory, 1-31.
  • Thompson, B. K. (2024). Quantitative Modeling Tools for Invasive Species Management Decisions (Doctoral dissertation, University of Washington).
  • Tian, F., Zhang, L., Zhu, L., Zhao, M., Liu, J., Dong, Q., & Zhao, Q. (2024). Advancements in Affective Disorder Detection: Using Multimodal Physiological Signals and Neuromorphic Computing Based on SNNs. IEEE Transactions on Computational Social Systems.
  • Timberlake, T. P., Tew, N. E., & Memmott, J. (2024). Gardens reduce seasonal hunger gaps for farmland pollinators. Proceedings B, 291(2033), 20241523.
  • Tiwary, A., & Mukhopadhyay, S. (2024). Identifying Highly Reactive Nanoplastic Polymers Using Frontier Molecular Orbital Theory and Computationally Modeling Associated Cellular Uptake of Modified Nanopolymers in Gastrointestinal Environments.
  • Tiwary, A., & Mukhopadhyay, S. Research Supervisors: Mr. Robert Gotwals3 and Dr. Sarah Shoemaker3 Raleigh Charter High School, Cary, North Carolina. Email: atiwary@ raleighcharterhs. org 2 North Carolina Central University, Biomedical and Biotechnology Research Institute. Email: smukhopadhyay@ nccu. edu.
  • Tofighi, A., & Berea, A. (2024). An agent-based model of human-whale interactions in the North Atlantic. Aquatic Mammals 50(6), 569-579, https://doi.org/10.1578/AM.50.6.2024.569
  • Topraklı, A. Y. (2024). A Review and Implementation Guide for Basic Cellular Automata Models in Pedestrian Evacuation Simulation. Gazi University Journal of Science Part B: Art Humanities Design and Planning, 12(3), 517-530.
  • Topuz, E., & Yücel, G. Analyzing the Emergence and Dynamics of Pluralistic Ignorance with Agent-Based Models. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 423). Springer Nature.
  • Torres, L. M. L., Meythaler, F. S. V., Muñoz, J. L. C., & Soria, S. L. G. (2024). Análisis de las intersecciones aledañas al terminal terrestre de la ciudad de Tena-Ecuador. HOLOPRAXIS. Revista de Ciencia, Tecnología e Innovación, 8(1), 117-147.
  • Tran, N. B., Nguyen, T. L., Nguyen, Q. V., & Nguyen, D. T. (2024). Optimizing Municipal Solid Waste (MSW) Collection and Transportation Network Using GIS and Agent-based Modeling Approach: A Case Study in Hung Yen City, Vietnam. VNU Journal of Science: Earth and Environmental Sciences.
  • Tran¹, L., Son¹, T. C., Flynn, D., & Balduccini, M. A Simulation for Supply Chains Contract. In Logic Programming and Nonmonotonic Reasoning: 17th International Conference, LPNMR 2024, Dallas, TX, USA, October 11–14, 2024, Proceedings (p. 331). Springer Nature.
  • Traore, C. A. D. G., Delay, E., Diop, D., & Bah, A. (2024). Agent-based model for analyzing the impact of movement factors of sahelian transhumant herds. Human-Centric Intelligent Systems, 1-19.
  • Tsintsaris, D., Tsompanoglou, M., & Ioannidis, E. (2024). Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business. Mathematics, 12(8), 1141.
  • Tuekam Kakeu, S. V., Fotsing, E., Kameni, E. D., & Antonio Nkenlifack, M. J. (2024). An agent architecture for expressive spatial knowledge and reasoning in land use modeling and simulations. SIMULATION, 00375497241247040.
  • Tung, E., Mostafavi, A., Li, M., Li, S., Rasheed, Z., & Shafique, K. (2024). A System for Critical Facility and Resource Optimization in Disaster Management and Planning. arXiv preprint arXiv:2410.02956.
  • Tzouras, P. G., Mitropoulos, L., Karolemeas, C., Stravropoulou, E., Vlahogianni, E. I., & Kepaptsoglou, K. (2024). Agent-based simulation model of micro-mobility trips in heterogeneous and perceived unsafe road environments. Journal of Cycling and Micromobility Research, 100042.
  • Umlauft, M., & Schranz, M. Using NetLogo to Simulate Large Production Plants: Simulation Performance: A Case Study.
  • Umlauft, M., Gojkovic, M., Harshina, K., Majbour, K., & Schranz, M. (2024). Bees, Bats and Glowworms: Swarm Algorithms for Optimizing Industrial Plants from the Bottom-Up. In Agents and Artificial Intelligence: 15th International Conference, ICAART 2023, Lisbon, Portugal, February 22–24, 2023, Revised Selected Papers (p. 3). Springer Nature.
  • van Bruggen, A. R., Hoekstra, J., Claassen, L., Princen, M., ter Hoeven, E., van Burgsteden, M., & te Brömmelstroet, M. (2024). Transformative Modelling for Sustainable Mobility and Healthy Neighborhood Spatial Design: The Case of Enka. Available at SSRN 4744940.
  • van den Hurk, M., Dechesne, M., & Dignum, F. Exploring the Stepwise Process and Consequences of Identity Fusion in Different Groups: An ABM. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 397). Springer Nature.
  • van den Ouweelen, T. M. R. (2024). Navigating Value Controversies.
  • van Dongen, M. (2024). The Teaching Methods and Strategies of Programming Education within High Schools in The Netherlands.
  • van Nes, E. H., Pujoni, D. G., Shetty, S. A., Straatsma, G., de Vos, W. M., & Scheffer, M. (2024). A tiny fraction of all species forms most of nature: Rarity as a sticky state. Proceedings of the National Academy of Sciences, 121(2), e2221791120.
  • van Oort, P. A. J., Fonteijn, H. M. J., & Hengeveld, G. M. (2024). DARTS: Modelling effects of shocks on global, regional, urban and rural food security. PLOS Complex Systems, 1(2), e0000006.
  • van Zinnicq Bergmann, M. P., Griffin, L. P., Bodey, T. W., Guttridge, T. L., Aarts, G., Heithaus, M. R., ... & Papastamatiou, Y. P. (2024). Intraguild processes drive space‐use patterns in a large‐bodied marine predator community. Journal of Animal Ecology.
  • Vandin, A. Statistical Model Checking of Python Agent-Based Models: An Integration. In Bridging the Gap Between AI and Reality: Second International Conference, AISoLA 2024, Crete, Greece, October 30–November 3, 2024, Proceedings (p. 398). Springer Nature.
  • Varela, L., Batallés, M., Tambusso, P. S., Costoya, G., & Fariña, R. A. (2024). The Megafauna3D Educational Environment: Harnessing the Combination of New and Traditional Technologies to Improve Geoscience Education and Outreach. Geosciences, 14(12), 321.
  • Vargas-Alejo, V., Montero-Moguel, L., & Reyes-Rodríguez, A. (2024). Evolución de Sistemas Conceptuales Durante un Programa de Actualización Docente Basado en Modelación Matemática. Bolema: Boletim de Educação Matemática, 38, e230198.
  • Velandia Lara, V. M. Propuesta de lineamientos de usos de la malla vial basado en el análisis espacial y simulación basada en agentes para la localidad de Chapinero (upz el Refugio y Chicó Lago) en la ciudad de Bogotá (Master's thesis, Universidad de La Sabana).
  • Veldhuis, A., Johry, A., Bekker, T., Jetten, M., Cronin, E. B., & Works, D. Design Futures. PROCEEDINGS OF CONSTRUCTIONISM/FABLEARN 2023, 30.
  • Verhoog, M. (2024) How Marketing and Sales can Push and Accelerate Residential Refurbishment.
  • Verma, P., Sood, S. K., Kaur, H., Kumar, M., Wu, H., & Gill, S. S. (2024). Data Driven Stochastic Game Network-Based Smart Home Monitoring System Using IoT-Enabled Edge Computing Environments. IEEE Transactions on Consumer Electronics.
  • Vestad, A., & Yang, B. (2024). Human Factors in Cybersecurity, Vol. 127, 2024, 83-93 AHFE. Human Factors in Cybersecurity, 83.
  • Vidal-Lamolla, P., Molinos-Senante, M., Oliva-Felipe, L., Alvarez-Napagao, S., Cortés, U., Martínez-Gomariz, E., ... & Poch, M. (2024). Assessing urban water demand-side management policies before their implementation: An agent-based model approach. Sustainable Cities and Society, 105435.
  • Vidler, A., & Walsh, T. (2024). Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation Study. arXiv preprint arXiv:2405.02849.
  • Vigna, I., Millington, J., Ascoli, D., Comino, E., Pezzoli, A., & Besana, A. (2024). A picit jeu: Agent-based modelling with serious gaming for a fire-resilient landscape. Journal of Environmental Management, 370, 122529.
  • Vijayan, M., Patil, A., & Kapse, V. (2024). An agent-based computational model on household electricity consumption in Indian cities. Journal of Green Building, 19(1), 235-260.
  • Voß, J. N. (2024). Konzeption und Entwicklung einer didaktischen Modellierungs-und Simulationsplattform für MARS (Doctoral dissertation, Hochschule für Angewandte Wissenschaften Hamburg).
  • Vulic, J., Jacobson, M. J., & Levin, J. A. (2024). Exploring Education as a Complex System: Computational Educational Research with Multi-Level Agent-Based Modeling. Education Sciences, 14(5), 551.
  • Wagh, A., Levy, S., Horn, M., Guo, B., & Wilensky, U. (2024) Learning to Reason About Natural Selection as a Computational Process by Programming Agent-Based Models in a Code-First Environment.
  • Wagh, A., Rosenbaum, L. F., Fuhrmann, T., Eloy, A., Blikstein, P., & Wilkerson, M. (2024). Toward Ontological Alignment: Coordinating Student Ideas with the Representational System of a Computational Modeling Unit for Science Learning. Cognition and Instruction, 1-32.
  • Wagh, A., Vogelstein, L., & Champion, D. (2024). Fused Representations: Linking Choreographic and Digital NetLogo Modeling through Intermodal Inquiry. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 714-721. International Society of the Learning Sciences.
  • Wan, L., Lv, Y., Wang, Z., & Tian, Y. (2024). The synergistic evolution of supply-demand composite system for airport green development: A case study in Guangzhou Baiyun International Airport, China. PloS one, 19(4), e0302303.
  • Wang, C., Zhou, Z., & Zheng, G. (2024). Efficient weighted multi-source trust aggregation scheme for edge computing offloading. Social Network Analysis and Mining, 14(1), 33.
  • Wang, H. (2024). Impacts of Altruism and Uncertainty on Consumption: Three Essays (Doctoral dissertation, University of Guelph).
  • Wang, H., Li, F., Zou, Y., Yang, M., Chen, Z., & Nie, X. (2024). How do smallholder farmers run towards agricultural modernization under multiple stressors in China's coastal zones: an agent-based modeling approach. Environment, Development and Sustainability, 1-27.
  • Wang, H., Lu, Q., Zhang, Z., Lin, J., & Wu, Q. (2024). Study on separation of CO2 condensation from natural gas based on cellular automaton method. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 46(1), 3663-3683.
  • Wang, M., Wang, H. H., Koralewski, T. E., Grant, W. E., White, N., Hanan, J., & Grimm, V. (2024). From known to unknown unknowns through pattern-oriented modelling: Driving research towards the Medawar zone. Ecological Modelling, 497, 110853.
  • Wang, S., Chang, J., Xue, J., Sun, H., Zeng, F., Liu, L., ... & Li, X. (2024). Coupling behavioral economics and water management policies for agricultural land-use planning in basin irrigation districts: Agent-based socio-hydrological modeling and application. Agricultural Water Management, 298, 108845.
  • Wang, W., & Wu, F. (2024). Dynamic simulation for reclaimed water reuse under multi-intervention policies in China. Heliyon, 10(3).
  • Wang, W., Sawleshwarkar, S., & Piraveenan, M. (2024). Computational Approaches of Modelling Human Papillomavirus Transmission and Prevention Strategies: A Systematic Review. arXiv preprint arXiv:2404.19235.
  • Wang, X., He, X., Sun, X., Qin, M., Pan, R., & Yang, Y. (2024, January). The Diffusion Path of Distributed Photovoltaic Power Generation Technology driven by Individual Behavior. In Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29, 2023, Tianjin, China.
  • Wang, X., Jin, L., & Wang, S. (2024). Research on Network Public Opinion in War Damage Incident of Major Water Conservancy Projects. IEEE Access.
  • Wang, Z. Z., Xu, S. Q., & Guan, Y. J. (2024). Impact of the innovation promotion strategy on digital technology diffusion in regional innovation ecosystems. Technology Analysis & Strategic Management, 1-17.
  • Wang, Z., Yue, C., & Wang, J. (2024). Evaluating parameter inversion efficiency in Heterogeneous Groundwater models using Karhunen-Loève expansion: a comparative study of genetic algorithm, ensemble smoother, and MCMC. Earth Science Informatics, 1-17.
  • Wanniarachchi, C., Wimalaratne, P., & Karunanayaka, K. (2024). Formation Control Algorithms for Drone Swarms and The Single Point of Failure Crisis: A Review. In 2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE) (pp. 1-6). IEEE.
  • Wasesa, H. A., & Prasetio, E. A. Prediction of Battery Electric Vehicle (BEV) Adoption in Indonesia Using Agent-Based Modelling and Simulation: An Integrated Model Implementation.
  • Wasesa, M., Rizaldi, A., Stam, A., Zuidwijk, R., & Van Heck, E. (2024). Advancing Smart Sustainable Seaports: Auction-based Truck Appointment System for Automated Container Terminal. IEEE Access.
  • Weitzel, E. M., Wilson, K. M., Spake, L., Schaffnit, S. B., Lynch, R., Sear, R., ... & Sosis, R. (2024). Cost structures and socioecological conditions impact the fitness outcomes of human alloparental care in agent-based model simulations. Evolution and Human Behavior, 45(5), 106613.
  • Wen, C. T., Liu, C. C., Li, C. Y., Chang, M. H., Chiang, S. H. F., Lin, H. M., ... & Biswas, G. (2024). The learning analytics of computational scientific modeling with self-explanation for subgoals and demonstration scaffolding. Computers & Education, 105043.
  • Wen, F., Chen, Y., & Pan, C. (2024, May). Cooperative tracking control of cross-domain heterogeneous unmanned systems via adaptive neural backstepping. In 2024 7th International Symposium on Autonomous Systems (ISAS) (pp. 1-6). IEEE.
  • Weng, X., Pan, D., & Jin, Y. (2024). An Adaptive Distributed Consumer Trust Model for Social Commerce. Applied Artificial Intelligence, 38(1), 2385857.
  • Westbury, S. O. (2024). RULER: Prebugging with Proxy-Based Programming.
  • Westerveld, J., & Verbraeck, A. A. Recurring COVID-19 Vaccinations.
  • Wiegel, E., & Yorke-Smith, N. (2024). An Agent-Based Market Analysis of Urban Housing Balance in The Netherlands. Real Estate, 1(1), 80-135.
  • Wiggins, D. E., & Solomonides, A. E. 2024. Modeling a Viral Epidemic With a Concurrent “Misinfodemic”.
  • Williams, A., Mesoudi, A., Williams, A. J., & Mesoudi, A. Testing the effect of circumscription on the evolution of social complexity in the Valley of Oaxaca, Mexico, using 2 agent-based models 3.
  • Wong, J., Nack, E., Steelman, Z., Erway, S., & Bastian, N. D. (2024, June). A methodology for representing and assessing artificial intelligence decision aids within modeling and simulation. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI (Vol. 13051, pp. 143-167). SPIE.
  • Woo, K., & Falloon, G. (2024). Integrating coding across the curriculum: a scoping review. Computer Science Education, 1-22.
  • Wood, T., An, G., Bryant, C. E., & Ferguson, B. J. (2024). Modelling molecular differences in the innate immune system responses of chickens and ducks to highly pathogenic avian influenza virus. bioRxiv, 2024-07.
  • Wu, J. (2024). Evaluation of Social Network Influence. In Social Network Computing (pp. 453-490). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Homogeneity in Social Networks. In Social Network Computing (pp. 159-188). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Introduction to Social Network Computing. In Social Network Computing (pp. 3-43). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Modeling and Simulation of Social Networks. In Social Network Computing (pp. 551-573). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Power Laws in Social Networks. In Social Network Computing (pp. 257-284). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Small World in Social Networks. In Social Network Computing (pp. 219-256). Singapore: Springer Nature Singapore.
  • Wu, L., Sun, Z., Liu, J., Shan, D., Ma, X., & Zhu, T. (2024). Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach. Computers and Electrical Engineering, 119, 109500.
  • Wu, Y. (2024, November). Research on product quality control based on multiagent modeling. In International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) (Vol. 13395, pp. 340-344). SPIE.
  • Wurth, B., & Kazakov, R. Combining SD and ABM: Fram
  • Xiang, L., Mirakhur, Z., Pilny, A., & Krall, R. (2024). Measuring and Comparing High School Teachers’ and Undergraduate Students’ Knowledge of Complex Systems. Education Sciences, 14(8), 837.
  • Xie, S., Gan, C., & Lawniczak, A. T. (2024). Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models. Mathematics, 12(23), 3768.
  • Xu, J., & Bi, Y. (2024). An Agent-Based Modeling Approach for the Diffusion Analysis of Electric Vehicles With Two-Stage Purchase Choice Modeling. Journal of Computing and Information Science in Engineering, 24(6), 064502.
  • Xu, S., Hsu, S. C., Du, E., Song, L., Lam, C. M., Liu, X., & Zheng, C. (2024). Agent-Based Modeling in Water Science: From Macroscale to Microscale. ACS ES&T Water.
  • Xu, W., & Zhang, K. (2024). Ant Colony Optimization for Urban Flood Rescue Routing: Integrating Agent-Based Simulation and Multi-Center Vehicle Routing. Available at SSRN 4998292.
  • Xu, Z., Ban, F., & Fotia, P. (2024). Efficient QoS processing for internet of medical things using non-cooperative game theory: resource allocation in cloud framework. Annals of Operations Research, 1-16.
  • Yalabadi, A. K., Yazdani-Jahromi, M., Abdidizaji, S., Garibay, I., & Garibay, O. O. (2024). Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents. arXiv preprint arXiv:2401.11524.
  • Yan, Z., & Yeo, J. (2024). Competing Mechanisms in Bacterial Invasion of Human Colon Mucus Probed with Agent-based Modeling. Biophysical Journal.
  • Yang, Q., Tang, Y., & Gao, D. (2024). Agent-based evolutionary game dynamics uncover the dual role of resource heterogeneity in the evolution of cooperation. Journal of Theoretical Biology, 111952.
  • Yang, X., & Zeng, X (2024). Combined with the Internet technology of college student training mode innovation and student management mechanism optimization. Applied Mathematics and Nonlinear Sciences, 9(1).
  • Yates, J. W. (2024). Integrated pharmacokinetic-pharmacodynamic and agent-based modelling in drug development: Current status and future perspectives. Advanced Drug Delivery Reviews, 115474.
  • Yezhova, Y., & Maslova, N. (2024). ВИКОРИСТАННЯ AGENTSCRIPT ДЛЯ СТВОРЕННЯ БАГАТОРІВНЕВИХ МОДЕЛЕЙ АГЕНТНОГО МОДЕЛЮВАННЯ. Scientific Papers of Donetsk National Technical University. Series:“Computer Engineering and Automation", 54-66.
  • Yin, L., Yin, F., & Silverman, R. M. (2024). Rethinking demolition plans to fight neighborhood blight in shrinking cities: Applying agent-based policy simulations. Cities, 150, 105035.
  • Yin, X., Xiao, S., Zhang, X., & Ruan, F. (2024). Social Network Analysis of a Norovirus Outbreak at a Primary School—Zhuhai City, Guangdong Province, China, 2023. China CDC Weekly, 6(44), 1142-1146.
  • Yirenkyi, E. (2024). An agent-based modeling approach to household adaptation for flooding and coastal erosion at Channel-Port aux Basque. Earth ArXiv.
  • You, X., & Zhou, Z. (2024). A Multi-attribute Large Group Decision-making Method Based on Interval-valued Pythagorean Fuzzy Number.
  • Young, E. (2024). Where the Smoke Was Coming From: Risk Assessment, Social Ties, and Expanding Roles in the Evacuation of the Beverly Hills Supper Club Fire (Doctoral dissertation, University of Delaware).
  • Youssefi, K. A., Gojkovic, M., & Schranz, M. (2024). Artificial Bee Colony Algorithm: Bottom-Up Variants for the Job-Shop Scheduling Problem.
  • Yu, C. L. (2024). Developing a Theory of Mind: A Multi-method Perspective (Doctoral dissertation).
  • Yu, J. H., & Chie, B. T. (2024). Interplay and Implications of Urban Renewal under Dual Laws-An Agent-Based Simulation. Advances in Management and Applied Economics, 14(5).
  • Yu, Y., Li, H., Liu, Y., & Ma, Y. (2024). Integrating Multi-Model Simulations to Address Partial Observability in Population Dynamics: A Python-Based Ecological Tool. Applied Sciences, 15(1), 89.
  • Yu, Y., Lu, Q., & Fu, Y. (2024). Dynamic Trust Management for the Edge Devices in Industrial Internet. IEEE Internet of Things Journal.
  • Yuan, X., Wang, J., Hu, S., Cheung, A., & Lu, Z. (2024). KoroT-3E: A Personalized Musical Mnemonics Tool for Enhancing Memory Retention of Complex Computer Science Concepts. arXiv preprint arXiv:2409.10446.
  • Yunping, Z. H. A. N. G., Jianping, L. I. N., Yimin, H. U. A. N. G., Zebin, C. H. E. N., Chenhui, Z. H. U., & Hao, Y. U. A. N. (2024). Delineation of urban growth boundary based on FLUS model under the perspective of land use evaluation in hilly mountainous areas. Journal of Mountain Science, 21(5), 1647-1662.
  • Yust, A. E. (2024). BODINE: Exploring the Versatility of Agent-Based Modeling in NetLogo: Lessons from Education and Research.
  • Zaffar, M. A., Kumar, R., & Zhao, K. (2024). Competitive dynamics in a multi-sided mobile payment platform market: an agent-based modeling perspective. International Journal of Bank Marketing, (ahead-of-print).
  • Zagorskas, J., & Turskis, Z. (2024). Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach. Sustainability, 16(7), 2994.
  • Zahwa, F., Simic, M., & Cheng, C. T. Fuzzy-Based Traffic Light Control Strategy: The Good, the Bad, and the Ugly. In Big Data Analytics and Data Science: Proceedings of Eighth International Conference on Information System Design and Intelligent Applications (ISDIA 2024), Volume 3 (p. 53). Springer Nature.
  • Zambonelli, F., Bergenti, F., Mariani, S., & Monica, S. (2024). Islands of cooperation emerge by stigmergic interactions in iterated spatial games. PLOS ONE, 19(7), e0306915.
  • Zambrano García, P. A. (2024). Sostenibilidad ambiental de la cadena de suministro de la franquicia: un modelo basado en agentes para el análisis de la influencia de los mecanismos de gobernanza en la adopción de prácticas verdes (Doctoral dissertation, Universidad Nacional de Colombia).
  • Zamudio Sosa, A., & Rueda Rodríguez, L. G. (2024). Non-Digital Social Networks in Collective Action: Agent-Based Model. Psicumex, 14.
  • Zanette, E. M., Bialozyt, R. B., Santos, M. M., de Almeida e Silva, A. S., Messaoudi, Y., Alcolea, M., ... & Culot, L. Predicting Frugivore Generated Seed Rain in Different Environmental Contexts: A Modeling Approach Applied to a Forest Specialist. Available at SSRN 5047520.
  • Zedadra, O., Guerrieri, A., Seridi, H., Benzaid, A., & Fortino, G. (2024). Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem. Big Data and Cognitive Computing, 8(2), 18.
  • Zedadra, O., Zedadra, A., Guerrieri, A., Seridi, H., & Ghelis, D. (2024). A New Multi-Robots Search and Rescue Strategy based on Penguin Optimization Algorithm. Scalable Computing: Practice and Experience, 25(5), 4428-4441.
  • Zeinali Kalkhoran, A., Platt, L. S., Iovine, N., Brown, S., & Pollitt, B. (2024). An agent-based modeling approach for infection prevention predesign: simulating the spread of pathogens between humans and the environment in an Intensive Care Unit. Architectural Engineering and Design Management, 1-22.
  • Zellner, M. L., & Massey, D. (2024). Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning. Heliyon.
  • Zhang, C., Wu, X., Zhao, S., Madani, H., Chen, J., & Chen, Y. (2024). Multi-agent simulation of the effects of Japanese electricity market policies on the low-carbon transition. Energy Strategy Reviews, 52, 101333.
  • Zhang, G., Wang, X., Wang, G., Suo, X., Qiu, Y., Luo, R. H., ... & Li, Y. (2024). Nanoparticles insert a three dimensional cavity structure of proteins for function inhibition: The Case of CeO2 and SARS-CoV-2. Nano Today, 55, 102183.
  • Zhang, J. (2024). Exploring the intra-platoon planning problem for heterogeneous train traffic under virtual coupling in the railway network via simulation. Journal of Rail Transport Planning & Management, 30, 100442.
  • Zhang, J., Rong, L., & Gong, Y. (2024). An Agent-Based Transmission Model of Major Infectious Diseases Considering Places: Forecast and Control. Mathematics, 12(6), 811.
  • Zhang, J., Sun, Z., Chen, L., Li, J., Wang, Q., Li, Y., ... & Dou, Z. (2024). Rapid assessment of the vulnerability of densely populated urban communities under major epidemics. International Journal of Disaster Risk Reduction, 105082.
  • Zhang, J., Yang, Y. E., Abeshu, G. W., Li, H., Hung, F., Lin, C. Y., & Leung, L. R. (2024). Exploring the food-energy-water nexus in coupled natural-human systems under climate change with a fully integrated agent-based modeling framework. Journal of Hydrology, 131048.
  • Zhang, R., & Bing, S. U. N. (2024). Complex adaptive system theory, agent-based modeling, and simulation in dominant technology formation. Journal of Systems Engineering and Electronics, 35(1), 130-153.
  • Zhang, X., Pitera, K., & Wang, Y. (2024). Exploring parking choices under the coexistence of autonomous and conventional vehicles. Physica A: Statistical Mechanics and its Applications, 129542.
  • Zhang, Y. (2024). An Investigation of the Housing Market and the Supply Modelling Process (Doctoral dissertation, University of Toronto (Canada)).
  • Zhang, Y., Ma, B., Cao, L., & Liu, Y. (2024). A Data-Driven Pandemic Simulator with Reinforcement Learning. Electronics, 13(13), 2531.
  • Zhang, Y., Zhang, B., & Hou, J. (2024). Simulation Study on Student Residential Energy Use Behaviors: A Case Study of University Dormitories in Sichuan, China. Buildings, 14(5), 1484.
  • Zhang, Y., Zhang, X., & Song, J. (2024). Enterprise intelligent financial sharing mechanism in the security environment of the internet of things. International Journal of Information and Computer Security, 24(1-2), 80-97.
  • Zhang, Z., Tang, Y., Tao, C., Zhang, J., Dong, F., Liu, S., ... & Wang, X. (2024). Mesoscopic ring element growth and deformation induced biofilm streamer evolution in microfluidic channels. Water Science & Technology, wst2024168.
  • Zhao, L. (2024, June). Foregrounding Agency in Participatory Design with Children. In Proceedings of the 23rd Annual ACM Interaction Design and Children Conference (pp. 994-996).
  • Zhao, X. (2024). Transaction Management in Blockchains (Doctoral dissertation, University of Macau).
  • Zhao, Y., & Ju, R. (2024). Investor Structure and Corn Futures Price Volatility in China: Evidence Based on the Agent-Based Model. Computational Economics 65, 937-961. https://doi.org/10.1007/s10614-024-10613-5
  • Zhao, Y., Zhu, Z., Zhang, Y., Yang, Y., Guo, Y., & Zhou, W. (2024). Unveiling the evolution of urban rail transit network: considering ridership attributes. Transportation Letters, 1-12.
  • Zheng, Y. (2024). Uncovering Learning in Maker Education: Employing Collective Documentation and Natural Language Processing to Identify Knowledge Construction in Complex Open-Ended Learning Environments (Doctoral dissertation, Teachers College, Columbia University).
  • Zhong, Y. (2024). Rural Small Business Entrepreneurship Strategy Analysis with Optimization in Cooperation Industry Using Evolutionary Game Theory.
  • Zhou, B. (2024). A Cautionary Note on the Application of GIS in Spatial Optimization Modeling. Journal of Geographic Information System, 16(01), 89-113.
  • Zhou, X., Zhou, Y., Gong, Y., Cai, Z., Qiu, A., Xiao, Q., ... & Bai, Z. (2024, June). " Bee and I need diversity!" Break Filter Bubbles in Recommendation Systems through Embodied AI Learning. In Proceedings of the 23rd Annual ACM Interaction Design and Children Conference (pp. 44-61).
  • Zhu, J. (2024). The impact of merchant-induced reviews on consumers’ shopping behavior from the perspective of the digital economy. Current Psychology, 1-16.
  • Zhu, J., Zhang, C., Zhang, M., & Yuan, J. (2024). Modeling and Simulation of Technological Innovation Diffusion of High-performance Construction Materials in Transportation Engineering. Case Studies in Construction Materials, e03838.
  • Zhuang, T., & Lin, Z. (2024).The why, what, and how of AI-based coding in scientific research.
  • Zonas, C., & De, M. D. E. Agenda Ambiental Para Focalizar Acciones De Intervención en Las Ciudades Que Componen Las Tres Zonas Metropolitanas Del Estado De Hidalgo: Pachuca, Tula Y Tulancingo.
  • Zosh, C., Dhameja, N., Ren, Y., & Pape, A. (2024). A Guide and General Method for Estimating Parameters and their Confidence Intervals in Agent-Based Simulations with Stochasticity.
  • Zou, X., Yan, G., Mohammed, K. J., Suhatril, M., Khadimallah, M. A., Marzouki, R., ... & Escorcia-Gutierrez, J. (2024). Advancements in nano-enhanced steel structures for earthquake resilience: Integrating metallic elements, AI, and sensor technology for engineering disasters mitigation in steel buildings. Steel and Composite Structures, 53(4), 443-460.
  • Καρυπίδου, Α. (2024). Τεχνολογικές καινοτομίες στην εκπαίδευση: προκλήσεις που προκύπτουν με τη χρήση των τεχνολογιών πληροφορίας και επικοινωνιών (ΤΠΕ) στην πρωτοβάθμια εκπαίδευση.
  • Афанасьев, А. П., & Колмогорова, С. С. ВОЗМОЖНОСТЬ ПРИМЕНЕНИЯ NETLOGO В ЗАДАЧЕ СБОРА ДАННЫХ. Санкт-Петербургский государственный лесотехнический университет им. СМ Кирова КОНФЕРЕНЦИЯ: АКТУАЛЬНЫЕ ВОПРОСЫ ЛЕСНОГО ХОЗЯЙСТВА Санкт-Петербург, 09–10 ноября 2023 года Организаторы: Санкт-Петербургский государственный лесотехнический университет им. СМ Кирова.
  • Власов, М. А., & Большаков, Р. С. (2024). Обзор Современного Состояния Разработок В Области Моделирования Железнодорожных Станций. Электронный Научный Журнал" Молодая Наука Сибири", (2 (24)).
  • Гайнанов, Д. А., Мигранова, Л. И., & Сайфуллина, Л. Д. (2024). Управление поведением индивидов на региональном рынке труда: подходы, модели и перспективы. Ars Administrandi (Искусство управления), 459-480.
  • Геренко, С. (2024). Когнітивно-функціональний потенціал штучного інтелекту в графічному дизайні. Scientific Collection «InterConf», 192, 123-125.
  • Гостенина, В. И., Мельников, С. Л., Лапыко, Т. П., Юдина, Е. Н., & Лимонова, М. А. (2024). Разработка мультиагентных симуляционных моделей для изучения взаимодействия стейкхолдеров в сфере образования и обучения в угольной промышленности. Уголь, 1180(5), 115-123.
  • Зінов’єва, О. Г. (2024). Імітаційне моделювання в освітньому процесі підготовки ІТ-спеціалістів.
  • Калягина, Д. А., Илларионова, В. И., & Шарнин, И. А. (2024) Проектный Подход К Управлению Логистическими Процессами В Условиях Цифровой Экономики.
  • Кузнецов, А. В., Серов, Н. А., & Адодина, М. М. (2024). Разработка платформы для многоагентного моделирования исторических сообществ. Вестник ВГУ. Серия: Системный анализ и информационные технологии, (3), 17-32.
  • Маркіна, Л. М., Гуменюк, Л. О., Сацик, В. О., & Гуменюк, П. О. ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В МОДЕЛЮВАННІ В’ЯЗКОСТІ ЗАМІСУ СПИРТОВОГО ВИРОБНИЦТВА НА ОСНОВІ МУЛЬТИАГЕНТНИХ СИСТЕМ. МИ РАДІ ВІТАТИ ВАС У ЛНТУ!, 11.
  • Маслобоев, А. В. (2024). Обобщенная Методология Построения Мультиагентных Систем Управления Жизнеспособностью Критических Инфраструктур. Надежность И Качество Сложных Систем, (2 (46)), 134-146.
  • Михайлович, Н. Р. К. С., & Симавонян, К. Ю. (2024). ПРОЕКТИРОВАНИЕ ИНФОРМАЦИОННЫХ СИСТЕМ. ПРОЕКТИРОВАНИЕ WEB-САЙТА, КАК ИНФОРМАЦИОННОЙ СИСТЕМЫ. Столыпинский вестник, 6(10).
  • Молодецька, К., & Веретюк, С. (2024). Імітаційна модель поширення вірусу в мережі з пріоритетним приєднанням. Організатор конференції, 39.
  • Набиева, А. И., & Исхаков, А. Р. МУЛЬТИАГЕНТНОЕ МОДЕЛИРОВАНИЕ ЭМЕРДЖЕНТНОГО ПОВЕДЕНИЯ ТЕПЛОВЫХ ЖУКОВ. ББК 30+ 22.1 Т38, 32.
  • Неділько, І. М. (2025). Загальний огляд платформи для агентного моделювання як альтернатива NetLogo.
  • Оспанова, А. К. (2024). ББК 74.3 Ц42.
  • Попов, А. А. ИСПОЛЬЗОВАНИЕ МУЛЬТИАГЕНТНОЙ МЕТОДОЛОГИИ ДЛЯ МОДЕЛИРОВАНИЯ СБОРА И ТРАНСПОРТИРОВКИ ОТХОДОВ. Инжиниринг предприятий и управление знаниями, 264.
  • Савенкова, Д. А. (2024) Разработка скоринговой системы банка. ББК 1 Н 34, 43.
  • Сагимов, М. К. (2024). УПРАВЛЕНИЕ МОТИВАЦИЕЙ ТРУДА ПЕРСОНАЛА ГОСУДАРСТВЕННОЙ СЛУЖБЫ.
  • Симавонян, К. Ю. ПРОЕКТИРОВАНИЕ ИНФОРМАЦИОННЫХ СИСТЕМ. ПРОЕКТИРОВАНИЕ WEB-САЙТА, КАК ИНФОРМАЦИОННОЙ СИСТЕМЫ.
  • Шагалін, К. А. (2024). Вдосконалення методів захисту від атак на системи інтернету речей: кваліфікаційна (бакалаврська) робота 125 «Кібербезпека».
  • امامی, & دهقانی سانیج. (2024). بررسی مسیر رفتار و آسیب‌شناسی بهره‌برداری از منابع آب دشت میاندوآب با استفاده از رویکرد ABM-FTA. پژوهش آب ایران.‎
  • خزاعی، صفا و نجفیانی. (2024). ارزیابی تخلیه اضطراری مجتمع‌های مسکونی با ساختمان‌های بلند (مطالعه موردی: شهرک پونک زنجان). مجله مدیریت اورژانس.
  • يوسف بن فراج الفراج. (2024). برنامج تطور مهني قائم على النمذجة البيئية ودوره في تعزيز تصورات معلمي العلوم نحو منهجية STEAM: دراسة نوعية. مجلة العلوم التربوية و النفسية, 17(2), 597-563.
  • รุ่ง ทิวา เดชะ ปรากรม, & ชัยวัฒน์ ใบไม้. (2024). การ ประยุกต์ ใช้ แบบ จำลอง จาก ตัวแทน กับ งาน วิจัย ทาง ธุรกิจ. วารสาร เกษตรศาสตร์ ธุรกิจ ประยุกต์, 18(28), 119-142.
  • 양윤영, & 김재근. (2024). 생태학 NetLogo 실습 프로그램 개발을 위해 예비 생물교사들이 겪은 어려움 및 개선점 탐색. 생물교육 (구 생물교육학회지), 52(1), 13-31.
  • 조혜인(Jo Hye-in). (2024). Aggregation Behavior in Polymorphic Foragers of the Japanese Carpenter Ant, Camponotus japonicus (Doctoral dissertation, 서울대학교 대학원).
  • 佐々木夏輝, & ササキナツキ. (2024). マルチエージェントシミュレーション用フレームワークの開発と評価. 人間情報学研究科年誌, (29), 82-85.
  • 大須賀昭彦. (2024). エージェント指向言語の過去・現在・未来. 人工知能, 39(3), 335-341.
  • 宮崎正也. (2024). 普及速度と交友関係: 高齢者のイノベーション採用はなぜ遅いのか. 研究 技術 計画, 38(4), 476-493.
  • 曾荣燊, 李弼程, 陈刚, & 熊尧. (2024). 基于线上线下超网络模型的舆论演化仿真分析. Application Research of Computers/Jisuanji Yingyong Yanjiu, 41(2), 507-514.
  • 树建马, 淑媛杨, 瑛滕, & 佳贺张. (2024). 供应链金融中区块链技术的扩散模型仿真研究——以核心企业为例. 财经与管理· 国际学术论坛, 3(7).
  • 相澤景, & 上野玄太. (2024). エージェントベースモデルにおけるエージェントの異質性を表現する確率分布の推定. 人工知能学会論文誌, 39(5), A-N82_1.
  • 胡景博, 杨克巍, 蒋平, & 姜江. (2024). 基于复杂适应性理论的无人机作战试验交互协作问题研究. 舰船电子工程, 44(4), 6-10.
  • 陈正宁, & 吴亮. (2024). 两栖登陆垂直投送指挥控制效能评估模型研究. 舰船电子工程, 44(4), 24-27.
  • 鶴島彰. (2024). 分散動的避難誘導システムにおける故障発生が避難誘導性能に与える影響. 人工知能学会第二種研究会資料, 2024(DOCMAS-026), 03.

2023

  • رمش ناصر القحطاني, & أحمد زيد آل مسعد. (2023). واقع تدريس مهارات التفكير الحوسبي من وجهة نظر معلمات الحاسب بمدينة الرياض. مجلة المناهج وطرق التدريس, 2(2), 82-106.
  • ودادی کلانتر, سیف الدین, & امیرعلی. (2023). سیاست پژوهی بحران خاموشی: مدل‌سازی تصادفیِ عامل پایه مصرف برق در شهر تهران. نشریه انرژی ایران, 25(4), 55-80.
  • Abadi, B., & Haghaninia, M. (2023). Drivers of Forecasting the Behavioral Intention and Acceptance Behavior of the Hail Canon Technology (HCT): Using Logistic and System Dynamics Modeling. Chinese Geographical Science, 1-16.
  • Abdelshafie, A., Rupnik, B., & Kramberger, T. (2023). Simulated Global Empty Containers Repositioning Using Agent-Based Modelling. Systems, 11(3), 130.
  • Abdolhosseini, S., Ghandehari, M., Ansari, A., & Roozmand, O. (2023). Joint pricing and inventory management in a competitive market using reinforcement learning: a combination of the agent-based and simulation-optimization approaches. International Journal of Management Science and Engineering Management, 18(2), 77-87.
  • Adam, C. (2023). Simulating the impact of cognitive biases on the mobility transition. arXiv preprint arXiv:2302.03554.
  • Adams, J. W., Duprey, M., Khan, S., Cance, J., Rice, D. P., & Bobashev, G. (2023). Examining buprenorphine diversion through a harm reduction lens: an agent-based modeling study. Harm Reduction Journal, 20(1), 150.
  • Addido, J., Borowczak, A. C., & Walwema, G. B. (2023). Teaching Newtonian physics with LEGO EV3 robots: An integrated STEM approach. Eurasia Journal of Mathematics, Science and Technology Education, 19(6), em2280.
  • Adu-Kankam, K. O., & Camarinha-Matos, L. M. (2023). Modeling Collaborative Behaviors in Energy Ecosystems. Computers, 12(2), 39.
  • Adzinets, D., & Alooeff, E. (2023). Field Service Management (FSM) Simulation Model. International Scientific Journal “Industry 4.0”, 8(6), 321-325.
  • Aghamohammadghasem, M., Azucena, J., Hashemian, F., Liao, H., Zhang, S., & Nachtmann, H. (2023, December). System simulation and machine learning-based maintenance optimization for an inland waterway transportation system. In 2023 Winter Simulation Conference (WSC) (pp. 267-278). IEEE.
  • Agnelli, J. P., Buffa, B., Knopoff, D., & Torres, G. (2023). A Spatial Kinetic Model of Crowd Evacuation Dynamics with Infectious Disease Contagion. Bulletin of Mathematical Biology, 85(4), 23.
  • Ahedo, V., Santos, I., Galán, J. M., & Izquierdo, L. R. (2023). La identificación de enlaces ausentes como competición Kaggle para la enseñanza de teoría de redes. Dirección y Organización, (79), 18-28.
  • Ahmad, F., Shah, Z., & Al-Fagih, L. (2023). Applications of Evolutionary Game Theory in Urban Road Transport Network: A State of the Art Review. Sustainable Cities and Society, 104791.
  • Aksu, B., & Aksu, M. V. (2023). Organizational Mnemonics of Gray Collar Workers: Implementing SNA, ABM, and ANT. In Management and Organizational Studies on Blue-and Gray-collar Workers: Diversity of Collars (pp. 105-117). Emerald Publishing Limited.
  • Al-Bazi, A., Madi, F., Monshar, A. A., Eliya, Y., Adediran, T., & Khudir, K. A. (2023). Modelling the impact of non-pharmaceutical interventions on COVID-19 exposure in closed-environments using agent-based modelling. International Journal of Healthcare Management, 1-15.
  • Alam, A. (2023, March). Leveraging the Power of ‘Modeling and Computer Simulation’for Education: An Exploration of its Potential for Improved Learning Outcomes and Enhanced Student Engagement. In 2023 International Conference on Device Intelligence, Computing and Communication Technologies,(DICCT) (pp. 445-450). IEEE.
  • Albo Ismail, W. K. F., & Ucan, O. N. (2023). VANET PERFORMANCE EVALUATION IN TERMS OF NODES DISTRIBUTION, MOBILITY MODELS, AND ROUTING PROTOCOLS. Technium, 8, 32-45.
  • Alghamdi, A. A. (2023). A novel intelligent agent-based framework for appropriate stream selection from perceptive of career counseling. PeerJ Computer Science, 9, e1256.
  • Ali, A. T., Leucker, M., Schuldei, A., Stellbrink, L., & Sachenbacher, M. (2023, September). A Comparative Analysis of Multi-agent Simulation Platforms for Energy and Mobility Management. In European Conference on Multi-Agent Systems (pp. 295-311). Cham: Springer Nature Switzerland.
  • Ali, G. A., Abubakar, H., Alzaeemi, S. A. S., Almawgani, A. H., Sulaiman, A., & Tay, K. G. (2023). Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation. Plos one, 18(9), e0286874.
  • Alkanjr, B., & Mahgoub, I. (2023). A Novel Deception-Based Scheme to Secure the Location Information for IoBT Entities. IEEE Access, 11, 15540-15554.
  • Allison, A., Stephens, S., Blackett, P., Lawrence, J., Dickson, M. E., & Matthews, Y. (2023). Simulating the Impacts of an Applied Dynamic Adaptive Pathways Plan Using an Agent-Based Model: A Tauranga City, New Zealand, Case Study. Journal of Marine Science and Engineering, 11(2), 343.
  • Almaguer, D., Islas, C., Padilla, P., Prado, M. A., & Vizuet, D. F. Manejo biológico de una plaga usando un modelo multiagentes. Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI, 10, 140-146.
  • Alsammak, I. L. H., Mahmoud, M. A., Gunasekaran, S. S., Ahmed, A. N., & AlKilabi, M. (2023). Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption. IEEE Access, 11, 50962-50983.
  • Altuwariki, S. (2023). Modelling land use using demographic forecasting and local optimisation: A case study of general education provision in Riyadh, Saudi Arabia (Doctoral dissertation, UCL (University College London)).
  • Alves, D. B., de Souza Junior, A. J., & da Motta Jafelice, R. S. (2023). Intelligence technologies in Mathematics Education: AnyLogic for the production of learning objects. Revista Internacional de Pesquisa em Educação Matemática, 13(3), 1-23.
  • Amakama, N. J., Dusserre, G., Cadiere, A., & Schuette, R. W. (2023, September). Assessing the Impact of Wait Times on Patient Mortality Outcomes in a Hypothetical Oil and Gas Industry Disaster Scenario: An Agent-Based Modeling Approach Using NetLogo. In QPSS 2023-Qatar Process Safety Symposium.
  • Amakama, N. J., Dusserre, G., Cadiere, A., Schuette, R. W., & Zacharewicz, G. (2023, April). Risk Management and Disaster Response In the Oil and Gas Industry: Modelling and Implementation of Interoperable Healthcare Systems Solution for Disaster Response in the Oil and Gas Industry. In Colloque IMT 2023:«Sécurité et Résilience».
  • Amissah, M. (2023). Modelling and analysis of heterogeneous data to improve process flow in the emergency department (Doctoral dissertation, University of Warwick).
  • Ammoneit, R., Reudenbach, C., & Peter, C. (2023). Developing geographic computer modeling competencies in higher education. Journal of Geography in Higher Education, 1-23.
  • Amparore, E., Beccuti, M., Castagno, P., Pernice, S., Franceschinis, G., & Pennisi, M. (2023). From compositional Petri Net modeling to macro and micro simulation by means of Stochastic Simulation and Agent-Based models. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 9(1), 1-30.
  • Amrita, S., & Sankaran, S. (2023, April). Modeling the Impact of Fake Data Dissemination During Covid-19. In International Symposium on Intelligent Informatics: Proceedings of ISI 2022 (pp. 471-486). Singapore: Springer Nature Singapore.
  • An, Y., & Park, S. (2023). Developing an Agent-Based Model to Mitigate Famine Risk in North Korea: Insights from the “Artificial North Korean Collective Farm” Model. Land, 12(4), 735.
  • An, Y., Lin, X., Li, H., & Wang, Y. (2023). Sandpile-simulation-based graph data model for MVD generative design of shield tunnel lining using information entropy. Advanced Engineering Informatics, 57, 102108.
  • Andelfinger, P., & Uhrmacher, A. M. (2023). Synchronous speculative simulation of tightly coupled agents in continuous time on CPUs and GPUs. SIMULATION, 00375497231158930.
  • Annette, H. (2023). Modellieren und Forschen: Zum Inhalt. Unterricht Biologie, 2023(482), 4-48.
  • Anusha, C. D., & Raju, K. G. (2023, November). RWA for multi-domain optical network using OBGP. In AIP Conference Proceedings (Vol. 2587, No. 1). AIP Publishing.
  • Apostolidis-Afentoulis, V., & Sakellariou, I. (2023). Teleo-Reactive Agents in a Simulation Platform. Proceedings of the 15th International Conference on Agents and Artificial Intelligence, 1, 26–36.
  • Appiagyei, B. D., Belhoucine-Guezouli, L., Bessah, E., & Morsli, B. (2023). Simulating land use and land cover change in a semi-arid region from 1989 to 2039: the case of Hafir-Zariffet forest, Tlemcen, Algeria. GeoJournal, 1-15.
  • Arcón, V., Caridi, I., Pinasco, J. P., & Schiaffino, P. (2023). Segregation patterns for non-homogeneous locations in Schellings model. Communications in Nonlinear Science and Numerical Simulation, 120, 107140.
  • Ardon, L., Vann, J., Garg, D., Spooner, T., & Ganesh, S. (2023, May). Phantom-A RL-driven Multi-Agent Framework to Model Complex Systems. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (pp. 2742-2744).
  • Aringhieri, R., Di Cunzolo, M., Dutto, M., Genga, L., Guastalla, A., Locatelli, M., ... & Boccuzzi, A. (2023). Simulation, optimization, and process mining: practicals applications in healthcare. In Proceedings of the 3rd National Conference on Artificial Intelligence (Ital-IA 2023) (pp. 1-6). CEUR Workshop Proceedings.
  • Ariza Angarita, Y. (2023). Auto organización para la innovación curricular: una mirada desde las universidades (Doctoral dissertation, Universidad Simón Bolívar, Venezuela).
  • Armitage, J., & Magnusson, T. (2023). Agential Scores: Exploring Emergent, Self-Organising and Entangled Music Notation. In Proceedings of the 8th International Conference on Technologies for Music Notation and Representation (Northeastern University, Boston, Massachusetts, USA, 2023).
  • Arnold, E. G., Burroughs, E. A., Burroughs, O., & Carlson, M. A. (2023). Using physical simulations to motivate the use of differential equations in models of disease spread. International Journal of Mathematical Education in Science and Technology, 1-14.
  • Assayed, S., & Maheshwari, P. (2023). Agent-Based Simulation for University Students Admission: Medical Colleges in Jordan Universities. Computer Science & Engineering: An International Journal (CSEIJ), 13(1).
  • Ataeizadeh, F. (2023). Different Fairness Perceptions in Different Fairness Problems: Algorithmic Decisions and Strategic Interactions (Doctoral dissertation, Carleton University).
  • Ávila, D. Spin-glass y la mano invisible de Adam Smith (Doctoral dissertation, Universidad Nacional de Colombia).
  • Awad, M. (2023). Analysis and modeling of what honey bees (Apis mellifera L.) bring back to the hive and how that affects the health of the hive and humans (Doctoral dissertation, Colorado State University).
  • Azari, B. (2023). Developing a New Three-Dimensional Finite-Difference Explicit in Time Solver Package for MODFLOW (Doctoral dissertation, The University of Memphis).
  • Azin, B. (2023). Integrated Charging Facility Developments and Incentive-Based Demand Management for Electric Vehicles Using Agent-Based Simulation (Doctoral dissertation, The University of Utah).
  • Baccino, L., & Villata, S. (2023, May). How Does a Minority Opinion Spread?: An Agent-Based Model on the Opposition Between a Silent Majority and a Loud Minority. In The International FLAIRS Conference Proceedings (Vol. 36).
  • Baden-Böhm, F., Dauber, J., & Thiele, J. (2023). Biodiversity measures providing food and nesting habitat increase the number of bumblebee (Bombus terrestris) colonies in modelled agricultural landscapes. Agriculture, Ecosystems & Environment, 356, 108649.
  • Bai, Y (2023). Research on Civil Engineering Cost Prediction Based on Decision Tree Algorithm. Academic Journal of Architecture and Geotechnical Engineering, 5(1), 39-44.
  • Bai, Y., Deng, X., Weng, C., Hu, Y., Zhang, S., & Wang, Y. (2023). Investigating climate adaptation in semi-arid pastoral social-ecological system: A case in Hulun Buir, China. Environmental and Sustainability Indicators, 100321.
  • Baldauf, T. (2023). sfctools-A toolbox for stock-flow consistent, agent-based models. Journal of Open Source Software, 8(87), 4980.
  • Banerjee, D. (2023). Knowledge and Innovation on the Road to Adoption of Green Infrastructure Technology: Stormwater Management in Fast-Growing, Arid-Climate Utah Cities and Towns (Doctoral dissertation, The University of Utah).
  • Barrett, M. C. (2023). Lithics in Perspective: Indeterminacy, Simulation, and the Formation of Lithic Assemblages (Doctoral dissertation, The University of Auckland, New Zealand).
  • Barricelli, B. R., Fischer, G., Fogli, D., Mørch, A., Piccinno, A., & Valtolina, S. (2023). Cultures of Participation in the Digital Age-Artificial and/or Human Intelligence: Nurturing Computational Fluency in the Digital Age.
  • Barth, L., Schweiger, L., Benedech, R., & Ehrat, M. (2023). From data to value in smart waste management: Optimizing solid waste collection with a digital twin-based decision support system. Decision Analytics Journal, 100347.
  • Batta, E., & Stephens, C. R. (2023). Evolutionary success of the thrifty genotype depends on both behavioral adaptations and temporal variability in the food environment. Scientific Reports, 13(1), 7975.
  • Batzke, M., & Ernst, A. (2023). Conditions and Effects of Norm Internalization. Journal of Artificial Societies and Social Simulation, 26(1).
  • Bayram, A. (2023). Hybrid LCA–ABM of dairy farming systems including nonlinear optimization under environmental, technical and economic constraints (Doctoral dissertation, University of Luxembourg,​​ Luxembourg).
  • Becote, B. (2023). Defining a Cyber Operations Performance Framework via Computational Modeling (Doctoral dissertation, Dakota State University).
  • Beerman, J. T. (2023). To Err Is Human: The Effect of Mistakes in Social Simulations (Doctoral dissertation, Miami University).
  • Beerman, J. T., Beaumont, G. G., & Giabbanelli, P. J. (2023). A framework for the comparison of errors in agent-based models using machine learning. Journal of computational science, 72, 102119.
  • Belcore, O. M., Di Gangi, M., & Polimeni, A. (2023). Connected Vehicles and Digital Infrastructures: A Framework for Assessing the Port Efficiency. Sustainability, 15(10), 8168.
  • Béler, C., Zacharewicz, G., Bisgambiglia, P. A., Poggi, B., Poux, F., & Thierry, A. S. (2024). Towards (An Aggregated) Territorial Digital Twin: From Smart-village to Smart-territory via the Territorial System of Digital Twin. In Concepts in Smart Societies (pp. 328-356). CRC Press.
  • Bell, A.R., Rakotonarivo, O.S., Bhargava, A., Duthie, A.B., Zhang, W., Sargent, R., Lewis, A.R. and Kipchumba, A., (2023). Financial incentives often fail to reconcile agricultural productivity and pro-conservation behavior. Communications Earth & Environment, 4(1), 27.
  • Bellvé, A. M. (2023). Reconstructing animal-vectored nutrient fluxes in paleoenvironments: A case-study of Aotearoa New Zealand’s burrowing procellariiforms (Doctoral dissertation, ResearchSpace@ Auckland).
  • Bemthuis, R., Govers, R., & Lazarova-Molnar, S. (2023, October). Using process mining for face validity assessment in agent-based simulation models: an exploratory case study. In International Conference on Cooperative Information Systems (pp. 311-326). Cham: Springer Nature Switzerland.
  • Benham, S. S. (2023). Landscape Genetics of the Gulf Coast Tick, Amblyomma maculatum (Doctoral dissertation, Old Dominion University).
  • Bennai, M. T., Guessoum, Z., Mazouzi, S., Cormier, S., & Mezghiche, M. (2023). Multi-agent medical image segmentation: A survey. Computer Methods and Programs in Biomedicine, 107444.
  • Berger, T., Bonte, T., Idel Mahjoub, Y., & Sallez, Y. (2023). Proposition of a software-assisted methodology to solve safety issues in reconfigurable assembly systems in a short time. International Journal of Computer Integrated Manufacturing, 1-26.
  • Bernardini, G., D’Orazio, M., & Quagliarini, E. (2023, September). Coupled Multi-risk Mitigation in Historical Urban Outdoor Built Environment: Preliminary Strategies Evaluation Through Typological Scenarios. In International Conference on Structural Analysis of Historical Constructions (pp. 1212-1226). Cham: Springer Nature Switzerland.
  • Bhat, S., Godse, R., Mestry, S., & Naik, V. (2023). Studying the Impact of Transportation During Lockdown on the Spread of COVID-19 Using Agent-Based Modeling. In ICAART (1) (pp. 80-92).
  • Biondo, A. E., Mazzarino, L., & Pluchino, A. (2023). Noise and Financial Stylized Facts: A Stick Balancing Approach. Entropy, 25(4), 557.
  • Bischoff, R. J., & Padilla-Iglesias, C. (2023). A description and sensitivity analysis of the ArchMatNet agent-based model. PeerJ Computer Science, 9, e1419.
  • Bjørnås, K. L., Railsback, S., & Piccolo, J. (2023). Modifying and parameterizing the individual-based model inSTREAM for Atlantic salmon and brown trout in the regulated Gullspång River, Sweden. MethodsX, 102243.
  • Blake-Westa, J. C., & Bersa, M. U. (2023). ScratchJr design in practice: Low floor, high ceiling. International Journal of Child-Computer Interaction, 100601.
  • Blakely, B., Horsthemke, W., Evans, N., & Harkness, D. (2023). Case Study A: A Prototype Autonomous Intelligent Cyber-Defense Agent. In Autonomous Intelligent Cyber Defense Agent (AICA) A Comprehensive Guide (pp. 395-408). Cham: Springer International Publishing.
  • Bogani, A., Faccenda, G., Riva, P., Richetin, J., Pancani, L., & Sacchi, S. (2023). The near-miss effect in flood risk estimation: A survey-based approach to model private mitigation intentions into agent-based models. International Journal of Disaster Risk Reduction, 103629.
  • Bolea Pérez, D. (2023). Integration of social values in a multi-agent platform running in a supercomputer (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Boman, B. M., Dinh, T. N., Decker, K., Emerick, B., Modarai, S., Opdenaker, L., ... & Schleiniger, G. (2023). Beyond the Genetic Code: A Tissue Code?. bioRxiv, 2023-03.
  • Bommi, R. M., Rajeev, S. V. S., Navya, S., Teja, V. S., & Supriya, U. (2023). Smart Health Care Waste Segregation and Safe Disposal. Mathematics and Computer Science, Volume 2, 205.
  • Borah, D. K., Zhang, H., Zellner, M., Ahmadisharaf, E., Babbar-Sebens, M., Quinn, N., ... & Lott, C. (2023). Total Maximum Daily Load Implementation Modeling, Planning, and Design: A Synthesis of Resources for Watershed Stakeholders. In World Environmental and Water Resources Congress 2023 (pp. 1298-1312).
  • Bort, J., Wiklund, J., Crawford, G. C., Lerner, D. A., & Hunt, R. A. (2023). The Strategic Advantage of Impulsivity in Entrepreneurial Action: An Agent-Based Modeling Approach. Entrepreneurship Theory and Practice, 10422587231178882.
  • Boss, L. N. (2023). Exploring Decentralized System Architectures and Their Influence on Performance and Robustness (Doctoral dissertation, The George Washington University).
  • Bourceret, A., Amblard, L., & Mathias, J. D. (2023). How do farmers’ environmental preferences influence the efficiency of information instruments for water quality management? Evidence from a social-ecological agent-based model. Ecological Modelling, 478, 110300.
  • Bowen, G. M., Wiseman, D., Shanahan, M. C., Khan, S., Gonsalves, A., Sengupta, P., ... & Carter, A. (2023). STEM in Canadian Teacher Education: An Overview. Reforming Science Teacher Education Programs in the STEM Era: International and Comparative Perspectives, 53-70.
  • Bozzi, A., Jimenez, J. F., Hernandez-Rodriguez, C., Gonzalez-Neira, E. M., & Trentesaux, D. (2023, July). Platoon-Based Distributed Control for Automated Material Handling Systems. In 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 2257-2262). IEEE.
  • Brazier, F. (2023, January). The Influence of National Culture on Evacuation Response Behaviour and Time: An Agent-Based Approach. In Multi-Agent-Based Simulation XXIII: 23rd International Workshop, MABS 2022, Virtual Event, May 8–9, 2022, Revised Selected Papers (Vol. 13743, p. 41). Springer Nature.
  • Breitwieser, L., Hesam, A., Rademakers, F., Luna, J. G., & Mutlu, O. (2023, February). High-performance and scalable agent-based simulation with BioDynaMo. In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (pp. 174-188).
  • Brewer, L. L. (2023). Distruptions in Supply Chain: An Agent-Based Model Simulation to Measure Resiliency and Performance During Disasters (Doctoral dissertation, The University of North Carolina at Charlotte).
  • Briggs, T. W. (2023). Essays in Computational Social and Organization Science: Manager-Subordinate Proximity, Informal Networks, and the Flynn Effect (Doctoral dissertation, George Mason University).
  • Brocardo, J., Vale, I., & Menezes, L. (2022). A investigação em resolução de problemas, raciocínio, comunicação e modelação: Uma análise de 30 anos de publicações na revista Quadrante. Quadrante, 31(2), 63-93.
  • Brodsky, J. The Role of Astrobiology in Systems Thinking Education. In Guidebook for Systems Applications in Astrobiology (pp. 210-222). CRC Press.
  • Broitman, D., & Czamanski, D. (2023). Resilience in a noisy urban system. Regional Science Policy & Practice.
  • Broutin, L. (2023). Prospecteurs des dunes et d'ailleurs: une étude géographique de l'expertise antiacridienne en Mauritanie (Doctoral dissertation, Université Paris-Nanterre).
  • Brudney, E. M. (2023). Building More Inclusive University Makerspaces for Students with Disabilities (Doctoral dissertation, The University of North Carolina at Chapel Hill).
  • Bryndin, E. (2023). Development of Artificial Intelligence of Ensembles of Software and Hardware Agents by Natural Intelligence on the Basis of Self-Organization. Journal of Research in Social Science and Humanities, 2(10), 13-22.
  • Bui, H., Sakurahara, T., Reihani, S., Kee, E., & Mohaghegh, Z. (2023). Probabilistic Validation: Computational Platform and Application to Fire Pra of Nuclear Power Plants. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 1-42.
  • Burrow, A. K., McEntire, K. D., & Maerz, J. C. (2023). Estimating the potential drivers of dispersal outcomes for juvenile gopher frogs (Rana capito) using agent-based models. Frontiers in Ecology and Evolution, 11, 1026541.
  • Bwire, C., Mohan, G., Karthe, D., Caucci, S., & Pu, J. (2023). A Systematic Review of Methodological Tools for Evaluating the Water, Energy, Food, and One Health Nexus in Transboundary Water Basins. Environmental Management, 1-16.
  • Calabrò, G., Le Pira, M., Giuffrida, N., Fazio, M., Inturri, G., & Ignaccolo, M. (2023). A spatial agent-based model of e-commerce last-mile logistics towards a delivery-oriented development. Transportation Research Interdisciplinary Perspectives, 21, 100895.
  • Calafat Montes, M. (2023). A Ssimulation model of passenger flow at the airport security system (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Calay, T. J., Qolomany, B., Mulahuwaish, A., Hossain, L., & Abdo, J. B. (2023, September). CCTFv1: Computational Modeling of Cyber Team Formation Strategies. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 199-208). Cham: Springer Nature Switzerland.
  • Calero Valdez, A., Nakayama, J., Vervier, L., Nunner, H., & Ziefle, M. (2023, July). Using Agent-Based Modeling to Understand Complex Social Phenomena-A Curriculum Approach. In International Conference on Human-Computer Interaction (pp. 368-377). Cham: Springer Nature Switzerland.
  • Cao, Z., Zhu, J., Tang, B., & Chen, T. (2023). System dynamics simulation of occupational health and safety management causal model based on NetLogo. Heliyon.
  • Caprioli, C., Bottero, M., & De Angelis, E. (2023). Combining an agent-based model, hedonic pricing and multicriteria analysis to model green gentrification dynamics. Computers, Environment and Urban Systems, 102, 101955.
  • Carrillo, C. C., Charbonneau, B. R., Altman, S., Keele, J. A., Pucherelli, S. F., Passamaneck, Y. J., ... & Swannack, T. M. (2023). Patterns of dreissenid mussel invasions in western US lakes within an integrated gravity model framework. Journal of Environmental Management, 332, 117383.
  • Castañón-Puga, M., Rosales-Cisneros, R. F., Acosta-Prado, J. C., Tirado-Ramos, A., Khatchikian, C., & Aburto-Camacllanqui, E. (2023). Earned Value Management Agent-Based Simulation Model. Systems, 11(2), 86.
  • Castañón–Puga, M., Tirado–Ramos, A., Khatchikian, C., Suarez, E. D., Palafox–Maestre, L. E., & Gaxiola–Pacheco, C. G. (2023, June). Towards an Earned Value Management Didactic Simulator to Engineering Management Teaching. In International Conference on Computational Science (pp. 780-792). Cham: Springer Nature Switzerland.
  • Catola, M., & Leoni, S. (2023). Pollution Abatement and Lobbying in a Cournot Game: An Agent-Based Modelling Approach. Computational Economics, 1-28.
  • Cavallo, D., & Briceño, A. (2023). Rizomas, epistemología y aprendizaje: reformulación y reestructuración de los entornos de aprendizaje. Revista 180, 1.
  • Čech, P., Mattoš, M., Anderková, V., Babič, F., Alhasnawi, B. N., Bureš, V., ... & Triantafyllou, I. (2023). Architecture-Oriented Agent-Based Simulations and Machine Learning Solution: The Case of Tsunami Emergency Analysis for Local Decision Makers. Information, 14(3), 172.
  • Cegielski, W. H. (2024). Networks, Agent-Based Modeling, and Archaeology. The Oxford Handbook of Archaeological Network Research, 280.
  • Celik, B., & Zorba, Y. (2023). AN APPLICATION OF AGENT-BASED TRAFFIC FLOW MODEL FOR MARITIME SAFETY MANAGEMENT EVALUATION. International Journal of Maritime Engineering, 165(A1), 55-70.
  • ÇETİNER, B., & YAŞARCAN, H. The El Farol Bar Problem: A Comparative Analysis of Expectation Models Used in Decision Making. Endüstri Mühendisliği, 34, 90-108.
  • Chaabani, S., Einum, S., Jaspers, V. L., Asimakopoulos, A. G., Zhang, J., & Muller, E. (2023). Impact of the antidepressant Bupropion on the Dynamic Energy Budget of Daphnia magna. Science of The Total Environment, 895, 164984.
  • Chatterjee, A., Cao, Q., Sajadi, A., & Ravandi, B. (2023). Deterministic random walk model in NetLogo and the identification of asymmetric saturation time in random graph. Applied Network Science, 8(1), 1-11.
  • Chella, A., Gaglio, S., Mannone, M., Pilato, G., Seidita, V., Vella, F., & Zammuto, S. (2023). Quantum planning for swarm robotics. Robotics and Autonomous Systems, 104362.
  • Chen, J., & Wilensky, U. J. (2023). Measuring Young Learners’ Open-ended Agent-based Programming Practices with Learning Analytics. Proceedings of AERA Annual Meeting 2023.
  • Chen, J., & Wilensky, U. J. (2023). Tortuga: Building Interactive Scaffolds for Agent-based Modeling and Programming in NetLogo. Proceedings of ISLS Annual Meeting 2023.
  • Chen, J., Horn, M. S., & Wilensky, U. J. (2023, June). NetLogo AR: Bringing Room-Scale Real-World Environments Into Computational Modeling for Children. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (pp. 736-739).
  • Chen, J., Zhao, L., Horn, M. S., & Wilensky, U. J. (2023, June). The Pocketworld Playground: Engaging Online, Out-of-School Learners with Agent-based Programming. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (pp. 267-277).
  • Chen, X., Wang, Z., Yang, H., Ford, A. C., & Dawson, R. J. (2023). Enhanced urban growth modelling: Incorporating regional development heterogeneity and noise reduction in a cellular automata model-a case study of Zhengzhou, China. Sustainable Cities and Society, 99, 104959.
  • Chen, Y., Wang, C., Du, X., Shen, Y., & Hu, B. (2023). An agent-based simulation framework for developing the optimal rescue plan for older adults during the emergency evacuation. Simulation Modelling Practice and Theory, 102797.
  • Cheng, S., Giabbanelli, P. J., & Kuang, Z. (2023, May). Identifying the Building Blocks of Social Simulation Models: A Qualitative Analysis using Open-Source Codes in NetLogo. In 2023 Annual Modeling and Simulation Conference (ANNSIM) (pp. 306-317). IEEE
  • Cheng, Y. (2023). Significance of Digital Landscape Architecture. In Digital Landscape Architecture: Logic, Structure, Method and Application (pp. 7-44). Singapore: Springer Nature Singapore.
  • Chetouani, M., Dignum, V., Lukowicz, P., & Sierra, C. (2023). The Advanced Course on Human-Centered AI: Learning Objectives. In Human-Centered Artificial Intelligence: Advanced Lectures (pp. 3-7). Cham: Springer International Publishing.
  • Chica, M., Hermann, R. R., & Lin, N. (2023). Adopting different wind-assisted ship propulsion technologies as fleet retrofit: An agent-based modeling approach. Technological Forecasting and Social Change, 192, 122559.
  • Chiriță, N., Delcea, C., Nica, I., & IONESCU, Ş. A. (2023). Financial contagion and identifying speculative frenzies: Unraveling price bubbles in cryptocurrency markets. Theoretical & Applied Economics, 30(3).
  • Chour, K., Reddinger, J. P., Dotterweich, J., Childers, M., Humann, J., Rathinam, S., & Darbha, S. (2023). An agent-based modeling framework for the multi-UAV rendezvous recharging problem. Robotics and Autonomous Systems, 166, 104442.
  • Christen Marote, S. (2023). Análisis de sistemas e instalaciones en torno a la arquitectura regenerativa, eficiencia energética y optimización de recursos naturales (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Christensen, K., Ma, Z. G., & Jørgensen, B. N. (2023). Multi-Agent Based Simulation for Investigating Electric Vehicle Adoption and Its Impacts on Electricity Distribution Grids and CO2 Emissions. In Energy Informatics. Academy Conference 2023.
  • Chu, C. M., & Van Noi, N. (2023). Optimising truck arrival management and number of service gates at container terminals. Maritime Business Review, 8(1), 18-31.
  • Chu, J., Morikawa, H., & Chen, Y. (2023). Simulation of SARS-CoV-2 epidemic trends in Tokyo considering vaccinations, virus mutations, government policies and PCR tests. BioScience Trends, 17(1), 38-53.
  • Chueca Del Cerro, C. (2023). Polarisation and protest mobilisation around secessionist movements: an agent-based model of online and offline social networks (Doctoral dissertation, University of Glasgow).
  • Cinquemani, L. (2023). Nationsim, a story-driven approach to Agent-Based Modeling of Nations interacting (Master's thesis, NTNU).
  • Cline, D. H., & Munson, J. (2024). Epigraphic Networks in Cross-Cultural Perspective. The Oxford Handbook of Archaeological Network Research, 363.
  • Cockx, B. J. R., Foster, T., Clegg, R. J., Alden, K., Arya, S., Stekel, D. J., ... & Kreft, J. U. (2023). Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0. bioRxiv, 2023-06.
  • Coelho, H. (2023). Interdisciplinary Practice in Education. In Theory and Practice in the Interdisciplinary Production and Reproduction of Scientific Knowledge (pp. 147-155). Springer, Cham.
  • Cogoni, F., Bernard, D., Kazhen, R., Valitutti, S., Lobjois, V., & Cussat-Blanc, S. (2023). ISiCell: involving biologists in the design process of agent-based models in cell biology. bioRxiv, 2023-06.
  • Corlu, M. S., Kurutas, B. S., & Ozel, S. (2023). Effective Online Professional Development: A Facilitator's Perspective. In M. Ludwig, S. Barlovits, A. Caldeira, & A. Moura (Eds.), Research On STEM Education in the Digital Age. Proceedings of the ROSEDA Conference (pp. 9-23). WTM.
  • Cortés, C. E., & Stefoni, B. (2023). Trajectory Simulation of Emergency Vehicles and Interactions with Surrounding Traffic. Journal of Advanced Transportation, 2023.
  • Costas, J., Puche, J., Ponte, B., Gupta, M. (2023). An agent-based simulator for quantifying the cost of uncertainty in production systems. Simulation Modelling Practice and Theory, 123.
  • Covitt, B. A., Gunckel, K. L., Berkowitz, A., Woessner, W. W., & Moore, J. (2023). Employing a Groundwater Contamination Learning Experience to Build Proficiency in Computational Modeling for Socioscientific Literacy. Journal of Science Education and Technology, 1-23.
  • Crespi, C., Scollo, R. A., Fargetta, G., & Pavone, M. (2023, February). How a Different Ant Behavior Affects on the Performance of the Whole Colony. In Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings (pp. 187-199). Cham: Springer International Publishing.
  • Crespi, C., Scollo, R. A., Fargetta, G., & Pavone, M. (2023). A sensitivity analysis of parameters in an agent-based model for crowd simulations. Applied Soft Computing, 146, 110684.
  • Crevier, L. P. (2023). Bears, spirals, and stakeholders: agent-based models and the need for stakeholder involvement in their development and implementation (Doctoral dissertation, University of British Columbia).
  • Cuevas, E., Zaldívar, D., & Pérez-Cisneros, M. (2023). Exploring the Potential of Agent Systems for Metaheuristics. In New Metaheuristic Schemes: Mechanisms and Applications (pp. 11-74). Cham: Springer Nature Switzerland.
  • Cuevas, E., Zaldívar, D., & Pérez-Cisneros, M. (2023). New Metaheuristic Schemes: Mechanisms and Applications (Vol. 246). Springer Nature.
  • Cui, T., & Cao, S. (2023, August). 2024 World Scientific Publishing Company. In Internet Finance And Digital Economy: Advances In Digital Economy And Data Analysis Technology-Proceedings Of The 2nd International Conference (p. 275). World Scientific.
  • Cui, T., & Cao, S. (2024). Development of the Female Internet Celebrity Economy Based on MATLAB Analysis Mode. In INTERNET FINANCE AND DIGITAL ECONOMY: Advances in Digital Economy and Data Analysis Technology The 2nd International Conference on Internet Finance and Digital Economy, Kuala Lumpur, Malaysia, 19–21 August 2022 (pp. 275-288).
  • da Silva Gallo, E. R., Bertella, M. A., & da Fonseca, C. N. (2023). Aversão à perda em um mercado acionário virtual: uma abordagem agent-based. Revista de Economia, 43(81), 442-471.
  • Daems, D., & Boogers, S. (2023). The Power of Emergence: The Effects of Bottom-Up Decision-Making in Resource Exploitation Strategies on Community Sustainability in Iron Age to Hellenistic Anatolia. In Modelling Human-Environment Interactions in and beyond Prehistoric Europe (pp. 133-142). Cham: Springer International Publishing.
  • Dagienė, V., Gülbahar, Y., Grgurina, N., López-Pernas, S., Saqr, M., Apiola, M., & Stupurienė, G. (2023). Computing Education Research in Schools. In Past, Present and Future of Computing Education Research: A Global Perspective (pp. 481-520). Cham: Springer International Publishing.
  • Davey, T. (2023). Cohesion: A Measure of Organisation and Epistemic Uncertainty of Incoherent Ensembles. Entropy, 25(12), 1605.
  • David, J., & Wu, J. (Eds.). (2023). Mathematics of Public Health: Mathematical Modelling from the Next Generation (Vol. 88). Springer Nature.
  • Davis, P. K. (2023). Supporting Social Science and Management Areas. In Body of Knowledge for Modeling and Simulation: A Handbook by the Society for Modeling and Simulation International (pp. 373-382). Cham: Springer International Publishing.
  • De Cubber, L., Lefebvre, S., Lancelot, T., Jorge, D. S. F., & Gaudron, S. M. (2023). Unravelling mechanisms behind population dynamics, biological traits and latitudinal distribution in two benthic ecosystem engineers: A modelling approach. Progress in Oceanography, 103154.
  • de Gauna, D. E. R., Sánchez, L. E., Ruiz-Iniesta, A., Villalonga, C., & Serrano, M. A. (2023). Towards an integrated swarm intelligence framework for urban mobility: A systematic review and proposed theoretical model. Journal of King Saud University-Computer and Information Sciences, 101836.
  • de Jager, M., Buitendijk, N. H., Baveco, J. M., van Els, P., & Nolet, B. A. (2023). Limiting scaring activities reduces economic costs associated with foraging barnacle geese: Results from an individual‐based model. Journal of Applied Ecology.
  • De La Paz, S., Levin, D. M., & Butler, C. (2023). Addressing an Unfulfilled Expectation: Teaching Students With Disabilities to Write Scientific Arguments. Written Communication, 07410883221149093.
  • De Luca, G., & Simoni, M. (2023). The role of trust in the diffusion of privacy-invading digital technologies. Technology Analysis & Strategic Management, 1-14.
  • De Nicola, R., Di Stefano, L., Inverso, O., & Valiani, S. (2023). Modelling flocks of birds and colonies of ants from the bottom up. International Journal on Software Tools for Technology Transfer, 1-17.
  • de Sousa Oliveira, K. K., da Silva Marcolino, A., de Deus, W. S., Falcão, T. P., & Barbosa, E. F. (2023). Pensamento Computacional na Programação Introdutória e Habilidades do Século XXI: Um Mapeamento Sistemático da Literatura. Revista Novas Tecnologias na Educação, 21(2), 519-531.
  • de Souza, G. F., & Lopes, P. T. C. (2023). APLICAÇÃO DO PENSAMENTO COMPUTACIONAL NO ENSINO, UMA REVISÃO SISTEMÁTICA DE LITERATURA. Interfaces Científicas-Educação, 12(1), 144-165.
  • Debacher, N. M., Kuster, L. F., dos Santos, A. F., Vahldick, A., & Santos, F. (2023, September). Back to the Promotion-EvacSIM: a Serious Game to Practice Requirements Elicitation on an Agent-based Simulation. In Anais do XX Encontro Nacional de Inteligência Artificial e Computacional (pp. 169-183). SBC.
  • Debie, E., Kasmarik, K., & Garratt, M. (2023). Swarm robotics: A Survey from a Multi-tasking Perspective. ACM Computing Surveys.
  • DelaPaz-Ruíz, N., Augustijn, E. W., Farnaghi, M., & Zurita-Milla, R. (2023). Spatiotemporal domestic wastewater variability: Assessing implications of population mobility in pollutants dynamics. AGILE: GIScience Series, 4, 23.
  • Delcea, C., & Chirita, N. (2023). Exploring the Applications of Agent-Based Modeling in Transportation. Applied Sciences, 13(17), 9815.
  • Delcea, C., & Cotfas, L. A. (2023). Risk Assessment and Transport Cost Reduction Based on Grey Clustering. In Advancements of Grey Systems Theory in Economics and Social Sciences (pp. 139-178). Singapore: Springer Nature Singapore.
  • Delcea, C., Yang, Y., Liu, S., & Cotfas, L. A. (2023). Agent-Based Modelling in Grey Economic Systems. In Emerging Studies and Applications of Grey Systems (pp. 105-139). Singapore: Springer Nature Singapore.
  • Deshpande, S., & Hsieh, S. J. (2023). Cyber-Physical System for Smart Traffic Light Control. Sensors, 23(11), 5028.
  • Dimka, J. (2023). An Agent-Based Simulation Model of Epidemic Spread in a Residential School for Children with Disabilities. Scandinavian Journal of Disability Research, 25(1).
  • Ding, H., & Xie, L. (2023). Simulating rumor spreading and rebuttal strategy with rebuttal forgetting: An agent-based modeling approach. Physica A: Statistical Mechanics and its Applications, 128488.
  • do Amaral Pinto, G., & Alberte, E. P. V. (2023). Modelagem baseada em agentes aplicada a estudos para incorporação imobiliária: um panorama acerca da literatura. SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E COMUNICAÇÃO NA CONSTRUÇÃO, 4, 1-11.
  • Domenteanu, A., Delcea, C., Chiriță, N., & Ioanăș, C. (2023). From Data to Insights: A Bibliometric Assessment of Agent-Based Modeling Applications in Transportation. Applied Sciences, 13(23), 12693.
  • Dong, D. (2023). Agent-based cloud simulation model for resource management. Journal of Cloud Computing, 12(1), 1-24.
  • Dong, J., Tian, M., Li, X., & Crossan, M. Effects of human capital and learning rate: When organizations meet with information distortion and environmental dynamism. European Management Review.
  • Doran, J. W., Thompson, R. N., Yates, C. A., & Bowness, R. (2023). Mathematical methods for scaling from within-host to population-scale in infectious disease systems. Epidemics, 100724.
  • Doroudi, S. (2023). The forgotten African American innovators of educational technology: stories of education, technology, and civil rights. Learning, Media and Technology, 1-17.
  • dos Santos, N. T., dos Prazeres, J. B., Braga, R. M., & do Espírito Santo, A. O. A DIMENSÃO CRÍTICA DA MODELAGEM MATEMÁTICA E DA EDUCAÇÃO AMBIENTAL: REVISÃO DE LITERATURA E APLICAÇÃO PRÁTICA. MODELAGEM MATEMÁTICA: RE/CONSTRUÇÃO DE PERSPECTIVAS, 15.
  • Du, W., Zhu, S., Tong, L., Cai, K., & Liang, Z. (2023). Robust gate assignment to minimise aircraft conflicts. Transportmetrica B: Transport Dynamics, 11(1), 2185497.
  • Dutcher, K. E., Nussear, K. E., Heaton, J. S., Esque, T. C., & Vandergast, A. G. (2023). Move it or lose it: Predicted effects of culverts and population density on Mojave desert tortoise (Gopherus agassizii) connectivity. Plos one, 18(9), e0286820.
  • Ebrie, A. S., Paik, C., Chung, Y., & Kim, Y. J. (2023). Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning. Energies, 16(16), 5920.
  • Edali, M. (2023). Using linear regression metamodels for evaluating interventions in an individual-based influenza epidemic model. Simulation Modelling Practice and Theory, 126, 102772.
  • Egger, C., Mayer, A., Bertsch-Hörmann, B., Plutzar, C., Schindler, S., Tramberend, P., ... & Gaube, V. (2023). Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning. Agronomy for Sustainable Development, 43(3), 39.
  • Eglash, R. (2023). Ethno-biomathematics: A Decolonial Approach to Mathematics at the Intersection of Human and Nonhuman Design. In Ubiratan D’Ambrosio and Mathematics Education: Trajectory, Legacy and Future (pp. 289-303). Cham: Springer International Publishing.
  • El-Maghraby, I. M., Jahin, H., & El-Hagla, K. S. (2023, September). Computational-based Generative Design Exploration, Multi-Agent System as an Approach. In LET IT GROW, LET US PLAN, LET IT GROW. Nature-based Solutions for Sustainable Resilient Smart Green and Blue Cities. Proceedings of REAL CORP 2023, 28th International Conference on Urban Development, Regional Planning and Information Society (pp. 145-154). CORP–Competence Center of Urban and Regional Planning.
  • Elara, L., & McCarthy, K. S. (2023). Exploring Supports to Enhance Learning from Online Virtual Experiments in Science. American Journal of Distance Education, 1-19.
  • Elsayed, P., Mostafa, H., & Marzouk, M. (2023). BIM based framework for building evacuation using Bluetooth Low Energy and crowd simulation. Journal of Building Engineering, 106409.
  • Elsner, J., Sadler, T., Kirk, E., Rawson, R., Friedrichsen, P., & Ke, L. (2023). Using Multiple Models to Learn about COVID-19 Breadcrumb. The Science Teacher, 90(3).
  • EMELYANOV, I., KIRILCHUK, I., BARKOV, A., & PERSIDSKAYA, K. (2023). USE OF INTELLIGENT TRANSPORT SYSTEMS TO IMPROVE ENVIRONMENTAL SAFETY OF ROAD TRANSPORT IN THE KURSK REGION. Мир транспорта и технологических машин, 59.
  • Epstein, J. M. (2023). Inverse generative social science: Backward to the future. Journal of artificial societies and social simulation: JASSS, 26(2).
  • Erceg, M. (2023). Primjena modeliranja temeljenog na agentima u simulaciji tržišta (Doctoral dissertation, University of Split. Faculty of economics Split).
  • Escobar, H., Cuevas, E., Toski, M., Ceron, F., & Perez-Cisneros, M. (2023). An agent-based model for public security strategies by predicting crime patterns. IEEE Access.
  • Eslamizadeh, S., Ghorbani, A., & Weijnen, M. (2023). Establishing industrial community energy systems: Simulating the role of institutional designs and societal attributes. Journal of Cleaner Production, 138009.
  • Esmaelnezhad, D., Taghizadeh-Yazdi, M., Mahdiraji, H. A., & Vrontis, D. (2023). International strategic alliances for collaborative product Innovation: An agent-based scenario analysis in biopharmaceutical industry. Journal of Business Research, 158, 113663.
  • Essghaier, F., Chargui, T., Hsu, T., Bekrar, A., Allaoui, H., Trentesaux, D., & Goncalves, G. (2023). Fuzzy multi-objective truck scheduling in multi-modal rail-road Physical Internet hubs. Computers & Industrial Engineering, 109404.
  • Estrada-Jimenez, L. A., Kalateh, S., Hojjati, S. N., & Barata, J. (2023, June). A Bio-inspired and Altruistic-Based Framework to Support Collaborative Healing in a Smart Manufacturing Shop-Floor. In Doctoral Conference on Computing, Electrical and Industrial Systems (pp. 111-121). Cham: Springer Nature Switzerland.
  • Estrada-Jimenez, L. A., Pulikottil, T. B., Nikghadam-Hojjati, S., & Barata, J. (2023). Self-organization in Smart Manufacturing-Background, Systematic Review, Challenges and Outlook. IEEE Access.
  • Evans, B. (2023). Strategic decision-making in multi-agent markets: The emergence of endogenous crises and volatility (Doctoral dissertation).
  • Farhat, D. (2023). The economics and evolution of heroic behavior. Theoretical & Applied Economics, 30(3).
  • Fayad, P., Hadjipetrou, S., Leventis, G., Kavroudakis, D., & Kyriakidis, P. (2023). Designing an Agent-Based Model for a City-Level Simulation of COVID-19 Spread in Cyprus. In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023) (pp. 218-224).
  • Feng, J. R., Zhao, M., Yu, G., Zhang, J., & Lu, S. (2023). Dynamic risk analysis of accidents chain and system protection strategy based on complex network and node structure importance. Reliability Engineering & System Safety, 109413.
  • Feng, J., Liu, B., Tang, J., & Wang, Q. E. (2023). The Emergence of the Contractor’s Innovation Capability at Project Level: An Agent-Based Modeling Approach. Buildings, 13(12), 2941.
  • Feng, S., Laili, Y., Zhang, L., & Zhang, L. (2023, January). Model Library System Based on Multi-Domain Simulation Model Integration Interface. In Intelligent Networked Things: 5th China Conference, CINT 2022, Urumqi, China, August 7-8, 2022, Revised Selected Papers (pp. 491-500). Singapore: Springer Nature Singapore.
  • Feng, Y., Zhou, C., Zou, Q., Liu, Y., Lyu, J., & Wu, X. (2023). A goal-based approach for modeling and simulation of different types of system-of-systems. Journal of Systems Engineering and Electronics, 34(3), 627-640.
  • Ferrare, F. D. Otimização do UAM usando modelagem de VTOL baseada em sistemas multiagentes (Doctoral dissertation, Universidade de São Paulo).
  • Ferreyra Coroy, V. M. (2023). Introducción al concepto de integral mediante un contexto de aproximación de la longitud de una curva asociada a la columna vertebral con apoyo de tecnología digital (Master's thesis, Tesis (MC)--Centro de Investigación y de Estudios Avanzados del IPN Departamento de Matemática Educativa).
  • Feuerwerker, S., Cockrell, R. C., & An, G. (2023). Characterizing the Crosstalk Between Programmed Cell Death Pathways in Cytokine Storm With an Agent-Based Model. Surgical Infections.
  • Fikirli, Ö., & Şahin, H. Türkiye’de e-ticaret difüzyon patikası: Ajan bazlı modelleme. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(2), 265-283.
  • Filandri, M., Pasqua, S., & Priori, E. (2023). Breaking through the glass ceiling. Simulating policies to close the gender gap in the Italian academia. Socio-Economic Planning Sciences, 88, 101655.
  • Fischer, H., Wijermans, N., & Schlüter, M. (2023). Testing the Social Function of Metacognition for Common‐Pool Resource Use. Cognitive Science, 47(3), e13212.
  • Flores López, A. C. (2023). Modelización basada en el individuo de sistemas de tratamiento anaerobio en biopilas de suelos contaminados por hidrocarburos de petróleo (Bachelor's thesis, Quito: UCE).
  • Foini, D., Rzyska, M., Baschmakov, K., & Murino, S. (2023). CrowdLogo: crowd simulation in NetLogo. arXiv preprint arXiv:2302.11036.
  • Francos, R. M., & Bruckstein, A. M. (2023, September). Spiral Sweeping Protocols for Detection of Smart Evaders. In Annual Conference Towards Autonomous Robotic Systems (pp. 89-100). Cham: Springer Nature Switzerland.
  • Francos, R. M., & Bruckstein, A. M. (2023). Guaranteed Evader Detection in Multi-Agent Search Tasks using Pincer Trajectories. arXiv preprint arXiv:2305.00533.
  • Freire, M., Marichal, R., Dufrechou, E., & Ezzatti, P. (2023, August). in Sparse Matrix Kernels. In Cloud Computing, Big Data & Emerging Topics: 11th Conference, JCC-BD&ET 2023, La Plata, Argentina, June 27–29, 2023, Proceedings (p. 17). Springer Nature.
  • Fuhrmann, T., Wagh, A., Rosenbaum, L. F., Eloy, A., Wilkerson, M., & Blikstein, P. (2023). How Can Computational Modeling Help Students Shift Their Ideas Towards Scientifically Accurate Explanations?. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 441-448. International Society of the Learning Sciences.
  • Gamalaldin, Y. (2023). The role of procedural utility in land market dynamics in Greater Cairo: an agent based model application. Environment and Planning B: Urban Analytics and City Science.
  • Gao, D., & Yang, Y. (2023). Identifying the impact of artifacts-based exploration and exploitation on routines’ formation dynamics: An agent-based model. Journal of Artificial Societies and Social Simulation, 26(3), 5.
  • Garcia Davalos, A. (2023). Mobile advertising spreading through personal social networks using a viral approach and branded apps (Doctoral dissertation, Enxeñaría telemática).
  • Garcia Davalos, A. (2023). Mobile advertising spreading through personal social networks using a viral approach and branded apps (Doctoral dissertation, Enxeñaría telemática).
  • Garcia-Davalos, A., & Garcia-Duque, J. (2023). A Simplified Mobile Advertising Model to Study Advertising Spreading through Personal Social Networks and Branded Apps. Journal of Promotion Management, 1-34.
  • Garcia-Diaz, J. G. (2023). WNT LIGAND-SPECIFIC SIGNALING IN BONE (Doctoral dissertation, Johns Hopkins University).
  • Garcia, J. M., & Bittencourt, R. A. (2023, April). Um Mapeamento Sistemático da Literatura sobre Pensamento Computacional na Perspectiva dos Fundamentos Teóricos de Aprendizagem. In Anais do III Simpósio Brasileiro de Educação em Computação (pp. 01-12). SBC.
  • Garnelo, I., & Islas, C. (2023). ¿ Hasta dónde alcanza realmente la potencia explicativa de los modelos basados en la autoorganización en el ámbito del aprendizaje? How far does the explanatory power of models based on self-organization really reach in the field of learning?. LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades, 4(2), 3307-3320.
  • Garzón, M., Álvarez-Pomar, L., & Rojas-Galeano, S. (2023, February). An Agent-Based Model of Follow-the-leader Search Using Multiple Leaders. In Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings (pp. 499-505). Cham: Springer International Publishing.
  • Gaur, S., & Singh, R. (2023). A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability, 15(2), 903.
  • Geldart, E. A., Love, O. P., Barnas, A. F., Harris, C. M., Gilchrist, H. G., & Semeniuk, C. A. (2023). A colonial-nesting seabird shows limited heart rate responses to natural variation in threats of polar bears. Royal Society Open Science, 10(10), 221108.
  • Genin, A., Dupont, G., Valencia, D., Zucconi, M., Avila-Thieme, M. I., Navarrete, S., & Wieters, E. (2023). Easy, fast and reproducible Stochastic Cellular Automata with'chouca'. bioRxiv, 2023-11.
  • Gerdes, L., Aigner, E., Meretz, S., Pahl, H., Schlemm, A., Scholz-Wäckerle, M., ... & Sutterlütti, S. (2023). COMMONSIM: Simulating the utopia of COMMONISM. Review of Evolutionary Political Economy, 1-37.
  • Ghaitaranpour, A., Koocheki, A., & Mohebbi, M. (2023). Simulation of bread baking with a conceptual agent-based model: An approach to study the effect of proofing time on baking behavior. Journal of Food Engineering, 111920.
  • Ghaleb, M., & Azzedin, F. (2023). Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach. Applied Sciences, 13(6), 3665.
  • Ghazimirsaeid, S. S., Jonban, M. S., Mudiyanselage, M. W., Marzband, M., Martinez, J. L. R., & Abusorrah, A. (2023). Multi-agent-based Energy Management of multiple Grid-connected green buildings. Journal of Building Engineering, 106866.
  • Giltri, M. (2023). From Real Affective States towards Affective Agents Modeling (Doctoral dissertation, University of Milano-Bicocca).
  • Gómez Herrera, J. S. Aplicación de un modelo de simulación para evaluar la difusión de taxis eléctricos en Colombia (Doctoral dissertation, Universidad Nacional de Colombia).
  • Gong, R., Hase, K., Ishikawa, N., Koshiba, R., & Minagawa, T. (2023). Pandemic Offline Informatics Education Using NetLogo-based Simulation for Course Scheduling in Japan. Bulletin of the Technical Committee on Learning Technology (ISSN: 2306-0212), 23(1), 13-19.
  • Gotschalk, P. A. (2023). Affluenza. In Dictionary of Ecological Economics (pp. 7-8). Edward Elgar Publishing.
  • Grajdura, S., Espeland, S., LanzDuret-Hernandez, J., & Rowangould, D. SARAH GRAJDURA, PH. D. Transportation Research Part D: Transport and Environment, 104, 103190.
  • Granger, J. N. (2023). Behavioral and Geophysical Factors Influencing Success in Long Distance Navigation (Doctoral dissertation, Duke University).
  • Grgurina, N., Tolboom, J., & de Vries, B. P. (2023, October). Evaluating the New Secondary Informatics Curriculum in The Netherlands: The Teachers’ Perspective. In International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 155-166). Cham: Springer Nature Switzerland.
  • Grønbakken, J. A. (2023). Mathematical modeling of multi-agent search & task allocation (Master's thesis).
  • Grotzer, T. A., & Solis, S. L. (2023). Thinking Like an Earthling: Children's Reasoning About Individual and Collective Action Related to Environmental Sustainability. Topics in Cognitive Science.
  • Gruzauskas, V., Burinskiene, A., & Krisciunas, A. (2023). Application of Information-Sharing for Resilient and Sustainable Food Delivery in Last-Mile Logistics. Mathematics, 11(2), 303.
  • Gu, H., Feng, L., & Zhen, X. (2022). Study on the stability of anaerobic digestion of food waste and the waste mushroom substrate based on SBR reactor and Netlogo simulation. Journal of Material Cycles and Waste Management, 1-17.
  • GÜLMEZ, B. (2023). Market zinciri ürün dağıtımı probleminin farklı genetik algoritma versiyonları ile çözümü ve karşılaştırması. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 180-196.
  • Gumahad, B., & Collins, A. (2023). Visualizing and Characterizing Emergent Behavior of Drone Swarm Systems with Agent-Based Modeling. In IIE Annual Conference. Proceedings (pp. 1-6). Institute of Industrial and Systems Engineers (IISE).
  • Gunaratne, C., Hatna, E., Epstein, J. M., & Garibay, I. (2023). Generating mixed patterns of residential segregation: An evolutionary approach. Journal of Artificial Societies and Social Simulation, 26(2).
  • Gürcan, Ö., Szczepanska, T., & Antosz, P. (2023, September). A Guide to Re-implementing Agent-Based Models: Experiences from the HUMAT Model. In Conference of the European Social Simulation Association (pp. 519-531). Cham: Springer Nature Switzerland.
  • Haase, K., Reinhardt, O., Lewin, W. C., Weltersbach, M. S., Strehlow, H. V., & Uhrmacher, A. M. (2023). Agent-Based Simulation Models in Fisheries Science. Reviews in Fisheries Science & Aquaculture, 1-24.
  • Haberle, I., Bavčević, L., & Klanjscek, T. Fish condition as an indicator of stock status: Insights from condition index in a food‐limiting environment. Fish and Fisheries.
  • Haddad, B. M., & Solomon, B. D. (Eds.). (2023). Dictionary of Ecological Economics: Terms for the New Millennium. Edward Elgar Publishing.
  • Haensel, M., Schmitt, T. M., & Bogenreuther, J. (2023). Teaching the Modeling of Human–Environment Systems: Acknowledging Complexity with an Agent-Based Model. Journal of Science Education and Technology, 1-11.
  • Hägglund, M. (2023). Agent Based Modelling for Simulating the Interregional Patient Mobility in Italy. CARING IS SHARING–EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION, 297.
  • Hamdi, M., & Goïta, K. (2023). Analysis of Groundwater Depletion in the Saskatchewan River Basin in Canada from Coupled SWAT-MODFLOW and Satellite Gravimetry. Hydrology, 10(9), 188.
  • Hamdi, M., & Goïta, K. (2023). Estimation of Aquifer Storativity Using 3D Geological Modeling and the Spatial Random Bagging Simulation Method: The Saskatchewan River Basin Case Study (Central Canada). Water, 15(6), 1156.
  • Hamed, N. A., & Hasson, S. T. (2023, April). A Developed Centralized Stable Clustering Approach for Vehicular Networks. In 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 867-872). IEEE.
  • Han, F., Zeng, J., Lin, J., Zhao, Y., & Gao, C. (2023). A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games. Applied Energy, 350, 121701.
  • Han, J., Tan, Q., Ji, Q., Li, Y., Liu, Y., & Wang, Y. (2023). Simulating the CCUS technology diffusion in thermal power plants: An agent-based evolutionary game model in complex networks. Journal of Cleaner Production, 421, 138515.
  • Han, N., & Liu, Z. (2023). Targeting alternative splicing in cancer immunotherapy. Frontiers in Cell and Developmental Biology, 11.
  • Han, Y. (2023). The Integration of New Media Communication and Social Network Based on Computer Technology. Media and Communication Research, 4(7), 13-21.
  • Han, Z., Mitani, Y., Kawano, K., Taniguchi, H., Honda, H., Meng, L., & Li, Z. (2023). Quantitative assessment of flooding risk based on predicted evacuation time: A case study in Joso city, Japan. International Journal of Disaster Risk Reduction, 104113.
  • Hanisch, S., & Eirdosh, D. (2023). Behavioral Science and Education for Sustainable Development: Towards Metacognitive Competency. Sustainability, 15(9), 7413.
  • Hanisch, S., Eirdosh, D., & Morgan, T. (2023). Evolving cooperation and sustainability for common pool resources. In Learning evolution through socioscientific issues (pp. 127-147). UA Editora.
  • Hartman, C. R. A. (2023). Hierarchically Embedded Social Dynamics in Vampire Bats (Doctoral dissertation, The Ohio State University).
  • Hassanpour, S., Gonzalez, V. A., Zou, Y., Liu, J., Wang, F., del Rey Castillo, E., & Cabrera-Guerrero, G. (2023). Incorporation of BIM-based probabilistic non-structural damage assessment into agent-based post-earthquake evacuation simulation. Advanced Engineering Informatics, 56, 101958.
  • Hatzis, J. J., Kim, J., & Klockow-McClain, K. E. (2024). An Agent-Based Modeling Approach to Protective Action Decision-Related Travel during Tornado Warnings. Natural Hazards Review, 25(1), 04023057.
  • Hayes, C. G. (2023). Expanding the Fisheries Management Tackle Box: A Multiple-Model Approach to Support Better Decisions (Doctoral dissertation, University of Maryland, College Park).
  • Hayes, R. (2023). Epistemic Agency in Lab: When, Why, and How Introductory College Biology Students Direct Their Own Science Investigations (Doctoral dissertation, Tufts University).
  • Hayes, R. J. (2023). Towards a Formal Theory of Humor in Organizations (Doctoral dissertation, Old Dominion University).
  • Hazlerigg, C. R., Mintram, K. S., Tyler, C. R., Weltje, L., & Thorbek, P. (2023). HARNESSING MODELLING FOR ASSESSING THE POPULATION RELEVANCE OF EXPOSURE TO ENDOCRINE ACTIVE CHEMICALS. Environmental Toxicology and Chemistry.
  • Hedger, R. D., Sundt‐Hansen, L. E., Juárez‐Gómez, A., Alfredsen, K., & Foldvik, A. (2023). Exploring sensitivities to hydropeaking in Atlantic salmon parr using individual‐based modelling. Ecohydrology, e2553.
  • Hénard, A., Rivière, J., Peillard, E., Kubicki, S., & Coppin, G. (2023). A unifying method-based classification of robot swarm spatial self-organisation behaviours. Adaptive Behavior, 10597123231163948.
  • Heppenstall, A., Polhill, J. G., Batty, M., Hare, M., Salt, D., & Milton, R. (2023). Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER)(Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
  • Heras, A., Sanchez-Enguix, V., Alberola, J. M., & Perez-Pascual, A. (2023). A BOARD GAME-BASED VIRTUAL ENVIRONMENT FOR INTELLIGENT BOTS PROGRAMMING. In INTED2023 Proceedings (pp. 3062-3068). IATED.
  • Herath, G. B., & Secchi, D. (2023). Organization-cognition fit: Supplementing or complementing team's capabilities?. In Organizational Cognition (pp. 120-144). Routledge.
  • Herberich, M. M., Gayler, S., & Tielbörger, K. (2023). Environmental heterogeneity promotes coexistence among plant life-history strategies through stabilizing mechanisms in space and time. Basic and Applied Ecology.
  • Hershkovitz, A., Bain, C., Kelter, J., Peel, A., Wu, S., Horn, M. S., & Wilensky, U. (2023). Contribution of Computational Thinking to STEM Education: High School Teachers' Perceptions after a Professional Development Program. Journal of Computers in Mathematics and Science Teaching, 42(1), 35-65.
  • Hicks, D. E. (2023). An Introduction to Complexity Pedagogy: Using Critical Theory, Critical Pedagogy and Complexity in Performance and Literature. Stylus Publishing, LLC.
  • Hipkiss, C. V. (2023). Stress in paradise: Reconstructing late holocene hydroclimate to investigate the role of drought in the timing of human migration and colonisation in the tropical South Pacific (Doctoral dissertation, University of Southampton).
  • Hofstede, G.J., Student, J., & Kramer, M.R. (2023). The status–power arena: a comprehensive agent-based model of social status dynamics and gender in groups of children. AI & Society, 38, 2511-2531. https://doi.org/10.1007/s00146-017-0793-5
  • Hu, H., & Wang, N. (2023). Dynamics of Business-IT Alignment: A Complex Adaptive System Model. PACIS 2023 Proceedings, 16.
  • Hu, W., Dong, Z., Huang, X., Gao, Y., Zhang, Z., & Hao, J. (2023, April). Photovoltaic inverter anomaly detection method based on LSTM serial depth autoencoder. In Journal of Physics: Conference Series (Vol. 2474, No. 1, p. 012026). IOP Publishing.
  • Hu, X., Yang, Z., Sun, J., & Zhang, Y. (2023). Optimal pricing strategy for electric vehicle battery swapping: Pay-per-swap or subscription?. Transportation Research Part E: Logistics and Transportation Review, 171, 103030.
  • Huang, R., Liu, G., Li, K., Liu, Z., Fu, X., & Wen, J. (2023). Evolution of residents' cooperative behavior in neighborhood renewal: An agent-based computational approach. Computers, Environment and Urban Systems, 105, 102022.
  • Huang, Y., Guo, Z., Chu, H., & Sengupta, R. (2023). Evacuation Simulation Implemented by ABM-BIM of Unity in Students’ Dormitory Based on Delay Time. ISPRS International Journal of Geo-Information, 12(4), 160.
  • Huber, M., & Karaali, G. (2023). Mathematics and Society. Journal of Humanistic Mathematics, 13(2), 1-3.
  • Huber, R., Späti, K., & Finger, R. (2023). A behavioural agent-based modelling approach for the ex-ante assessment of policies supporting precision agriculture. Ecological Economics, 212, 107936.
  • Huckins, E. (2023). Challenges and opportunities for consumers and producers in Central Iowa local food systems (Doctoral dissertation, Iowa State University).
  • Hughes, J. D., Langevin, C. D., Paulinski, S. R., Larsen, J. D., & Brakenhoff, D. (2023). FloPy Workflows for Creating and Constructing Structured and Unstructured MODFLOW 6 Models. Groundwater.
  • Hui, H., Gong, Z., An, J., & Qi, J. (2023). A dynamic Bayesian-based comprehensive trust evaluation model for dispersed computing environment. China Communications, 20(2), 278-288.
  • Hulkkonen, M., Kaaronen, R. O., Kokkola, H., Mielonen, T., Clusius, P., Xavier, C., ... & Malila, J. (2023). Modeling non-linear changes in an urban setting: From pro-environmental affordances to responses in behavior, emissions and air quality. Ambio, 52(5), 976-994.
  • Husarek, D. (2023). Analysis of sector-coupling effects between the mobility sector and the energy system under consideration of energy transport and charging infrastructure (Doctoral dissertation, Technische Universität Darmstadt).
  • Hussain, H., & de Vries, M. (2023). Extending the Meta Model for Enterprise Systems Dynamics from a Software Tooling Perspective. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) (Vol. 2, pp. 50-61). SCITEPRESS.
  • Iancu, L. D., Dragoi, P. A., & Delcea, C. (2023, June). Computer Modeling of Evacuation Patterns Comparison and Crowd Dynamics: A Use of NetLogo. In IBIMA Conference on Artificial intelligence and Machine Learning (pp. 356-367). Cham: Springer Nature Switzerland.
  • Ibrahim, S., & Maheshwari, P. (2023, March). Non-Pharmaceutical Intervention measures in the UAE–What Next?. In 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 226-231). IEEE.
  • Iii, B. Y. (2023). Combining Block-Based Programming With Robotics Kits to Support a Middle School Computing Curriculum (Doctoral dissertation, Vanderbilt University).
  • Ilagan, J. B., & Ilagan, J. R. (2023, July). Teaching Diffusion of Innovations Involving Technology Startups Using Agent-Based Simulation Modeling: Architecture and Design Considerations. In International Conference on Human-Computer Interaction (pp. 298-311). Cham: Springer Nature Switzerland.
  • Ilyas, N. A. J. I. (2023). Comparison of a posteriori error estimators. The 14th Edition of" Journées d'Analyse Numérique Optimisation, 75(256), 1659-1674.
  • Ionescu, Ș., Chiriță, N., Nica, I., & Delcea, C. (2023). An Analysis of Residual Financial Contagion in Romania’s Banking Market for Mortgage Loans. Sustainability, 15(15), 12037.
  • Isherwood, A., Koehler, M., & Slater, D. (2023, December). Using Evolutionary Model Discovery to Develop Robust Policies. In 2023 Winter Simulation Conference (WSC) (pp. 130-137). IEEE.
  • Ismael, W. K. F. A., & Ucan, O. (2023). Vanet performance evaluation in terms of nodes distribution, mobility models, and routing protocols. Technium: Romanian Journal of Applied Sciences and Technology, 8, 32-45.
  • Iswara, L., Gunawan, F. E., & Alamsjah, F. (2023, April). Assessing task difficulty, team-member ability, and motivation on the team productivity by an agent-based model. In AIP Conference Proceedings (Vol. 2594, No. 1). AIP Publishing.
  • Itahashi, K. (2023). Effeteness of Programing Education in Science Classroom—Through a Review of Studies on Programming Education. JSSE Research Report, 37(5), 29-34.
  • Ivanova, Y. (2023). APPLICATIONS OF DIGITAL TRANSFORMATIONS AND SIMULATION MODELING IN AEROSPACE ENGINEERING AND SECURITY. International Journal on Information Technologies & Security, 15(3).
  • Ivanova, Y. A. (2023, September). Building Drawing Simulation Models for the Purposes of Industrial Design. In 2023 International Conference on Information Technologies (InfoTech) (pp. 1-4). IEEE.
  • Jagutis, M., Russell, S., & Collier, R. (2023). Flexible simulation of traffic with microservices, agents & REST. International Journal of Parallel, Emergent and Distributed Systems, 1-17.
  • Jahn, L., Rendsvig, R. K., & Stærk-Østergaard, J. (2023). Detecting Coordinated Inauthentic Behavior in Likes on Social Media: Proof of Concept. arXiv preprint arXiv:2305.07350.
  • Jaimez-González, C. R., Erazo-Palacios, J., & García-Mendoza, B. (2023). BlockCode: A Web Application to Create Games that Support the Learning of Computer Programming Logic. International Journal of Emerging Technologies in Learning, 18(15).
  • Jayathilake, P., Victori, P., Pavillet, C., Voukantsis, D., Miar Cuervo, A., Arora, A., ... & Buffa, F. M. (2023). Metabolic Symbiosis between Oxygenated and Hypoxic Tumour Cells: An Agent-based Modelling Study. bioRxiv, 2023-07.
  • JE, O. (2023). SIX TIPS FOR BETTER CODING WITH CHATGPT. Nature, 618.
  • Jen, T., Brady, C. E., Vogelstein, L., & Ayalon, E. (2023). Designing for Feelings: Disruptive Beginnings in Youths’ Designs of Mixed Reality Activities for Sustainability. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 950-953. International Society of the Learning Sciences.
  • Jevtić, M., Mladenović, S., & Granić, A. (2023). Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps. Applied Sciences, 13(9), 5719.
  • Ji, J. (2023, October). Construction of public policy decision-making model based on big data analysis from the perspective of sustainable development. In Second International Conference on Sustainable Technology and Management (ICSTM 2023) (Vol. 12804, pp. 434-441). SPIE.
  • Jialin, L. I. U., Xianyu, Z. H. A. N. G., & Da, P. A. N. G. (2023). Research progress of adaptive therapy strategy based on Darwinian dynamics in tumor therapy. China Oncology, 33(4), 397-402.
  • Jiang, F., & Chen, Z. (2023, May). Behavior Simulation of Disturbed UAV Based on NetLogo. In 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP) (pp. 1143-1147). IEEE.
  • Jiang, J., & Sun, R. (2023, April). Research on the opinion evolution process under the effect of time delay. In Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) (Vol. 12610, pp. 1499-1506). SPIE.
  • Jiang, J., & Sun, R. (2023, June). Model of the evolution of opinion propagation under the effect of group pressure. In International Conference on Pure, Applied, and Computational Mathematics (PACM 2023) (Vol. 12725, pp. 8-16). SPIE.
  • Jiang, X., Jia, R., & Yang, L. (2023). Assessing the economic ripple effect of flood disasters in light of the recovery process: Insights from an agent‐based model. Risk analysis.
  • Jie, Y. A. N. G. Assessment of train passengers’ pre-and post-boarding behaviors and insights into platform and carriage design, preference, and satisfaction (Doctoral dissertation, RMIT University).
  • Jin, C. (2023). A dynamic management mechanism for object cooperation in the social Internet of Things (Doctoral dissertation, University of Manchester).
  • Jin, H., Zhang, S., Zhang, B., Dong, S., Liu, X., Zhang, H., & Tan, J. (2023). Evolutionary game decision-making method for network attack and defense based on regret minimization algorithm. Journal of King Saud University-Computer and Information Sciences, 35(3), 292-302.
  • Jing, K. (2023). Design of control algorithms for false information dissemination in smart city social media based on symmetric triangle interval. Soft Computing, 1-15.
  • Johnson, M., Lewis, T., Martin, K., Cesare, A., Schmitt, A., Lengerich, E., ... & Divyak, C. (2023). The Science of COVID-19. The Science Teacher, 90(3).
  • Jones, J. M (2023). A processual theory of strategic consensus (Doctoral dissertation, University of Georgia).
  • Jørgensen, B. N., da Silva, L. C. P., & Ma, Z. (Eds.). (2023). Energy Informatics: Third Energy Informatics Academy Conference, EI. A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part II (Vol. 14468). Springer Nature.
  • Jünger, J., & Gärtner, C. (2023). Simulationsverfahren. In Computational Methods für die Sozial-und Geisteswissenschaften (pp. 423-434). Wiesbaden: Springer Fachmedien Wiesbaden.
  • Júnior, A. O., Calvo-Rolle, J. L., & Leitão, P. (2023, April). Artificial Intelligence Data-Driven Petri nets Approach for Virtualizing Digital Twins. In 2023 IEEE International Conference on Industrial Technology (ICIT) (pp. 1-6). IEEE.
  • Käfer, T., Charpenay, V., & Harth, A. (2023). BOLD: A Benchmark for Linked Data User Agents and a Simulation Framework for Dynamic Linked Data Environments. arXiv preprint arXiv:2307.09114.
  • Kaimal, A. M., & Singhal, R. S. (2023). Bigels for controlled gastric release of ascorbic acid: Impact on rheology, texture, thermal stability and antioxidant activity. Food Hydrocolloids for Health, 100171.
  • Karalidis, K., Roumpos, C., Servou, A., Paraskevis, N., & Pavloudakis, F. (2023). A Scenario-Based Analysis for the Selection of Post-Mining Land Uses Applying a Cellular Automata Model. Materials Proceedings, 15(1), 4.
  • Karimian, H., Fan, Q., Li, Q., Chen, Y., & Shi, J. (2023). Spatiotemporal transmission of infectious nanochemical particles in water environment: A case study of Covid-19. Chemosphere, 139065.
  • Karutz, R., Klassert, C. J., & Kabisch, S. (2023). On Farmland and Floodplains—Modeling Urban Growth Impacts Based on Global Population Scenarios in Pune, India. Land, 12(5), 1051.
  • Katalevsky, D. (2023). New Governance Approaches to Prevent the Collapse of Complex Socioeconomic Systems. Foresight and STI Governance, 17(3), 56-67.
  • Kavitha, P., & Latha, S. D (2023). Genetic fuzzy logic algorithm as intelligent agents for swarm intelligence application. ICTACT Journal on Soft Computing, 14(3), 3243-48.
  • Ke, L., Kirk, E., Lesnefsky, R., & Sadler, T. D. (2023). Exploring system dynamics of complex societal issues through socio-scientific models. In Frontiers in Education (Vol. 8). Frontiers Media SA.
  • Kelter, J., Peel, A., Davey, B., Horn, M., & Wilensky, U. (2023). Quickstart Spaceship Programming for Developing Physical Intuition and Connecting it to Propositional Physics Knowledge. In proceedings of the International Society of the Learning Sciences (ISLS) 2023. Montreal, CA.
  • Kelter, J., Wilensky, U., & Potvin, J. (2023). Introducing Land Constraints to Macroeconomic Agent-Based Models. In Z. Yang & S. Núñez-Corrales (Eds.), Proceedings of the 2022 Conference of The Computational Social Science Society of the Americas (pp. 35–48). Springer International Publishing. https://doi.org/10.1007/978-3-031-37553-8_3
  • Khaddage, F., Lattemann, C., & Gebbing, P. (2023, February). Towards User2Machine Model for Higher Education-Enforced by Covid19 Pandemic. In Learning in the Age of Digital and Green Transition: Proceedings of the 25th International Conference on Interactive Collaborative Learning (ICL2022), Volume 2 (pp. 163-171). Cham: Springer International Publishing.
  • Khair, F., Wijaya, D. I., & Yulianto, H. D. (2023, August). Basic model simulation for disaster evacuation routes evaluation using agent based modeling (ABM). In AIP Conference Proceedings (Vol. 2485, No. 1). AIP Publishing.
  • Khajouei, M. S. H., Pilevari, N., Radfar, R., & Mohtashami, A. (2023). Iran’s Steel Chain Interactive Network Mapping. International Journal of ISSI.
  • Khan, J. R., Siddiqui, F. A., Rizwan, W., & Hamid, I. (2023). State of the Art Systematic Literature Review on Selection of Top Ten WSN Simulators–Part A. Journal of Independent Studies and Research Computing, 21(2), 42-49.
  • Khan, Z., Koubaa, A., Benjdira, B., & Boulila, W. (2023). A game theory approach for smart traffic management. Computers and Electrical Engineering, 110, 108825.
  • Khound, P., Will, P., Tordeux, A., & Gronwald, F. (2023). The Over-Damped String Stability Condition for a Platooning System. System Theory, Control and Computing Journal, 3(1), 12-19.
  • Kiel, L. (2023). The Importance of Replication In Uncertain Epistemic Landscapes (Master's thesis).
  • Kim, G., & Heo, G. (2023). Agent-based radiological emergency evacuation simulation modeling considering mitigation infrastructures. Reliability Engineering & System Safety, 109098.
  • Kim, Y. J., & Wang, J. (2023). Nondestructive Testing of Bridge Decks: Case Study and Suggestions. ACI Structural Journal, 120(2).
  • Kleiner, G., Rybachuk, M., & Ushakov, D. (2023, July). Behavioral Model of Interaction Between Economic Agents and the Institutional Environment. In Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies: Second International Conference, MSBC 2022, Vilnius, Lithuania, September 21–23, 2022, Proceedings (p. 48). Springer Nature.
  • KLINEC, M. (2023). Agentno modeliranje dinamike tropov volkov v okolju NetLogo (Doctoral dissertation, Univerza v Ljubljani, Fakulteta za računalništvo in informatiko).
  • Knepper, H. J., Evans, M. D., & Henley, T. J. (Eds.). (2023). Intersectionality and Crisis Management: A Path to Social Equity. Taylor & Francis.
  • Ko, C., Cho, W., Hwang, B., Chang, B., Kang, W., & Ko, D. W. (2023). Simulating Hunting Effects on the Wild Boar Population and African Swine Fever Expansion Using Agent-Based Modeling. Animals, 13(2), 298.
  • Koch, J., & De Schamphelaere, K. A. (2023). Investigating population level toxicity of the antidepressant citalopram in harpacticoid copepods using in vivo methods and bioenergetics‐based population modeling. Environmental Toxicology and Chemistry.
  • Koenig, L. J., Khurana, N., Islam, M. H., Gopalappa, C., & Farnham, P. G. (2023). Closing the gaps in the continuum of depression care for persons with HIV: modeling the impact on viral suppression in the United States. AIDS, 37(7), 1147-1156.
  • Koide, R., Yamamoto, H., Nansai, K., & Murakami, S. (2023). Agent-based model for assessment of multiple circular economy strategies: Quantifying product-service diffusion, circularity, and sustainability. Resources, Conservation and Recycling, 199, 107216.
  • Kolligs, T. (2023). The Slow Spread of Environmentally Friendly Action: An agent-based model simulation of social networks (Master's thesis, Stockholm University).
  • Kolodner, J. L. (2023). Learning engineering: What it is, why I’m involved, and why I think more of you should be. Journal of the Learning Sciences, 32(2), 305-323.
  • Kong, L., Wang, L., Cao, Z., & Wang, X. (2023). Resilience evaluation of UAV swarm considering resource supplementation. Reliability Engineering & System Safety, 109673.
  • Kortsch, S., Saravia, L., Cirtwill, A. R., Timberlake, T., Memmott, J., Kendall, L., ... & Strona, G. (2023). Landscape composition and pollinator traits interact to influence pollination success in an individual‐based model. Functional Ecology.
  • Köstler, V. (2023). Zwischen Präzision und Sensitivität: Generierung eines Studienkorpus am Beispiel einer Fragestellung zu Künstlicher Intelligenz (KI) in Bildungsprozessen. MedienPädagogik: Zeitschrift für Theorie und Praxis der Medienbildung, 54, 1-27.
  • Kousar, H., Fatima, S., Ahmed, S. I., Sajithra, S., Kushwaha, S., & Balaji, N. A. (2023, September). AI Based Security for Internet of Transportation Systems. In 2023 4th International Conference on Smart Electronics and Communication (ICOSEC) (pp. 701-708). IEEE.
  • Kout, A., Bouaita, B., Beghriche, A., Labed, S., Chikhi, S., & Bourennane, E. B. (2023). A Hybrid Optimization Solution for UAV Network Routing. Engineering, Technology & Applied Science Research, 13(2), 10270-10278.
  • Kumar, P., Raglin, A., & Richardson, J. (2023, July). General Agent Theory of Mind: Preliminary Investigations and Vision. In International Conference on Human-Computer Interaction (pp. 504-515). Cham: Springer Nature Switzerland.
  • Kumar, R., & Agrawal, N. (2023). Analysis of multi-dimensional Industrial IoT (IIoT) data in Edge-Fog-Cloud based architectural frameworks: A survey on current state and research challenges. Journal of Industrial Information Integration, 100504.
  • Kumari, S. (2023). Trust Management in Social Internet of Things: Challenges and Future Directions. International Journal of Computing and Digital Systems, 14(1), 1-xx.
  • Kunz, N., Chesney, T., Trautrims, A., & Gold, S. (2023). Adoption and transferability of joint interventions to fight modern slavery in food supply chains. International Journal of Production Economics, 258, 108809.
  • Kuroswiski, A. R., Medeiros, F. L. L., De Marchi, M. M., & Passaro, A. (2023). Beyond visual range air combat simulations: validation methods and analysis using agent-based models. The Journal of Defense Modeling and Simulation, 15485129231211915.
  • Kusumah, H., & Wasesa, M. (2023). Unraveling the Most Influential Determinants of Residential Segregation in Jakarta: A Spatial Agent-Based Modeling and Simulation Approach. Systems, 11(1), 20.
  • Lang, D., & Ertsen, M. W. (2023). Modelling farmland dynamics in response to farmer decisions using an advanced irrigation-related agent-based model. Ecological Modelling, 486, 110535.
  • Langbeheim, E., Ben-Hamo, S., Weintraub, G., & Shapira, S. (2023, April). Reasoning about crowd evacuations as emergent phenomena when using participatory computational models. In Frontiers in Education (Vol. 8, p. 1137828). Frontiers.
  • Larsson, A., & Große, C. (2023). Data use and data needs in critical infrastructure risk analysis. Journal of Risk Research, 26(5), 524-546.
  • Le Pira, M., Marcucci, E., Gatta, V., Ignaccolo, M., & Inturri, G. (2023). 16. Participatory decision-support tools for stakeholder engagement in urban freight transport policy making. Handbook on City Logistics and Urban Freight: 0, 327.
  • Le, H. (2023). Automated Discovery of Candidate Simulation Models for Steering Behavior Simulation (Doctoral dissertation, Georgia State University).
  • Le, N. T. T. (2023). Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways. Journal of Information and Telecommunication, 1-15.
  • Ledder, G. (2023). Modeling in Biology. In Mathematical Modeling for Epidemiology and Ecology (pp. 3-44). Cham: Springer International Publishing.
  • Lee, I. P. A., Eldakar, O. T., Gogarten, J. P., & Andam, C. P. (2023). Protocol for an agent-based model of recombination in bacteria playing a public goods game. STAR protocols, 4(4), 102733.
  • Lee, I. P. A., Eldakar, O. T., Gogarten, J. P., & Andam, C. P. (2023). Recombination as an enforcement mechanism of prosocial behavior in cooperating bacteria. iScience.
  • Lei, H., Wang, H., Wang, L., Dong, Y., Cheng, J., & Cai, K. (2023). An Analysis of the Evolution of Online Public Opinion on Public Health Emergencies by Combining CNN-BiLSTM+ Attention and LDA. Journal of Computer and Communications, 11(4), 190-199.
  • León, J., Ogueda, A., Gubler, A., Catalán, P., Correa, M., Castañeda, J., & Beninati, G. (2023). Increasing resilience to catastrophic near-field tsunamis: systems for capturing, modelling, and assessing vertical evacuation practices. Natural Hazards, 1-27.
  • Leonard-Duke, J., Bonham, C., & Peirce, S. M. (2023). Low Vascular Endothelial Growth Factor Levels and Increasing Environmental Stiffness Potentiate Vascular Hyper-remodeling in Computational Model of Idiopathic Pulmonary Fibrosis. In A65. THE ALVEOLAR NICHE IN THE SPOTLIGHT OF LUNG FIBROSIS (pp. A2190-A2190). American Thoracic Society.
  • Lessnau, T. A. (2023). An agent-based model of the canadian housing market (Doctoral dissertation, Université du Québec en Outaouais).
  • Lestari, D. P., Kosasih, R., Sari, I., & Amalia, A. (2023, August). Fire detection system on surveillance videos using faster region-based convolutional neural network for high buildings evacuation. In AIP Conference Proceedings (Vol. 2431, No. 1). AIP Publishing.
  • Lestari, D. P., Putra, H. D., & Sari, I. (2023, July). Prototype of evacuation management system on high rise building using stochastic dynamical optimization system. In AIP Conference Proceedings (Vol. 2689, No. 1). AIP Publishing.
  • Li, D., Yang, J., Li, J., Zhao, N., Ju, W., & Guo, M. (2023). Agent-Based Modeling and Simulation (ABMS) on the influence of adjusting medical service fees on patients' choice of medical treatment. BMC Health Services Research, 23(1), 928.
  • Li, H., & Zhang, H. (2023). Cost-effectiveness analysis of COVID-19 screening strategy under China's dynamic zero-case policy. Frontiers in Public Health, 11, 1099116.
  • Li, J., Wan, Q., & Yu, Z. (2023, November). Research on network security algorithms in distributed computing environment. In International Conference on Internet of Things and Machine Learning (IoTML 2023) (Vol. 12937, pp. 82-87). SPIE.
  • Li, L., Yuwen, Z., Zhu, J., Li, P., Duan, M., & Guo, X. Decision Support for Regeneration Mode of Old Community Under Multi-Agent Interaction: Evidence from China. Available at SSRN 4570595.
  • Li, P. (2023). 基于统计分析方法的网红直播带货效果影响因素研究 (Doctoral dissertation, Arizona State University).
  • Li, W., Lv, M., Hao, J., Chen, J., & Yu, K. Dynamic Simulation and Control Strategy Exploration of Unsafe Behavior of Coal Mine Staff. Maoyun and Hao, Jian and Chen, Jing and Yu, Kai, Dynamic Simulation and Control Strategy Exploration of Unsafe Behavior of Coal Mine Staff.
  • Li, W., Zhang, Q., Deng, S., Zhou, B., Wang, B., & Cao, J. (2023). Q-Learning Improved Lightweight Consensus Algorithm for Blockchain-structured Internet of Things. IEEE Internet of Things Journal.
  • Li, W., Zhou, L., Hao, J., Yu, K., Chen, J., Liu, P., & Feng, R. (2023). Dynamic simulation and control strategy exploration of the unsafe behavior of coal mine employees. Resources Policy, 86, 104067.
  • Li, X., Xu, D., Ding, C., Lu, W., Wang, M., Yan, W., ... & Li, Y. (2023). Comparative analysis of domestic and foreign coal mine safety supervision modes based on knowledge map. Environmental Science and Pollution Research, 1-13.
  • Li, Y., Yang, M., & Zhang, S. (2023). Study on the influence diffusion of SMEs in open-source communities from the perspective of complex networks. Mathematical Biosciences and Engineering, 20(7), 12731-12749.
  • Lian, C., & Wang, J. (2023). Multi-actor cooperation for emergency supply support: a simulation of behavior diffusion based on social networks. Natural Hazards, 1-22.
  • Lian, C., Liu, J., & Wang, J. (2023, May). Resource Support for “Mobilization–Participation” in Public Health Emergencies Based on a Complex Network Evolutionary Game. In Healthcare (Vol. 11, No. 10, p. 1506). MDPI.
  • Liang, Z., Várady, G., & Zagorácz, M. B. (2023). Sustainable Application of Automatically Generated Multi-Agent System Model in Urban Renewal. Sustainability, 15(9), 7308.
  • Liao, H., Holguín-Veras, J., & Calderón, O. (2023). Comparative analysis of the performance of humanitarian logistic structures using agent-based simulation. Socio-Economic Planning Sciences, 101751.
  • Liao, R., Liu, W., & Yuan, Y. (2023). Resilience Improvement and Risk Management of Multimodal Transport Logistics in the Post–COVID-19 Era: The Case of TIR-Based Sea–Road Multimodal Transport Logistics. Sustainability, 15(7), 6041.
  • Lienhard, D. Z. (2023). Reducing Abortion Rates Without Restricting Legal Access to Abortion: Evidence From Comparative Analysis of Relevant Policies and Demographic Indicators in 15 Post-Soviet Countries and Adaptive Agent-Based Modeling of Unintended Pregnancies (Doctoral dissertation, Arizona State University).
  • Lili, A., Tian, X., Wenbin, Y., & Xinbo, W. (2023). Modeling and Simulation of Spaceborne, Near-Spaceborne, and Airborne Integrated Collaborative Remote Sensing System Based on DoDAF. Journal of System Simulation, 35(5), 936.
  • Lima, H. P. D. Formigas de correição do gênero Eciton (Latreille, 1804): interação predador presa, forrageio e nidificação (Doctoral dissertation, Universidade de São Paulo).
  • Lin, C. Y. (2023). Co-evolution in Complex Adaptive Water Systems from Long-Term Planning to Short-Term Responses (Doctoral dissertation, Lehigh University).
  • Ling, B., Raynor, E. J., Joern, A., & Goodin, D. G. (2023). Dynamic Plant–Herbivore Interactions between Bison Space Use and Vegetation Heterogeneity in a Tallgrass Prairie. Remote Sensing, 15(22), 5269.
  • Liu, C., Liu, S., Zhang, J., Wang, L., Guo, X., Li, G., & Wang, W. (2023). An optimal design method of emergency evacuation space in the high-density community after earthquake based on evacuation simulation. Natural Hazards, 1-27.
  • Liu, H., & Gu, X. (2023). Leveraging AI, big data and educational technology to promote collaborative learning and improve cyberlearning courses: synopsis and linked presentations of the workshop at Orlando, Florida, 4-6 June 2019, and the online workshop, 13-14 August 2020. International Journal of Smart Technology and Learning, 3(2), 118-137.
  • Liu, M., & Liu, T. (2023). An Agent-Based Approach to Adaptive Design Based on Influences Mediated by Artifacts. In International Conference on-Design Computing and Cognition (pp. 605-625). Springer, Cham.
  • Liu, S., & Li, Y. (2023). Dynamic Simulation Study on Evolution Law and Intervention Strategy of Public Opinion Information of Major Public Health Events. Advances in Applied Sociology, 13(5), 422-440.
  • Liu, Y. (2023). Application: Simulation Model of Pedestrian Flows in the Re-design of Built Environment Around Metro Stations. In Built Environment and Walking & Cycling Around Metro Stations (pp. 97-121). Singapore: Springer Nature Singapore.
  • Liu, Y., Song, D., Wang, Z., Yu, X., & Wang, R. (2023, July). Walkability Assessment Using Agent-Based Model: Why It Becomes An Advantageous Way. In World Congress of Architects (pp. 367-374). Cham: Springer International Publishing.
  • Locatelli, M., Pellegrini, L., Accardo, D., Sulis, E., Tagliabue, L. C., & DI GIUDA, G. M. (2023, September). People flow management in a healthcare facility through crowd simulation and agent-based modeling methods. In JOURNAL OF PHYSICS. CONFERENCE SERIES (pp. 1-6).
  • Lomos, C., Luyten, J. W., & Tieck, S. (2023). Implementing ICT in classroom practice: what else matters besides the ICT infrastructure?. Large-scale Assessments in Education, 11(1), 1-28.
  • Long, Y., Yang, C., Li, X., Lu, W., Zhang, Q., & Gao, J. (2023). Forecasting law enforcement frequency of internet+ coal mine safety supervision. International Journal of Energy Sector Management.
  • Lorente, P. J., & Pereda, M. (2023, March). An Iterated Prisoner’s Dilemma Tool to Play and Learn Inside and Outside the Class. In IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización (pp. 59-63). Cham: Springer International Publishing.
  • Lorig, F., Vanhée, L., & Dignum, F. (2023). Agent-Based Social Simulation for Policy Making. In Human-Centered Artificial Intelligence: Advanced Lectures (pp. 391-414). Cham: Springer International Publishing.
  • Lou, J., Borjigin, S., Tang, C., Saadat, Y., Hu, M., & Niemeier, D. A. (2023). Facility design and worker justice: COVID‐19 transmission in meatpacking plants. American Journal of Industrial Medicine.
  • Lu, J., Wang, C., Li, J., Li, X., Zhao, J., & Wang, X. (2023, August). Trust evaluation model in new power system edge computing. In International Conference on Optoelectronic Information and Functional Materials (OIFM 2023) (Vol. 12781, pp. 8-16). SPIE.
  • Lu, P., Li, Y., Wen, F., & Chen, D. (2023). Agent-based modeling of mass shooting case with the counterforce of policemen. Complex & Intelligent Systems, 1-21.
  • Lu, P., Zhang, Z., & Li, M. (2023). Individual heights and phase transition under crowd emergencies: Agent-based modeling from 2 to 3D. Artificial Intelligence Review, 1-23.
  • Lu, P., Zhang, Z., Onyebuchi, C. H., & Zheng, L. (2024). Agent-based modeling of high-rise building fires reveals self-rescue behaviors and better fire protection designs. Engineering Applications of Artificial Intelligence, 127, 107401.
  • Lu, Q., & Hua, J. (2023). Micro-Household Human Capital Investment Decisions and a Simulation Study from the Intergenerational Conflict Perspective. International Journal of Environmental Research and Public Health, 20(3), 1696.
  • Lu, Y., Ou, D., Zhou, Z., Li, H., Deng, Y., Deng, Y., & Zhang, Z. (2023). Simulation analysis of passengers’ rescheduling strategies in metro station under COVID-19. Tunnelling and Underground Space Technology, 134, 105023.
  • Lucas, P., & Feliciani, T. (2023). Investigating social phenomena with agent-based models. In Research Handbook on Digital Sociology (pp. 146-160). Edward Elgar Publishing.
  • Luckner, K., & Fikfak, V. (2023). Not all nations at all times: How States Imitate Each Other’s Behavior Towards Non-Compliance with International Law Norms: an ABM proposal. In iCourts Working Paper 318; Proceedings of the AMPM-Workshop@JURIX2022.
  • Luo, F., Ijeluola, S. A., Westerlund, J., Walker, A., Denham, A., Walker, J., & Young, C. (2023). Supporting Elementary Teachers’ Technological, Pedagogical, and Content Knowledge in Computational Thinking Integration. Journal of Science Education and Technology, 1-14.
  • Lynch, A. R., Bradford, S., Zhou, A. S., Oxendine, K., Henderson, L., Horner, V. L., ... & Burkard, M. E. (2023). A survey of CIN measures across mechanistic models. bioRxiv, 2023-06.
  • Lyu, G., & Brennan, R. W. (2024). Evaluating a self-manageable architecture for industrial automation systems. Robotics and Computer-Integrated Manufacturing, 85, 102627.
  • Ma, J., & Xiao, C. (2023). Large-scale fire spread model for traditional Chinese building communities. Journal of Building Engineering, 105899.
  • Ma, J., Xu, S., Zhang, L., Li, Z., Qian, Z., & Cao, K. Y. (2023). Study on the Evolution Mechanism of Lane Change Decision in Urban Expressway Diversion Area. Tehnički vjesnik, 30(5), 1503-1516.
  • Ma, J., Zhao, J., Sun, G., & Peng, S. (2023, December). ANYLOGIC-Based Dynamics Model of the Bass New Product Diffusion Process. In Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023) (Vol. 19, p. 26). Springer Nature.
  • Ma, Y., Wei, X., Zhao, H., Zhao, D., Wang, S., Han, T., ... & Gao, K. (2023). UAV-based emergency treatment plan for flood disasters at the Hongyanhe nuclear power plant. Ecological Indicators, 154, 110676.
  • Mabey, C. S., Peiffer, E. E., MacCarty, N., & Mattson, C. A. (2023). Simulating the Adoption and Social Impact of Improved Cookstoves in Uganda Using Agent-Based Modeling and Neural Networks. Journal of Mechanical Design, 145(12).
  • Madhavi, M., Kusumaniswari, V., Srinivas, G. S. H., Nayak, T. P. K., & Shivaji, M. (2023, March). Land Development Interface. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 314-319). IEEE.
  • Maghaydah, S., Maheshwari, P., & Alomari, K. M. (2023, March). Agent-Based Modelling and Simulation of Crowd Evacuation: Case Study for Electric Train Cabin. In 2023 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1-7). IEEE.
  • Maheshwari, P. (2023). Agent-Based Simulation for University Students Admission: Medical Colleges in Jordan Universities. Computer Science & Engineering: An International Journal (CSEIJ), 13(1).
  • Maheshwari, P., & Devi, Y. (2023). Investigating the relationship between Lean Six Sigma performance strategy with digital twin modeling: Practices and factors. Journal of Cleaner Production, 140449.
  • MAKSIMYCHEV, O., MEZENTSEV, K., & VOLOSOVA, A. (2023). INFORMATION AND COMMUNICATION TECHNOLOGIES AND ELEMENTS OF ARTIFICIAL INTELLIGENCE IN INTELLIGENT TRANSPORT SYSTEMS. Мир транспорта и технологических машин, 3390, 62.
  • Malanson, G. P., Testolin, R., Pansing, E. R., & Jiménez‐Alfaro, B. (2023). Area, environmental heterogeneity, scale and the conservation of alpine diversity. Journal of biogeography, 50(4), 743-754.
  • Malik, J., Putra, H. C., Sun, K., & Hong, T. (2023, May). On the applicability of various levels of detail for occupant behavior representation and modeling in building performance simulation. In Building Simulation (pp. 1-18). Beijing: Tsinghua University Press.
  • Malik, S., Khan, M. A., El-Sayed, H., & Khan, M. J. (2023). Should Autonomous Vehicles Collaborate in a Complex Urban Environment or Not?. Smart Cities, 6(5), 2447-2483.
  • Manju, T., Rehman, S., & Sankar, M. G. A. (2023). Multi Domain Optical Network Routing Using OBGP and OSPF. International Journal for Recent Developments in Science & Technology, 7(2), 146-152.
  • Mannone, M., Seidita, V., & Chella, A. (2023). Modeling and designing a robotic swarm: A quantum computing approach. Swarm and Evolutionary Computation, 101297.
  • Manzi, D. (2023). THE RESILIENCE OF CRIMINAL NETWORKS: AN AGENT-BASED SIMULATION ASSESSING DRUG TRAFFICKING ORGANIZATIONS REACTIONS TO LAW ENFORCEMENT ATTEMPTS AT DISRUPTION (Doctoral dissertation, Università Cattolica del Sacro Cuore).
  • Marah, H., & Challenger, M. (2023). An Architecture for Intelligent Agent-Based Digital Twin for Cyber-Physical Systems. In Digital Twin Driven Intelligent Systems and Emerging Metaverse (pp. 65-99). Singapore: Springer Nature Singapore.
  • Marchand, G. C., & Hilpert, J. C. (2024). Contributions of complex systems approaches, perspectives, models, and methods in educational psychology. In Handbook of educational psychology (pp. 139-161). Routledge.
  • Marin Gutierrez, D., Vázquez Salceda, J., Álvarez Napagao, S., & Gnatyshak, D. (2023). Adding preferences and moral values in an agent-based simulation framework for high-performance computing. In International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS 2023: papers accepted for presentation.
  • Marron, A., Cohen, I. R., Frankel, G., Harel, D., & Szekely, S. (2023). Challenges in Modeling and Unmodeling Emergence, Rule Composition, and Networked Interactions in Complex Reactive Systems. In MODELSWARD (pp. 202-209).
  • Marsteller, R. B., & Bodzin, A. M. (2023). GIVING ONLINE LEARNING THE PERSONAL TOUCH. Teaching and Learning Online: Science for Secondary Grade Levels, 107.
  • Martens, Chris, Alexander Card, Henry Crain, and Asha Khatri. "Modeling Game Mechanics with Ceptre." IEEE Transactions on Games (2023).
  • Martí, P., Jordán, J., & Julian, V. (2023). A flexible approach for demand-responsive public transport in rural areas. Computer Science and Information Systems, (00), 74-74.
  • Marwal, A., & Silva, E. A. (2023). City affordability and residential location choice: A demonstration using agent based model. Habitat International, 136, 102816.
  • Masa, R. C., Barca, M. Á. M., & Baquedano, E. P. (2023). Desarrollo de un modelo híbrido para reproducir el crecimiento de la placa de ateroma. Jornada de Jóvenes Investigadores e Investigadoras del I3A, 11.
  • Mateos, A., Hölzchen, E., & Rodríguez, J. (2023). Sabretooths, giant hyenas, and hominins: Shifts in the niche of scavengers in Iberia at the Epivillafranchian-Galerian transition. Palaeogeography, Palaeoclimatology, Palaeoecology, 111926.
  • Mattia, S., Paolo, M., & Stefano, N. (2023). Virtual earth cloud: a multi-cloud framework for enabling geosciences digital ecosystems. International Journal of Digital Earth, 16(1), 43-65.
  • Maupin, C. K., Mohan, G., Choudhury, A., Deepak, P., & Jin, F. (2023). Network-based approaches to leadership: An organizing framework, review, and recommendations. The Leadership Quarterly, 101753.
  • Mayerhoffer, D. M. (2023). One Model, Multiple Stories? Using Agent-Based Models to Unveil Structural Similarities in a Complex World (Doctoral dissertation, Otto-Friedrich-Universität Bamberg, Fakultät Sozial-und Wirtschaftswissenschaften).
  • McClure, C. J., & Rolek, B. W. (2023). Pitfalls arising from site selection bias in population monitoring defy simple heuristics. Methods in Ecology and Evolution, 14(6), 1489-1499.
  • McCue-Weil, L., Knight, M., Driscoll, M., Jenkins, P., & Sorensen, J. (2023). A Case Study on the Practical Use of Low-Fidelity Modeling to Mitigate the Spread of COVID-19 Amongst the Underserved Farmworker Community. La Matematica, 1-19.
  • McCune, J. CBai, Y. (2023). Research on Civil Engineering Cost Prediction Based on Decision Tree Algorithm. Academic Journal of Architecture and Geotechnical Engineering, 5(1), 39-44.. (2023). Computational Concepts, Practices, and Perspectives in K-8 Computer Science Teaching Readiness (Doctoral dissertation, Grand Canyon University).
  • McElhaney, K. W., Basu, S., McBride, E., Hutchins, N., & Biswas, G. (2023). Design and Implementation of a Week-long, High School Curriculum Unit Integrating Physics and Computational Modeling. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 497-504. International Society of the Learning Sciences.
  • McEligot, K. (2023). Integrated Coastal Flood Mitigation Policies for Myrtle Beach, South Carolina Utilizing a Federated Hurricane Mitigation Modeling Framework (Doctoral dissertation, George Mason University).
  • McGough, A., Kavak, H., & Mahabir, R. (2023). Is more always better? Unveiling the impact of contributor dynamics on collaborative mapping. Computational and Mathematical Organization Theory, 1-14.
  • McNeill, G., Sondag, M., Powell, S., Asplin, P., Turkay, C., Moller, F., & Archambault, D. (2023, April). From Asymptomatics to Zombies: Visualization-Based Education of Disease Modeling for Children. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-17).
  • Mellmann, H., Taliaronak, V., & Hafner, V. V. (2023, May). Towards an Anticipatory Mechanism for Complex Decisions in a Bio-Hybrid Beehive. In Concurrency, Specification and Programming: Revised Selected Papers from the 29th International Workshop on Concurrency, Specification and Programming (CS&P'21), Berlin, Germany (pp. 145-173). Cham: Springer International Publishing.
  • Men, F., Wang, M., & Dong, F. (2023, August). Research on Simulation Technology of Intelligent Manufacturing Scene Operation Mechanism Based on Evolutionary Game Theory. In Journal of Physics: Conference Series (Vol. 2562, No. 1, p. 012064). IOP Publishing.
  • Menzemer, L. W., Ronchi, E., Karsten, M. M. V., Gwynne, S., & Frederiksen, J. (2023). A scoping review and bibliometric analysis of methods for fire evacuation training in buildings. Fire Safety Journal, 103742.
  • Meyer, D., & Yon, G. G. V. (2023). epiworldR: Fast Agent-Based Epi Models. Journal of Open Source Software, 8(90), 5781.
  • Meza, A., Ari, I., Al Sada, M., & Koç, M. (2023). Relevance and potential of the Arctic Sea Routes on the LNG trade. Energy Strategy Reviews, 50, 101174.
  • Mi, J., Yao, C., Zhao, X., & Li, F. (2023). Research on the Diffusion Mechanism of Green Technology Innovation Based on Enterprise Perception. Computational Economics, 1-30.
  • Mi, J., Yao, C., Zhao, X., & Li, F. (2023). The impact of innovation evolution and interaction control on interfirm network performance. Technology Analysis & Strategic Management, 1-17.
  • Miao, H., Zhang, G., Yu, P., Shi, C., & Zheng, J. (2023). Dynamic Dose-Based Emergency Evacuation Model for Enhancing Nuclear Power Plant Emergency Response Strategies. Energies, 16(17), 6338.
  • Minggang, Y., Yanjie, N., Xueda, L., Dongge, Z., Peng, Z., Ming, H., & Ling, L. (2023). Adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game. Journal of Systems Engineering and Electronics.
  • Miranda, L., Baggio, J., & Ozmen Garibay, O. (2023). Evolutionary model discovery of human behavioral factors driving decision-making in irrigation experiments. JASSS: Journal of Artificial Societies and Social Simulation, 26(2).
  • Miszczak, J.A., Rule switching mechanisms in the Game of Life with synchronous and asynchronous updating policy, Physica Scripta, 98,115210 (2023). arXiv:2310.05979 DOI:10.1088/1402-4896/acfc6c
  • Mitcham, J. (2023). Agent-Based Simulation of Police Funding Tradeoffs Through the Lens of Legitimacy and Hardship. Journal of Artificial Societies and Social Simulation, 26(3).
  • Mitcham, J. (2023). Simulation Modeling for Robust and Just Public Policy Decision-Making (Doctoral dissertation, University of Massachusetts Boston).
  • Mittal, S., Wittman, R. L., Gibson, J., Huffman, J., & Miller, H. (2023). Providing a User Extensible Service-Enabled Multi-Fidelity Hybrid Cloud-Deployable SoS Test and Evaluation (T&E) Infrastructure: Application of Modeling and Simulation (M&S) as a Service (MSaaS). Information, 14(10), 528.
  • Miyazaki, S. (2023). Heterodox modeling: practicing well-tuned provisioning or commoning with networked multi-agent environments. Review of Evolutionary Political Economy, 1-14.
  • Mobinizadeh, M., Mohammadshahi, M., Aboee, P., Fakoorfard, Z., Olyaeemanesh, A., & Mohamadi, E. (2023). The Application of System Simulation in the Health Sector: A Rapid Review. Decision Making in Healthcare Systems, 513, 11.
  • Modu, B., Polovina, N., & Konur, S. (2023). Agent-Based Modeling of Malaria Transmission. IEEE Access, 11, 19794-19808.
  • Moghaddam, R. M., & Aghazadeh, N. (2023). Lung Parenchyma Segmentation from CT Images with a Fully Automatic Method. Multimedia Tools and Applications, 1-23.
  • Mohammadi, N., Mesgari, M. S., & Klein-Paste, A. (2023). An Empirical Agent-Based Model for Residential Segregation, Case Study: Tehran. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 71-78.
  • Mohammadi, V., Rahmani, A. M., Darwesh, A., & Sahafi, A. (2023). Fault tolerance in fog-based Social Internet of Things. Knowledge-Based Systems, 265, 110376.
  • Mohammed, G. B., Shitharth, S., & Sucharitha, G. (2023). A Novel Trust Evaluation and Reputation Data Management Based Security System Model for Mobile Edge Computing Network. In Security and Risk Analysis for Intelligent Edge Computing (pp. 155-170). Cham: Springer International Publishing.
  • Mohammed, R., Kennedy-Clark, S., & Reimann, P. (2023). Using Immersive Technologies to build Primary Preservice Teacher Confidence in Science. Journal of Computers in Mathematics and Science Teaching, 42(3), 205-226.
  • Mohandes, N., Bayhan, S., Sanfilippo, A., & Rub, H. A. (2023). Peer-to-peer trade and the sharing economy at distribution level: A review of the literature. IEEE Access.
  • Mongkhonvanit, K., Hummer, T. M., & Chen, J. (2023, June). Velo: Exploring Animal Behavior Modeling through Hybrid Robotics-Simulation Learning Experience. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (pp. 701-704).
  • Monroe, J., Bolton, E. R., & Berglund, E. Z. (2023). Evaluating Peer-to-Peer Electricity Markets across the US Using an Agent-Based Modeling Approach. Advances in Environmental and Engineering Research, 4(1), 1-40.
  • Moosavi, S. F., Salehnia, N., Seifi, A., AsgharpourMasouleh, A., & Salehnia, N. Designing and Calibrating an Agent‐Based Platform to Evaluate the Effect of Climate Variables on Residential Water Demand. Water and Environment Journal.
  • Morell, J. (2023). A Complexity-Based Plan for Evaluating Transformation. Journal of MultiDisciplinary Evaluation, 19(45), 105-130.
  • Moreno, A., Jorba, J., Peralta, C., César, E., Sikora, A., & Hanzich, M. (2023). A methodology for selecting a performance-convenient ABMS development framework on HPC platforms. Simulation Modelling Practice and Theory, 102812.
  • Moritz, M., Cross, B., & Hunter, C. E. (2023). Artificial pastoral systems: a review of agent-based modelling studies of pastoral systems. Pastoralism, 13(1), 31.
  • Moritz, M., Hunter, C. E., Peart, D. C., & Hamilton, I. M. Agent-Based Modeling in Mixed Methods Research. In The Handbook of Teaching Qualitative and Mixed Research Methods (pp. 322-325). Routledge.
  • Moritz, M., Hunter, C. E., Peart, D. C., Buffington, A., Yoak, A. J., Thomas, J. R., ... & Hamilton, I. M. (2023). Coupled Demographic Dynamics of Herds and Households Constrain Livestock Population Growth in Pastoral Systems. Human Ecology, 1-13.
  • Morsi, N., Kamel, S., Sabry, H., & Assem, A. (2023). Computational design for architectural space planning of commercial exhibitions: A framework for visitors interaction using parametric design and agent-based modeling. Architecture and Planning Journal (APJ), 28(3), 11.
  • Mou, S., Zhong, K., & Ma, Y. (2023). Regulating the Big Data-Based Discriminatory Pricing in Platform Retailing: A Tripartite Evolutionary Game Theory Analysis. Mathematics, 11(11), 2579.
  • Mousavi Moghaddam, R., & Aghazadeh, N. (2023). Lung Parenchyma Segmentation from CT Images with a Fully Automatic Method. Multimedia Tools and Applications, 1-23.
  • Mponela, P., Le, Q. B., Snapp, S., Villamor, G. B., Tamene, L., & Borgemeister, C. (2023). MASSAI: Multi-agent system for simulating sustainable agricultural intensification of smallholder farms in Africa. MethodsX, 11, 102467.
  • Müller, B., Gosal, A., & Ziv, G. (2023, September). An Agent-Based Model of UK Farmers' Decision-Making on Adoption of Agri-environment Schemes Chunhui Li, Meike Will, Nastasija Grujić, Jiaqi Ge®. In Advances in Social Simulation: Proceedings of the 17th Social Simulation Conference, European Social Simulation Association (p. 463). Springer Nature.
  • Musaeus, L. H., Caspersen, M. E., & Musaeus, P. (2023, September). A Template for Teaching Computational Modelling in High School. In Proceedings of the 18th WiPSCE Conference on Primary and Secondary Computing Education Research (pp. 1-10).
  • Mussawar, O., Mayyas, A., & Azar, E. (2023). Built Form and Function as Determinants of Urban Energy Performance: An Integrated Agent-based Modeling Approach and Case Study. Sustainable Cities and Society, 104660.
  • Mussawar, O., Urs, R. R., Mayyas, A., & Azar, E. (2023). Performance and prospects of urban energy communities conditioned by the built form and function: A systematic investigation using agent-based modeling. Sustainable Cities and Society, 99, 104957.
  • Naredo, E., Sansores, C., Godinez, F., López, F., Urbano, P., Trujillo, L., & Ryan, C. (2023). Comprehensive Analysis of Learning Cases in an Autonomous Navigation Task for the Evolution of General Controllers. Mathematical and Computational Applications, 28(2), 35.
  • Navarrete López, C. F. (2023). Metodologías epidemiológicas de análisis de datos para la operación y gestión de redes de abastecimiento urbano de agua (Doctoral dissertation, Universitat Politècnica de València).
  • Navarro Osma, R. (2023). Implementación del juego jGomas en Spade 3 (Doctoral dissertation, Universitat Politècnica de València).
  • Neal, A., Ballard, T., & Palada, H. (2023). HOW TO PUBLISH AND REVIEW A COMPUTATIONAL MODEL. Computational Modeling for Industrial-Organizational Psychologists, 122.
  • Neilan, R.M., Reith, C., Anandan, I., Kraeuter, K., Allen, H.N., & Kolber, B.J. (2023). Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain. Front. Pain Res. 4. https://doi.org/10.3389/fpain.2023.1183553
  • Nespeca, V., Comes, T., & Brazier, F. (2023). A Methodology to Develop Agent-Based Models for Policy Support Via Qualitative Inquiry. Journal of Artificial Societies and Social Simulation, 26(1).
  • Neumann, M., Dirksen, V., & Dickel, S. On the construction of plausible futures in interpretive agent-based modelling. In An Interpretive Account to Agent-based Social Simulation (pp. 170-188). Routledge.
  • Newman, J. D. (2023). Determining the influence of organism level processes on the population dynamics of Bactrocera tryoni (Doctoral dissertation, Queensland University of Technology).
  • Ng, L. T. (2023). AGENT-BASED COMPUTATIONAL GEOMETRY (Doctoral dissertation, University of Washington).
  • Nguyen, D. T. (2023). Hybrid Simulation-based Lean Management Methodology to Improve the Sustainability of the Construction Phase (Doctoral dissertation, University of Kassel).
  • Nica, I., Georgescu, I., Delcea, C., & Chiriță, N. (2023). Toward Sustainable Development: Assessing the Effects of Financial Contagion on Human Well-Being in Romania. Risks, 11(11), 204.
  • Nichols, A. C. (2023). An Artificial Honeybee Colony Algorithm to Quantify Adaptability via Resilience for Space System Architectures (Doctoral dissertation, The University of Alabama).
  • Nitsch, F., Schimeczek, C., Frey, U., & Fuchs, B. (2023). FAME-Io: Configuration tools for complex agent-based simulations. Journal of Open Source Software, 8(84).
  • Noormohammadi, R., Khatami Firoozabadi, S. M., Alamtabriz, A., Ehtesham Rasi, R., & Daneshvar, A. (2023). Estimating the amount of fuel consumption and air pollution caused by the traffic of buses rapid transit using agent-based modeling. Journal of Environmental Science and Technology.
  • Noubar, H. B. K., Holagh, S. R., & Sadri, A. (2023). Identifying Factors Affecting Green Consumer Purchase Behavior on E-Commerce Websites. TalTech Journal of European Studies, 13(1), 40-62.
  • Nurdiansyah, H., Almubaroq, H. Z., Risdhianto, A., & Mualim, M. (2023). EVALUATION OF THE SPREAD OF RADICALISM, EXTREMISM, AND TERRORISM IN INDONESIA'S DEFENSE USING AGENT-BASED SIMULATIONS. International Journal of Humanity Studies (IJHS), 6(2), 228-239.
  • O’Brien, W., Calì, D., De Simone, M., Tabadkani, A., Azar, E., Rajus, V. S., ... & Rysanek, A. (2023). Introduction to Occupant Modeling. In W. O'Brien & F. Tahmasebi (Eds.), Occupant-Centric Simulation-Aided Building Design: Theory, Application, and Case Studies (pp. 104-144). Routledge.
  • O’Neill, M. (2023). Coordination games and regional economic transitions. Territory, Politics, Governance, 1-19.
  • O'Shea, T., Bates, P., & Neal, J. Testing the impact of direct the indirect flood warnations on population environment using an agent-based model. Natural Hazards and Earth System Sciences, 20, 2281-2305.
  • Obaid, A. N., & Namah, A. J. (2023). Effectiveness of Artificial Intelligence in Graphic Design. Remittances Review, 8(4).
  • Ocak, C., Yadav, A., Vogel, S., & Patel, A. (2023). Teacher Education Faculty’s Perceptions About Computational Thinking Integration for Pre-service Education. Journal of Technology and Teacher Education, 31(3), 299-349.
  • Ogami, T., & Nishinari, K. (2023). Features of ladders during evacuation from oil and LNG plants. Physica A: Statistical Mechanics and its Applications, 128745.
  • Ogunsakin, R., Mehandjiev, N., & Marin, C. A. (2023). Towards adaptive digital twins architecture. Computers in Industry, 149, 103920.
  • Oktavia Mulyono, Y., Sukhbaatar, U., & Cabrera, D. (2023). ‘Hard’and ‘Soft’Methods in Complex Adaptive Systems (CAS): Agent Based Modeling (ABM) and the Agent Based Approach (ABA). Journal of Systems Thinking, 1-33.
  • Olagoke, A., Jeltsch, F., Tietjen, B., Berger, U., Ritter, H., & Maaß, S. (2023). Small‐scale heterogeneity shapes grassland diversity in low‐to‐intermediate resource environments. Journal of Vegetation Science, 34(4), e13196.
  • Oliveira, H., Mendes, F., & Henriques, A. (2022). A investigação sobre o ensino e a aprendizagem de temas matemáticos publicada em 30 anos da revista Quadrante. Quadrante, 31(2), 32-62.
  • Osoianu, F. (2023). Utilizarea datelor etnografice în crearea de medii și agenți pentru simularea comportamentului criminal real (Doctoral dissertation, Universitatea Tehnică a Moldovei).
  • Ouda, E., Sleptchenko, A., & Simsekler, M. C. E. (2023). Comprehensive review and future research agenda on discrete-event simulation and agent-based simulation of emergency departments. Simulation Modelling Practice and Theory, 102823.
  • Ozulumba, T., Montalbine, A. N., Ortiz-Cárdenas, J. E., & Pompano, R. R. (2023). New tools for immunologists: models of lymph node function from cells to tissues. Frontiers in Immunology, 14, 1183286.
  • Paape, N., Van Eekelen, J. A. W. M., & Reniers, M. A. (2023). Review of simulation software for cyber-physical production systems with intelligent distributed production control. International Journal of Computer Integrated Manufacturing, 1-23.
  • Pacheco, V. C., dos Santos Bonfim, G., Junior, M. C. B., Alberte, E. P. V., & Costa, D. B. (2023). Modelagem baseada em agentes para a gestão da segurança no canteiro de obras: uma análise da produção científica. SIMPÓSIO BRASILEIRO DE GESTÃO E ECONOMIA DA CONSTRUÇÃO, 13, 1-9.
  • Palanca, J., Rincon, J. A., Carrascosa, C., Julian, V. J., & Terrasa, A. (2023). Flexible Agent Architecture: Mixing Reactive and Deliberative Behaviors in SPADE. Electronics, 12(3), 659.
  • Palma-Morales, O. J., Noguera-Hidalgo, Á. L., Hernández-Sandoval, J. S., & Ávila-Robayo, D. S. (2023). Redes de narcotráfico marítimo: un análisis desde la complejidad y la simulación de sistemas sociales. Revista Científica General José María Córdova, 21(43), 743-764.
  • Panbumrungkij, T., & Vannametee, E. (2023). การ ศึกษา พฤติกรรม การ เลือก สถาน ที่ ท่องเที่ยว และ การ กระจาย ตัว ของ นักท่องเที่ยว ด้วย แบบ จำลอง ตัวแทน: กรณี ศึกษา จังหวัด ชลบุรี: A Case Study of Chon Buri Province. Journal of Letters, 52(2), 1-30.
  • Pantelić, S., Milovanović, B., Đogatović, M., Živanović, P., Bajčetić, S., Tica, S., & Nađ, A. (2023). Consequence Assessment Model for Gasoline Transport: Belgrade Case Study Based on Multi-Agent Simulation. Sustainability, 15(3), 2598.
  • Panzer, M., & Gronau, N. (2023). Designing an adaptive and deep learning based control framework for modular production systems. Journal of Intelligent Manufacturing, 1-24.
  • Patel, V. (2023). The impacts of climate change on structurally interconnected social-ecological systems: using integrated spatial modelling to assess beehive migration patterns in Western Australia (Doctoral dissertation, The University of Western Australia).
  • Paudel, R., & Ligmann-Zielinska, A. (2023). A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-Based Model Development. Algorithms, 16(7), 338.
  • Paul, P. N. T., Diarra, M., Dione, D., & Dathe, H. (2023, October). An Agent-Based Model for Studying Effects of Mosquito Bites Protection and the Intra-host Treatment on the Malaria Transmission. In Proceedings of the Future Technologies Conference (pp. 298-308). Cham: Springer Nature Switzerland.
  • Pavone, M. (2023, February). How a Different Ant Behavior Affects on the Performance of the Whole Colony. In Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings (Vol. 13838, p. 187). Springer Nature.
  • Pecoraro, F., Accordino, F., Cecconi, F., & Paolucci, M. (2023). Agent Based Modelling for Simulating the Interregional Patient Mobility in Italy. Studies in Health Technology and Informatics, 302, 297-301.
  • Pedreschi, D., Dignum, F., Morini, V., Pansanella, V., & Cornacchia, G. (2023). Towards a Social Artificial Intelligence. In Human-Centered Artificial Intelligence: Advanced Lectures (pp. 415-428). Cham: Springer International Publishing.
  • Peel, A., Dabholkar, S., Anton, G., Horn, M., & Wilensky, U. (2023). Characterizing changes in teacher practice and values through co-design and implementation of computational thinking integrated biology units. Computer Science Education, 1-26.
  • Peleg, R., Lahav, O., Hagab, N., Talis, V., & Levy, S. T. (2023). Listening to or looking at models: Learning about dynamic complex systems in science among learners who are blind and learners who are sighted. Journal of Computer Assisted Learning.
  • Pellet, T. L. (2023). Essays in Macroeconomics, Production Networks and Monetary History (Doctoral dissertation, Northwestern University).
  • Peng, L., & Luo, S. (2023). Exploration of tolerance of unfairness under COVID-19 mortality salience and its effect on epidemic development. Journal of Pacific Rim Psychology, 17, 18344909231165188.
  • Peng, Y., Lopez, J. M. R., Santos, A. P., Mobeen, M., & Scheffran, J. (2022). Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil. Cities, 104161.
  • Peñuela Escobar, L. F. (2023). Agent-based Modelling for Assessing Potential Water-Related Conflicts (Doctoral dissertation, Escuela Colombiana de Ingeniería).
  • Perdana, M. J. G., Mulyatno, I. P., Santosa, A. W. B., & dibantu Komputer, L. D. K. K. (2023). Analisa Evakuasi Pada Kapal KM. Kirana IX 9168 GT Menggunakan Metode Agent Based Modelling Simulation Dalam Kondisi Kebakaran. Jurnal Teknik Perkapalan, 11(2), 93.
  • Perez Aguilar, D. A. (2023). Diseño de un clasificador para el mantenimiento predictivo de una red de distribución eléctrica utilizando Deep Learning (Doctoral dissertation, University of Piura, Peru).
  • Peters, M. (2023). Smallholder Market Systems: Understanding System Behavior and the Role of Relationships (Doctoral dissertation, The George Washington University).
  • Pietzsch, B. W., Schmidt, A., Groeneveld, J., Bahlburg, D., Meyer, B., & Berger, U. (2023). The impact of salps (Salpa thompsoni) on the Antarctic krill population (Euphausia superba): an individual-based modelling study. Ecological Processes, 12(1), 1-16.
  • Platas-López, A., Guerra-Hernández, A., Quiroz-Castellanos, M., & Cruz-Ramírez, N. (2023). Agent-Based Models Assisted by Supervised Learning: A Proposal for Model Specification. Electronics, 12(3), 495.
  • Potturi, A. (2023). MASS JAVA Benchmarking (Doctoral dissertation, University of Washington).
  • Potvin, J. (2023). Data with direction: design research leading to a system specification for ‘an internet of rules’ (Doctoral dissertation, Université du Québec en Outaouais).
  • Prakayaphun, T., Hayashi, Y., Vichiensan, V., & Takeshita, H. (2023). Identifying Impacts of School-Escorted Trips on Traffic Congestion and the Countermeasures in Bangkok: An Agent-Based Simulation Approach. Sustainability, 15(23), 16244.
  • Prédhumeau, M., & Manley, E. (2023). A synthetic population for agent-based modelling in Canada. Scientific Data, 10(1), 148.
  • Prinz, A., Engebretsen, M., Gjøsæter, T., Møller-Pedersen, B., & Xanthopoulou, T. D. (2023). Models, systems, and descriptions. Frontiers in Computer Science, 5, 1031807.
  • Prnjak, A., Zaharija, G., Mladenović, M., & Nejašmić, D. (2023). USING SIMULATION IN TEACHING ARTIFICIAL INTELLIGENCE. In INTED2023 Proceedings (pp. 6954-6960). IATED.
  • Purbani, D., Marzuki, M. I., Ontowirjo, B., Zein, F. M., & Wisha, U. J. Tsunami Evacuation Model Assessment in the Panimbang Subdistrict, Banten Province, Indonesia: GIS-and Agent-Based Modeling Approaches. Banten Province, Indonesia: GIS-and Agent-Based Modeling Approaches.
  • Qabaja, H., Ashqer, M. I., Bikdash, M., & Ashqar, H. I. (2023). A Meso-Scale Petri Net Model to Simulate a Massive Evacuation along the Highway System. Future Transportation, 3(1), 311-328.
  • Qi, J. (2023). Advancing Green Stormwater Infrastructure Through Understanding the Influences of Social Factors (Doctoral dissertation, The University of North Carolina at Charlotte).
  • Qi, X., Xie, J., Huang, H., Li, J., & Yuan, W. (2023). Reconciling grain production and environmental costs during rural livelihood transitions: a simulation-based approach in southern China. Food Security, 1-19.
  • Quagliarini, E., Bernardini, G., & D'Orazio, M. (2023). How Increasing Temperature Scenarios Can Alter Terrorist Act Risk in Different Historical Squares? A Simulation-Based Approach in Typological Italian Squares. Heritage, 6(7), 5151-5188.
  • Rabb, N., & Cowen, L. (2023). Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization. In International Conference on Complex Networks and Their Applications (pp. 3-15). Springer, Cham.
  • Rabb, N., Cowen, L., & de Ruiter, J. P. (2023). Investigating the effect of selective exposure, audience fragmentation, and echo-chambers on polarization in dynamic media ecosystems. Applied Network Science, 8(1), 78.
  • Radzvilas, M., De Pretis, F., Peden, W., Tortoli, D., & Osimani, B. (2023). Incentives for research effort: An evolutionary model of publication markets with double-blind and open review. Computational Economics, 61(4), 1433–1476. https://doi.org/10.1007/s10614-022-10250-w
  • Rahman, S., & Li, S. (2023, March). Multi-Agent-Based Modeling of Deshopping Behavior Considering Two or More Shops or Web Sites. In 2023 International Conference on Information Management (ICIM) (pp. 104-109). IEEE.
  • Raimbault, J., & Pumain, D. (2023, July). Innovation dynamics in multi-scalar systems of cities. In ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press.
  • Rajanikanth, K. N., Sait, M. R., & Kashi, S. R. (2023, February). Enhancing Immersive User Experience Quality of StudoBot Telepresence Robots with Reinforcement Learning. In 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS) (pp. 1-6). IEEE.
  • Rakotoarisoa, M. M., Reulier, R., & Delahaye, D. (2023). Agent-Based Modelling of the Evolution of Hydro-Sedimentary Connectivity: The Case of Flash Floods on Arable Plateaus. Applied Sciences, 13(5), 2967.
  • Ran, P., Jie, M. C., Keyuan, M. D., Yehao, B. L., & Xueliang, B. L. (2023). Research on Traditional Performing Places in Wuling Mountain Area of China. Convergence of Contemporary Thought in Architecture, Urbanism, and Heritage Studies, 237.
  • Rangoni, R., & Jager, W. Social Dynamics von Littering and Adaptable Clean Solutions Explored Usage Agent-Based Modelling Download PDF. Journal out Artificial Societies and Socially Simulation, 20(2), 1.
  • Rankin, P., Meunier, L., & Kontopoulou, M. (2023, May). Characterization and identification of microplastics in freshwater systems. In AIP Conference Proceedings (Vol. 2607, No. 1). AIP Publishing.
  • Rappel, O., Ben-Asher, J. Z., & Bruckstein, A. M. (2023). Exploration of unknown indoor regions by a swarm of energy-constrained drones. arXiv preprint arXiv:2305.08957.
  • Rathee, G., Garg, S., Kaddoum, G., Choi, B. J., Benslimane, A., & Hassan, M. M. (2023). A secure and trusted context prediction for next generation autonomous vehicles. Alexandria Engineering Journal, 78, 131-140.
  • Rattner, B. A., Bean, T. G., Beasley, V. R., Berny, P., Eisenreich, K. M., Elliott, J. E., ... & Salice, C. J. (2023). Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects. Integrated Environmental Assessment and Management.
  • Regis, S., Manicom, O., & Doncescu, A. (2023). An agent-based model of COVID-19 pandemic and its variants using fuzzy subsets and real data applied in an island environment. The Knowledge Engineering Review, 38, e2.
  • Reimuth, A., Hagenlocher, M., Yang, L. E., Katzschner, A., Harb, M., & Garschagen, M. (2023). Urban growth modelling for the assessment of future climate and disaster risks: approaches, gaps and needs. Environmental Research Letters.
  • Reinhard, A., & Zaia, S. (2023). Photogrammetry and GIS in Human-Occupied Digital Landscapes. Advances in Archaeological Practice, 1-13.
  • Ren, J., Li, H., Zhang, M., Wu, C., & Yu, X. (2023). A Self-powered Sensor Network Data Acquisition, Modeling and Analysis Method for Cold Chain Logistics Quality Perception. IEEE Sensors Journal.
  • Rennels, L., & Chasins, S. E. (2023). How Domain Experts Use an Embedded DSL. Proceedings of the ACM on Programming Languages, 7(OOPSLA2), 1499-1530.
  • Richards, S., Gámez, S., & Harris, N. C. (2023). Modeling effects of habitat structure on intraguild predation frequency and spatial coexistence between jaguars and ocelots. Behavioral Ecology, arad080.
  • Richmond, P., Chisholm, R., Heywood, P., Chimeh, M. K., & Leach, M. (2023). FLAME GPU 2: A framework for flexible and performant agent based simulation on GPUs. Software: Practice and Experience.
  • Rivière, J., Hénard, A., Peillard, E., Kubicki, S., & Coppin, G. (2023, May). How to Grasp the Complexity of Self-Organised Robot Swarms?. In French Regional Conference on Complex Systems (FRCCS 2023).
  • Robak, A., Bush, S., & Bjornlund, H. (2023). Advancing the impact identification step of benefit-cost analysis of potable water infrastructure investments: A systems method for identifying important impacts pre-monetisation. Water Research, 239, 120058.
  • Roberts, S., Lebbin, P., Gwynne, S., Thomas, R., Dabkowski, R., Law, A., ... & Grewal, A. (2023). Communicable Disease Transmission in Air Travel: Human Behavior–Phase 1 Report (No. DOT/FAA/AM-23/28). United States. Department of Transportation. Federal Aviation Administration. Office of Aviation. Civil Aerospace Medical Institute.
  • Robinson, J. A., Kanduč, T., Sarigiannis, D., & Kocman, D. (2023). Simulating the impact of particulate matter exposure on health-related behaviour: A comparative study of stochastic modelling and personal monitoring data. Health & Place, 83, 103111.
  • Rodríguez-Arias, A., Alonso-Betanzos, A., Guijarro-Berdiñas, B., & Sánchez-Marroño, N. (2023). Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures. arXiv preprint arXiv:2307.15723.
  • Rodríguez, J., Hölzchen, E., Caso-Alonso, A. I., Berndt, J. O., Hertler, C., Timm, I. J., & Mateos, A. (2023). Computer simulation of scavenging by hominins and giant hyenas in the late Early Pleistocene. Scientific Reports, 13(1), 14283.
  • Rodriguez, M., Boixader, F., Epelde, F., Bruballa, E., De Giusti, A., Wong, A., ... & Luque, E. (2023, June). Resilience Analysis of an Emergency Department in Stressful Situations. In Conference on Cloud Computing, Big Data & Emerging Topics (pp. 45-54). Cham: Springer Nature Switzerland.
  • Rodríguez, S. S., & Alatriste, F. R. (2023). La coopetencia como un fenómeno emergente. Un modelo basado en agentes. Revista Universidad y Empresa, 25(45), 1-31.
  • Roma, G. Agent-Based Music Live Coding: Sonic adventures in 2D. Organised Sound, 1-10.
  • Roosta, A., Kaths, H., Barthauer, M., Erdmann, J., Flötteröd, Y. P., & Behrisch, M. (2023, June). The state of bicycle modeling in sumo. In SUMO Conference Proceedings (Vol. 4, pp. 55-64).
  • Rouleau, M. D. (2023). Agent-based modeling (ABM). In Dictionary of Ecological Economics (pp. 8-8). Edward Elgar Publishing.
  • Ruiz-Martin, C. (2023). Theory and foundations of modeling and simulation. Simulation, 99(5), 431-432.
  • Rumsey, K., Francom, D., & Shen, A. (2023). Generalized Bayesian MARS: Tools for Emulating Stochastic Computer Models. arXiv preprint arXiv:2306.01911.
  • Rusinko, J. (2023). 2012-Rusinko-Swan-Agent-Based Fabric Modeling Using Differential Equations.
  • Russo, R., & Blikstein, P. (2023). Beyond Disinformation: An Agent-Based Modeling and Curriculum for the Post-Truth World. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 2125-2126. International Society of the Learning Sciences.
  • Saba, J., Hel-Or, H., & Levy, S. T. (2023). Promoting learning transfer in science through a complexity approach and computational modeling. Instructional Science, 1-33.
  • Saba, J., Kapur, M., & Roll, I. (2023, June). The Development of Multivariable Causality Strategy: Instruction or Simulation First?. In International Conference on Artificial Intelligence in Education (pp. 41-53). Cham: Springer Nature Switzerland.
  • Sabal, J., Kapur, M., & Roll, I. 1 Hebrew University of Jerusalem, Jerusalem, Israel 2. In Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (p. 41). Springer Nature.
  • Sáez, P., Herrera, C., & Parada, V. (2023). Reducing Nervousness in Master Production Planning: A Systematic Approach Incorporating Product-Driven Strategies. Algorithms, 16(8), 386.
  • Sáez, P., Herrera, C., Booth, C., Belmokhtar-Berraf, S., & Parada, V. (2023). A product-driven system with an evolutionary algorithm to increase flexibility in planning a job shop. PloS one, 18(2), e0281807.
  • Safdar, M. F., Nowak, R. M., & Pałka, P. (2023). Exploring artificial intelligence algorithms for electrocardiogram (ECG) signal analysis: A comprehensive review. Computers in Biology and Medicine, 107908.
  • Sagar, S. (2023). Trust Computational Heuristics for Social Internet of Things (Doctoral dissertation, Macquarie University).
  • Saint-Pierre, P., & Savy, N. (2023). Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue. The International Journal of Biostatistics.
  • Saisridhar, P., Thürer, M., & Avittathur, B. (2023). Assessing supply chain responsiveness, resilience and robustness (Triple-R) by computer simulation: a systematic review of the literature. International Journal of Production Research, 1-31.
  • Salazar-Serna, K., Cadavid, L., Franco, C. J., & Carley, K. M. (2023, September). Simulating Transport Mode Choices in Developing Countries. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 209-218). Cham: Springer Nature Switzerland.
  • Salman, M. Y., & Hasar, H. (2023). Review on Environmental Aspects in Smart City Concept: Water, Waste, Air Pollution and Transportation Smart Applications using IoT Techniques. Sustainable Cities and Society, 104567.
  • Sánchez, S., & Alatriste, F. R. (2023). Coopetition as an Emerging Phenomenon: A Model Based on Agents. Universidad & Empresa, 25(45), 1.
  • Sanfilippo, D. The Aesthetics of Musical Complex Systems. Organised Sound, 1-11.
  • Sang, B., Aghamohammadi, N., & Md Noor, R. (2023). The Effects of Dynamic Strategy and Updating Network Structure Towards Customer Participation Innovation Performance. Journal of the Knowledge Economy, 1-31.
  • Santana-Robles, F., Granillo-Macias, R., Armas-Alvarez, B., & Rodríguez, Z. B. (2023). Modelos matemáticos para la evacuación de personas en la cadena de suministro humanitaria: una revisión. Ingenio y Conciencia Boletín Científico de la Escuela Superior Ciudad Sahagún, 10(19), 48-60.
  • Santos, J. I., Pereda, M., Ahedo, V., & Galán, J. M. (2023, July). NetLogo Teaching Tool to Illustrate the Cooling Process in Simulated Annealing Using the Metropolis Model. In Industry 4.0: The Power of Data: Selected Papers from the 15th International Conference on Industrial Engineering and Industrial Management (pp. 19-27). Cham: Springer International Publishing.
  • Santos, M., Garcês, C., Ferreira, A., Carvalho, D., Travassos, P., Bastos, R., ... & Cabral, J. A. (2023). Side effects of European eco schemes and agri-environment-climate measures on endangered species conservation: Clues from a case study in mountain vineyard landscapes. Ecological Indicators, 148, 110155.
  • Santucci, J. F., Capocchi, L., Ören, T., Szabo, C., & Graciano Neto, V. V. (2023). Synergies of Soft Computing and M&S. In Body of Knowledge for Modeling and Simulation: A Handbook by the Society for Modeling and Simulation International (pp. 287-309). Cham: Springer International Publishing.
  • Sapienza, A., & Falcone, R. (2023). An Autonomy-Based Collaborative Trust Model for User-IoT Systems Interaction. In Intelligent Distributed Computing XV (pp. 178-187). Cham: Springer International Publishing.
  • Sapienza, A., & Falcone, R. (2023). Exploiting autonomy in a User-IoT system collaborative trust model. International Journal of Parallel, Emergent and Distributed Systems, 1-13.
  • Sapino, F., Haer, T., Saiz-Santiago, P., & Pérez-Blanco, C. D. (2023). A multi-agent cellular automata model to explore water trading potential under information transaction costs. Journal of Hydrology, 618, 129195.
  • Sarnatskyi, V. V., & Baklan, I. V. (2023). CTRACEENV: A PLATFORM FOR DEVELOPMENT AND ANALYSIS OF AGENT-BASED EPIDEMIOLOGICAL MODELS. ВЧЕНІ ЗАПИСКИ, 234-240.
  • Saval, A., Minh, D. P., Chapuis, K., Tranouez, P., Caron, C., Daudé, É., & Taillandier, P. (2023). Dealing with mixed and non-normative traffic. An agent-based simulation with the GAMA platform. Plos one, 18(3), e0281658.
  • Sazzad, A., Nawer, N., Mahbub Rimi, M., Habibul Kabir, K., & Foysal Haque, K. (2023). Designing of an Underwater-Internet of Things (U-IoT) for Marine Life Monitoring. In The Fourth Industrial Revolution and Beyond: Select Proceedings of IC4IR+ (pp. 291-303). Singapore: Springer Nature Singapore.
  • Schmickl, T., & Karsai, I. (2023). Self-complexification through integral feedback in eusocial paper wasps of various levels of sociality. Heliyon, 9(9).
  • Schmolke, A., Galic, N., & Hinarejos, S. (2023). SolBeePop: A model of solitary bee populations in agricultural landscapes. Journal of Applied Ecology.
  • Schoeman, I., & Chakwizira, J. (2023). Advancing a Performance Management Tool for Service Delivery in Local Government. Administrative Sciences, 13(2), 31.
  • Scholz, G., Wijermans, N., Paolillo, R., Neumann, M., Masson, T., Chappin, É., ... & Kocheril, G. (2023). Social Agents? A Systematic Review of Social Identity Formalizations. Journal of Artificial Societies and Social Simulation, 26(2), 6.
  • Schubert, D. (2023, September). Using Survey Data to Develop Agent-Based Models of Spatial Segregation. In Conference of the European Social Simulation Association (pp. 609-619). Cham: Springer Nature Switzerland.
  • Secchi, D. (2023). The relevance of social dynamics and dispositions on non-traditional aids to the strategic process. In Cognitive aids in strategy (Vol. 6, pp. 135-157). Emerald Publishing Limited.
  • Seger, H. (2023). Agent-based and contact network modeling applications for Escherichia coli transmission in commercial feedlot settings (Doctoral dissertation, Kansas State University).
  • Seizovic, A., Thorpe, D., Goh, S., & Skoufer, L. (2023). Cybernetics and battle management system (BMS) in network soldier system application. Australian Journal of Multi-Disciplinary Engineering, 1-23.
  • Sellán Vera, K. B. (2023). Modelado basado en agentes para comprobar la validez de un sistema industrial circular en Santa Elena, Ecuador (Bachelor's thesis, La Libertad: Universidad Estatal Península de Santa Elena, 2023).
  • Sengupta, R., Vankeerberghen, G., Wen, R., Rao, J., & Chen, Y. (2023). GIS‐enabled historiography to determine travel routes during the Western Han period via agent‐based models and least‐cost path analysis. Transactions in GIS, 27(4), 1090-1103.
  • Sengupta, S., Kovalevsky, D. V., Bouwer, L. M., & Scheffran, J. (2023). Urban Planning of Coastal Adaptation under Sea-Level Rise: An Agent-Based Model in the VIABLE Framework. Urban Science, 7(3), 79.
  • Serena, L., Marzolla, M., D’Angelo, G., & Ferretti, S. (2023). A review of multilevel modeling and simulation for human mobility and behavior. Simulation Modelling Practice and Theory, 102780.
  • Seuru, Samuel, Liliana Perez, and Ariane Burke. "Why Were Rabbits Hunted in the Past? Insights from an Agent-Based Model of Human Diet Breadth in Iberia During the Last Glacial Maximum." Modelling Human-Environment Interactions in and beyond Prehistoric Europe. Cham: Springer International Publishing, 2023. 107-123.
  • Shaaban, M. (2023). Viability of the social–ecological agroecosystem (ViSA). SoftwareX, 101360.
  • Shahangian, S. A., Tabesh, M., Yazdanpanah, M., Akbarzadeh, A., Raoof, M. A., Zobeidi, T., ... & Sitzenfrei, R. Introducing a Novel Hybrid Agent-Based Framework for Simulating the Adoption of Residential Water Conservation Behaviors. In World Environmental and Water Resources Congress 2023 (pp. 775-788).
  • Shaharuddin, R. A., & Misro, M. Y. (2023). Controlling Traffic Congestion in Urbanised City: A Framework Using Agent-Based Modelling and Simulation Approach. ISPRS International Journal of Geo-Information, 12(6), 226.
  • Shakya, J., Ghribi, C., & Merghem-Boulahia, L. (2023). Agent-based modeling and simulation for 5G and beyond networks: A comprehensive survey. Simulation Modelling Practice and Theory, 102855.
  • Shangguan, Y., Tian, X., Jin, S., Gao, K., Hu, X., Yi, W., ... & Wang, S. (2023). On the Fundamental Diagram for Freeway Traffic: Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models. Mathematics, 11(16), 3460.
  • Shen, X., & Zhou, Y. (2023, April). Analysis and Research on Express Packaging Recycling Logistics System Based on Netlogo. In 2023 IEEE 3rd International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB) (pp. 124-129). IEEE.
  • Shinde, S. B., & Kurhekar, M. P. (2023, June). Agent-based modeling of complex adaptive systems: An interdisciplinary approach. In AIP Conference Proceedings (Vol. 2705, No. 1). AIP Publishing.
  • Shobayo, P. (2023). Enhancing the competitiveness of inland waterway transport: a multi-methodological approach applied to port barge congestion and urban areas (Doctoral dissertation, University of Antwerp).
  • Siatras, V., Bakopoulos, E., Mavrothalassitis, P., Nikolakis, N., & Alexopoulos, K. (2023). On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System. Applied Sciences, 13(17), 9540.
  • Sikk, K. (2023). Exploring Environmental Determinism with Agent-Based Simulation of Settlement Choice. In Modelling Human-Environment Interactions in and beyond Prehistoric Europe (pp. 143-154). Cham: Springer International Publishing.
  • Siljak, H., Kennedy, J., Byrne, S., & Einicke, K. (2023, February). Noise mitigation of UAV operations through a complex networks approach. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings (Vol. 265, No. 5, pp. 2208-2214). Institute of Noise Control Engineering.
  • Simms, I. J. (2023). Are Food Banks Impacting Food Retail? Examining the Relationship Between Hunger Relief Distributions and Retail Transactions in a Local Food Environment (Doctoral dissertation, Case Western Reserve University).
  • Singh, S. K., Sharma, C., & Maiti, A. (2023). Modeling and experimental validation of forward osmosis process: Parameters selection, permeate flux prediction, and process optimization. Journal of Membrane Science, 121439.
  • Slingerland, G., Nikolic, I., & Brazier, F. (2023). It's in the social network: The Social Neighbourhood model to unravel local social structures for liveable and safe neighbourhoods. Cities, 135, 104215.
  • Smaldino, P. (2023). Modeling social behavior: Mathematical and agent-based models of social dynamics and cultural evolution. Princeton University Press.
  • Smith, M. M., & Pauli, J. N. Connectivity maintains genetic diversity and population persistence within an archipelagic refugia even under declining lake ice. Mechanisms of species recovery for a forest carnivore in a changing landscape, 173.
  • Snyder, S., Zhu, K., Vega, R., Nowzari, C., & Parsa, M. (2023). Zespol: A Lightweight Environment for Training Swarming Agents. arXiv preprint arXiv:2306.17744.
  • Sobkowicz, P. (2023). Social Depolarization and Diversity of Opinions—Unified ABM Framework. Entropy, 25(4), 568.
  • Sondakh, D. E., Pungus, S. R., & Tombeng, M. T. (2023). Pengenalan Computational Thinking Bagi Siswa Vokasi SMKN 1 Sorong. Servitium Smart Journal, 2(2), 90-98.
  • Sotnik, G., Choporov, S., & Shannon, T. (2023). The Role of Social Identity in a Population's Adoption of Prosocial Common-Pool Behavior. Journal of Artificial Societies and Social Simulation, 26(3).
  • Soto, M. D. C. S., Ramírez, M. R., Rojas, E. M., & Barajas, S. M. (2023). La simulación computacional del ecosistema social Entorno Laboral-Escuela. Revista Ibérica de Sistemas e Tecnologias de Informação, (E61), 296-306.
  • Souidi, M. E. H., Maarouk, T. M., Ledmi, M., Ledmi, A., & Rahab, H. (2023). Multi-Pursuer Multi-Evader Games Based on Dynamic Elimination Priorities of the Dominated Strategies. Journal of Computer and Systems Sciences International, 1-14.
  • Soumya, K. V., Sujitha, S., Kanaujia, S., Agarwalla, S., Sameer, S., & Manzoor, T. (2023, February). Silent Surveillance Autonomous Drone For Disaster Management And Military Security Using Artificial Intelligence. In 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 1-4). IEEE.
  • Soy, O., & Tavacioglu, L. (2023). Satisfaction of Istanbul Citizens with Urban Public Transportation. Transactions on Maritime Science, 12(1).
  • Speelman, E. N., Escano, E., Marcos, D., & Becu, N. (2023). Serious games and citizen science; from parallel pathways to greater synergies. Current Opinion in Environmental Sustainability, 64, 101320.
  • Squazzoni, F., & Bianchi, F. (2023). Exploring Interventions on Social Outcomes with In Silico, Agent-Based Experiments. In Causality in Policy Studies: a Pluralist Toolbox (pp. 217-234). Cham: Springer International Publishing.
  • Stavropoulou, E., Mitropoulos, L., Tzouras, P. G., Karolemeas, C., & Kepaptsoglou, K. (2023, March). An Evaluation of Agent-Based Models for Simulating E-Scooter Sharing Services in Urban Areas. In Smart Energy for Smart Transport: Proceedings of the 6th Conference on Sustainable Urban Mobility, CSUM2022, August 31-September 2, 2022, Skiathos Island, Greece (pp. 959-976). Cham: Springer Nature Switzerland.
  • Stellbrink, L., Kojan, L., & Calero Valdez, A. (2023, July). Making Assumptions Transparent: Iterative Exploratory Modeling as a Stepping Stone for Agent-Based Model Development. In International Conference on Human-Computer Interaction (pp. 389-402). Cham: Springer Nature Switzerland.
  • Stern, J. L., Siddiqi, A., & Grogan, P. T. (2023). Effects of individual strategies for resource access on collaboratively maintained irrigation infrastructure. Systems Engineering, 1-17.
  • Sterrett, S. G. (2023). How mathematics figures differently in exact solutions, simulations, and physical models. In Working Toward Solutions in Fluid Dynamics and Astrophysics: What the Equations Don’t Say (pp. 5-29). Cham: Springer International Publishing.
  • Stolpe, K., & Hallström, J. (2023). Visual Programming as a Tool for Developing Knowledge in STEM Subjects: A Literature Review. Programming and Computational Thinking in Technology Education, 130-169.
  • Su, M. (2023). Fostering Deep Understandings of Emergent Science Concepts (Doctoral dissertation, Arizona State University).
  • Su, M., Ha, J., & Xin, Y. (2023). Investigating the Efficacy of an Ontological Framework for Teaching Natural Selection Using Agent-Based Simulations. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 106-113. International Society of the Learning Sciences.
  • Su, Y., & Jiang, X. Simulation research on knowledge flow in a collaborative innovation network. Expert Systems, e13280.
  • Sulis, E., Mariani, S., & Montagna, S. (2023). A survey on agents applications in healthcare: Opportunities, challenges and trends. Computer Methods and Programs in Biomedicine, 107525.
  • Summad, E., Al Kindi, M., Ouhmidou, I., & Al Kindi, A (2023). An Agent-based Modeling Approach for Effective Innovation Ecosystem Orchestration. In Proceedings of the International Conference on Industrial Engineering and Operations Management.
  • Sun, X., Hu, C., Liu, T., Yue, S., Peng, J., & Fu, Q. (2023). Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics. Biomimetics, 8(8), 580.
  • Sun, Z., Bai, R., & Bai, Z. (2023). The Application of Simulation Methods During the COVID-19 Pandemic: A Scoping Review. Journal of Biomedical Informatics, 104543.
  • Swanson, H., Lawrence, L., Arnell, J., Dawkins, A., Jones, B., Sherin, B., & Wilensky, U. (2023, June). How Co-Designing Computational Modeling Activities Helped Teachers Implement Responsive Teaching Strategies. In Proceedings of the 2023 Symposium on Learning, Design and Technology (pp. 79-86).
  • Sznajd-Weron, K., Je˛ drzejewski, A., & Kamińska, B. (2023). Toward Understanding of the Social Hysteresis: Insights From Agent-Based Modeling. Perspectives on Psychological Science, 17456916231195361.
  • Tack, E., Énée, G., Gaillard, T., Fotsing, J. M., & Flouvat, F. (2023, February). Towards User-Centred Validation and Calibration of Agent-Based Models. In 15th International Conference on Agents and Artificial Intelligence (Vol. 1, pp. 322-329). SCITEPRESS-Science and Technology Publications.
  • Taha, M. A., Sah, M., & Direkoglu, C. (2023). Identification of Locations in Mecca using Image Pre-Processing, Neural Networks and Deep Learning. Arabian Journal for Science and Engineering, 1-21.
  • Tan, Q., Han, J., & Liu, Y. (2023). Examining the synergistic diffusion process of carbon capture and renewable energy generation technologies under market environment: A multi-agent simulation analysis. Energy, 282, 128815.
  • Tang, X., & Lira, M. (2023). Drawing Upon Computational Experiences to Navigate Ontologies. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 926-929. International Society of the Learning Sciences.
  • Tang, Y., & Javeed, S. A. (2023). The dynamics of entrepreneurial agglomeration formation: Social selection and simulation. Plos one, 18(9), e0291615.
  • Tang, Y., Cui, A., & Hu, Y. (2023, September). Research on the Ideological and Political System of Logistics System Simulation Course in the Context of Artificial Intelligence. In 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023) (pp. 519-525). Atlantis Press.
  • Tantiwong, C., Dunster, J. L., Cavill, R., Tomlinson, M. G., Wierling, C., Heemskerk, J. W., & Gibbins, J. M. (2023). An agent-based approach for modelling and simulation of glycoprotein VI receptor diffusion, localisation and dimerisation in platelet lipid rafts. Scientific Reports, 13(1), 3906.
  • Tarantino, R., Panunzi, G., & Romano, V. (2023). Modeling of Hardy-Weinberg Equilibrium Using Dynamic Random Networks in an ABM Framework. In International Conference on Complex Networks and Their Applications (pp. 241-250). Springer, Cham.
  • Tarazona, M., Mula, J., & Poler, R. (2023, March). Optimisation of Production Scheduling and Sequencing Problems in Industry 4.0. In IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización (pp. 107-112). Cham: Springer International Publishing.
  • Tashtoush, B., Alyahya, W. E., Al Ghadi, M., Al-Omari, J., & Morosuk, T. (2023). Renewable energy integration in water desalination: State-of-the-art review and comparative analysis. Applied Energy, 352, 121950.
  • Tauböck, S., Schöfecker, A., Ledermüller, K., Krakovsky, M., Sharma, S., Reismann, M., ... & Wurzer, G. (2023). PASSt–Predictive Analytics Services für Studienerfolgsmanagement. Zeitschrift für Hochschulentwicklung, 18(Sonderheft Hochschullehre), 251-277.
  • Tedeschi, L. O. (2023). The prevailing mathematical modelling classifications and paradigms to support the advancement of sustainable animal production. animal, 100813.
  • Thompson, J., Zhao, H., Seneviratne, S., Byrne, R., Vidanaarachchi, R., & McClure, R. (2023). A Template for Transfer of NetLogo Models to High-Performance Computing Environments for Enhanced Real-World Decision-Support. In: Squazzoni, F. (eds) Advances in Social Simulation. ESSA 2022. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-34920-1_45
  • Tian, Y., Zhao, Y., Zhang, X., Li, S., & Wu, H. Incorporating Carbon Sequestration into Lake Management: A New Perspective on Climate Change. Science of The Total Environment, 895, 164939.
  • Tian, Z. (2023, October). Multi-Stage Vertex-Centric Programming for Agent-Based Simulations. In Proceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE'23), October 22--23, 2023, Cascais, Portugal.
  • Tian, Z., Lindner, P., Nissl, M., Koch, C., & Tannen, V. (2023). Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations. Proceedings of the ACM on Management of Data, 1(2), 1-28.
  • Tittonell, P. (2023). Trade-Offs Around Production and Livelihood Decisions. In A Systems Approach to Agroecology (pp. 317-353). Cham: Springer Nature Switzerland.
  • Tomás, V. R., García, L. A., & Alonso, A. L. (2023). An agent-based platform to evaluate V2X routing road traffic scenarios. Simulation Modelling Practice and Theory, 102750.
  • Trček, D. (2023). Trust Management Methodology and Agents Simulations Framework for Conflict Research. Advanced Theory and Simulations, 2200705.
  • Trinh, T. T., & Munro, A. (2023). Integrating a choice experiment into an agent-based model to simulate climate-change induced migration: The case of the Mekong River Delta, Vietnam. Journal of Choice Modelling, 48, 100428.
  • Trivedi, A., Pandey, M., Ramesh, G., & Chhabra, R. (2023). An agent based modeling approach to evaluate crowd movement strategies and density at bathing areas during Kumbh Mela-2019. Multimedia Tools and Applications, 1-39.
  • Tshakwanda, P. M., Arzo, S. T., & Devetsikiotis, M. (2023, March). Multi-agent-based simulation of intelligent network system. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0813-0819). IEEE.
  • Tsurushima, A. (2023, January). Efficient Visual Sign Assignment for Crowd Evacuation Guidance Considering Risks and Multiple Objectives. In Agents and Artificial Intelligence: 14th International Conference, ICAART 2022, Virtual Event, February 3–5, 2022, Revised Selected Papers (pp. 3-26). Cham: Springer International Publishing.
  • Tyagi, H., Kumar, R., & Pandey, S. K. (2023). A detailed study on trust management techniques for security and privacy in IoT: Challenges, trends, and research directions. High-Confidence Computing, 100127.
  • Udayakumar, R., Kalam, M. A., Sugumar, R., & Elankavi, R. (2023). Assessing Learning Behaviors Using Gaussian Hybrid Fuzzy Clustering (GHFC) in Special Education Classrooms. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 14(1), 118-125.
  • Uhrmacher, A. M., Frazier, P., Hähnle, R., Klügl, F., Lorig, F., Ludäscher, B., ... & Wilsdorf, P. (2023). 4.3 Context, composition, automation and communication: towards sustainable simulation studies. Computer Science Methods for Effective and Sustainable Simulation Studies, 30(4), 53.
  • Umlauft, M., Gojkovic, M., Harshina, K., & Schranz, M. (2023). Bottom-Up Bio-Inspired Algorithms for Optimizing Industrial Plants. In ICAART (1) (pp. 59-70).
  • Urbane, L. M., Chen, C., Lindell, M., & Wang, H. (2023). Which Mode Should I Choose to Evacuate: Analyze and Synthesize Case Studies of Rapid-Onset Disasters. In International Conference on Transportation and Development 2023 (pp. 72-83).
  • Uslu, B. Ç. (2023). IoT uygulamalarında çok etmenli sistemlerin (MAS) birlikte çalışabilirliğinin rolü: Üretim sistemlerindeki son gelişmeler üzerine bir araştırma. Journal of the Faculty of Engineering & Architecture of Gazi University/Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi,, 38(2).
  • Uthpala, N., Hansika, N., Dissanayaka, S., Tennakoon, K., Dharmarathne, S., Vidanarachchi, R., ... & Herath, D. (2023). Analyzing transportation mode interactions using agent-based models. SN Applied Sciences, 5(12), 357.
  • Vargas-Pérez, V. A., Chica, M., Leon, C. J., & Cordon, O. Understanding the Impact of Climate Change in European Island Tourism by Agent-Based Simulations. Available at SSRN 4600317.
  • Vargas, L. F. C., & Marmolejo-Saucedo, J. A. (2023, September). A Strategy to Analyze the Metal Packaging Market in the Food Cans Industry Using Agent-Based Simulation. In Computer Science and Engineering in Health Services: 6th EAI International Conference Proceedings, COMPSE 2022, Mexico City, July 28, 2022 (p. 109). Springer Nature.
  • Vasquez, S., Barquero, B., & Bosch, M. (2023, July). Modelling COVID-19 data with simulations: A recursive process. In Thirteenth Congress of the European Society for Research in Mathematics Education (CERME13) (No. 42). Alfréd Rényi Institute of Mathematics; ERME.
  • Veloso, P., & Krishnamurti, R. (2023). Spatial synthesis for architectural design as an interactive simulation with multiple agents. Automation in Construction, 154, 104997.
  • Verma, P., Gupta, A., Kumar, M., & Gill, S. S. (2023). FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease. Internet of Things, 100828.
  • Viaene, K. P., De Schamphelaere, K. A., & Van Sprang, P. (2023). Extrapolation of Metal Toxicity Data for the Rotifer Brachionus calyciflorus Using an Individual‐Based Population Model. Environmental Toxicology and Chemistry.
  • Villa Enciso, E. M. El rol de la universidad en la innovación inclusiva: análisis desde el modelado y simulación computacional (Doctoral dissertation, Universidad Nacional de Colombia).
  • Villa-Enciso, E., Ruiz-Castañeda, W., & Robledo Velásquez, J. (2023). Agent-Based Model to Analyze the Role of the University in Reducing Social Exclusion. Sustainability, 15(16), 12666.
  • Villamor, G. B. (2023). Gender and Water-Energy-Food Nexus in the Rural Highlands of Ethiopia: Where Are the Trade-Offs?. Land, 12(3), 585.
  • Villavicencio-Valero, K., Ramírez-Juidias, E., Madueño-Luna, A., Madueño-Luna, J. M., & López-Gordillo, M. C. (2023). Influence of the Surface Temperature Evolution over Organic and Inorganic Compounds on Iapetus. Universe, 9(9), 403.
  • Vizanko, B., Kadinski, L., Ostfeld, A., & Berglund, E. Z. (2023). Social Distancing, Water Demand Changes, and Quality of Drinking Water During the Covid-19 Pandemic. Water Demand Changes, and Quality of Drinking Water During the Covid-19 Pandemic.
  • von Essen, M., & Lambin, E. F. (2023). Agent-Based Simulation of Land Use Governance (ABSOLUG) in Tropical Commodity Frontiers. Journal of Artificial Societies and Social Simulation, 26(1).
  • Vosoughkhosravi, S., Norouziasl, S., & Jafari, A. (2023, July). Lighting energy load prediction framework using agent-based simulation and artificial neural network models. In EC3 Conference 2023 (Vol. 4, pp. 0-0). European Council on Computing in Construction.
  • Vu, T. M., Buckley, C., Duro, J. A., Brennan, A., Epstein, J. M., & Purshouse, R. C. (2023). Can social norms explain long-term trends in alcohol use? Insights from inverse generative social science. Journal of artificial societies and social simulation: JASSS, 26(2).
  • Waight, N., Liu, X., & Whitford, M. (2023). “Like They Are Everyday Substances, You Like See Them, Hold Them, Use Them Every Day”: Students’ Understanding of Big Ideas and Macro and Submicro Chemistry Phenomena in the Context of Computer-Based Models. Research in Science Education, 1-26.
  • WALID, M., & ZAKARIA, A. B. (2023). Simulation Du Modèle De Ségrégation De Schelling (Doctoral dissertation, UNIVERSITY BBA).
  • Walzberg, J., Sethuraman, S., Ghosh, T., Uekert, T., & Carpenter, A. (2023). Think before you throw! An analysis of behavioral interventions targeting PET bottle recycling in the United States. Energy Research & Social Science, 100, 103116.
  • Wang, B., & Liao, X. (2023). A trusted routing mechanism for multi-attribute chain energy optimization for Industrial Internet of Things. Neural Computing and Applications, 1-11.
  • Wang, D., Wu, Z., Ma, G., Gao, Z., & Yang, Z. (2023). Coupled Control of Traffic Signal and Connected Autonomous Vehicles at Signalized Intersections. Journal of Advanced Transportation, 2023.
  • Wang, H. H., Bishop, A. E., Koralewski, T. E., & Grant, W. E. (2023). In Search of Proximate Triggers of Anthrax Outbreaks in Wildlife: A Hypothetical Individual-Based Model of Plasmid Transfer within Bacillus Communities. Diversity, 15(3), 347.
  • Wang, H., Zhang, Y., & Zhao, J. (2023). Enhancing the SVD Compression Losslessly. Journal of Computational Science, 102182.
  • Wang, J., Wu, J., Li, J., Kong, R., Li, X., & Wang, X. (2023). Simulation of various biofilm fractal morphologies by agent-based model. Colloids and Surfaces B: Biointerfaces, 113352.
  • Wang, L., Wu, J., Yang, M., Zhang, J., & Meng, Z. (2023, June). Guidance Method of Connected Autonomous Vehicles Under Automatic Control Intersections. In Proceedings of KES-STS International Symposium (pp. 35-43). Singapore: Springer Nature Singapore.
  • Wang, T., Liu, Y., Li, Q., Du, P., Zheng, X., & Gao, Q. (2023). State-of-the-Art Review of the Resilience of Urban Bridge Networks. Sustainability, 15(2), 989.
  • Wang, T., Zhang, X., Ma, Y., & Wang, Y. (2023). Risk contagion and decision-making evolution of carbon market enterprises: Comparisons with China, the United States, and the European Union. Environmental Impact Assessment Review, 99, 107036.
  • Wang, W., Wu, F., Yu, H., & Wang, X. (2023). Assessing the effectiveness of intervention policies for reclaimed water reuse in China considering multi-scenario simulations. Journal of Environmental Management, 335, 117519.
  • Wang, Y., Ge, J., & Comber, A. (2023). A pedestrian ABM in complex evacuation environments based on Bayesian Nash Equilibrium. AGILE: GIScience Series, 4, 50.
  • Wang, Y., Ge, J., & Comber, A. (2023). Navigation in Complex Space: An Bayesian Nash Equilibrium-Informed Agent-Based Model (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
  • Warr, O., Song, M., & Sherwood Lollar, B. (2023). The application of Monte Carlo modelling to quantify in situ hydrogen and associated element production in the deep subsurface. Frontiers in Earth Science, 11, 1150740.
  • Warrier, R., Boone, R., & Salerno, J. Migration land systems model: a theoretical agent-based model (Doctoral dissertation).
  • Watts, K. M., & Richardson, W. (2023). Inclusive pedagogy strategies to introduce high schoolers to systems biology. bioRxiv, 2023-03.
  • Watz, J., Schill, J., Addo, L., Piccolo, J. J., & Hajiesmaeili, M. (2023). Increased Temperature and Discharge Influence Overwinter Growth and Survival of Juvenile Salmonids in a Hydropeaking River: Simulating Effects of Climate Change Using Individual-Based Modelling. Fishes, 8(6), 323.
  • Weatherley, G., Araujo, R. P., Dando, S. J., & Jenner, A. L. (2023). Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis?. Bulletin of Mathematical Biology, 85(8), 75.
  • Weber, A. L., Ruesink, B., & Gronau, S. (2023). Dynamics of refugee settlements and energy provision: the case of forest stocks in Zambia. Journal of Economics and Development.
  • Wever, M., O'Leary, N., Shah, M., Wognum, N., & Onofrei, G. (2023). Towards a transdisciplinary framework for systemic risk detection. International Journal of Agile Systems and Management, 16(4), 458-483.
  • Will, M., Groeneveld, J., Lenel, F., Frank, K., & Müller, B. (2023). Determinants of Household Vulnerability in Networks with Formal Insurance and Informal Risk-Sharing. Ecological Economics, 212, 107921.
  • Williams, E. M., & Carley, K. M. (2023, September). Agent-Based Moral Interaction Simulations in Imbalanced Polarized Settings. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 139-148). Cham: Springer Nature Switzerland.
  • Wilson, K. M., Cole, K. E., & Codding, B. F. (2023). Identifying key socioecological factors influencing the expression of egalitarianism and inequality among foragers. Philosophical Transactions of the Royal Society B, 378(1883), 20220311.
  • Wrona, Z., Buchwald, W., Ganzha, M., Paprzycki, M., Leon, F., Noor, N., & Pal, C. V. (2023). Overview of Software Agent Platforms Available in 2023. Information, 14(6), 348.
  • Wu, J., Ohya, T., & Sekiguchi, T. (2023). Applications of agent-based modeling and simulation in organization management: a quarter-century review through bibliometric mapping (1998–2022). Computational and Mathematical Organization Theory, 1-31.
  • Xiang, W., Chen, L., Yan, X., Wang, B., & Liu, X. (2023). The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model. Cities, 135, 104238.
  • Xiangmei, W., Xiaoxiao, G., & Wang, Y. (2023). Research on the network topology characteristics of unsafe behavior propagation in coal mine group from the perspective of human factors. Resources Policy, 85, 104020.
  • Xu, B., & Lu, M. (2023). Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing. Applied Sciences, 13(6), 3703.
  • Xu, B., Lu, M., & Zhang, H. (2023). Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs. Sensors, 23(8), 3846.
  • Xu, G., Liu, X., Zhong, L., Ren, K., Lu, C., & Deng, L. (2023). Seat allocation optimization for railways considering social distancing during the post-pandemic period. Journal of Transport & Health, 33, 101691.
  • Xu, J., Wang, Y., Gomez, H., & Feng, X. Q. (2023). Biomechanical modelling of tumor growth with chemotherapeutic treatment: A review. Smart Materials and Structures.
  • Xu, X., Xu, L., & Wang, X. (2023). Study on coopetition relationship simulation among M-commerce information service subjects based on Lotka-Volterra model. Journal of Management Analytics, 1-24.
  • Xu, Y., & Wali, A. (2023). Handwritten Pattern Recognition using Birds-Flocking Inspired Data Augmentation Technique. IEEE Access.
  • Xue, F., Geng, P., Hao, H., He, Y., & Liang, H. (2023, November). A Multiple Fire Zones Detection Method for UAVs Based on Improved Ant Colony Algorithm. In International Artificial Intelligence Conference (pp. 194-208). Singapore: Springer Nature Singapore.
  • Yang, C., Yang, Z., & Li, Y. (2023). Negotiation mechanism of carbon emission quota trading process. Sustainable Production and Consumption, 39, 336-344.
  • Yang, D., Snelson, C., & Feng, S. (2023). Identifying computational thinking in students through project-based problem-solving activities. Information Discovery and Delivery, (ahead-of-print).
  • Yang, G., Cai, W., Hu, M., Li, C., & Pan, D. (2023, April). S tudy on the Influence of Exit Width Change on Heterogeneous Passengers Evacuation Based on the Social Force Model. In Bio-Inspired Computing: Theories and Applications: 17th International Conference, BIC-TA 2022, Wuhan, China, December 16–18, 2022, Revised Selected Papers (pp. 531-539). Singapore: Springer Nature Singapore.
  • Yang, J., Shiwakoti, N., & Tay, R. (2023). Exploring Melbourne Metro Train Passengers’ Pre-Boarding Behaviors and Perceptions. Sustainability, 15(15), 11564.
  • Yang, L., Han, J., Long, W., & Zhang, Y. (2023, June). Trust evaluation model for electric power mobile Internet environment based on graph and semantic time window. In Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023) (Vol. 12721, pp. 22-27). SPIE.
  • Yang, Y., & Fukuda, M. (2023). Agents Visualization and Web GUI Development in MASS Java. MS Capstone White Paper, University of Washington Bothell, Bothell, WA, 98011.
  • Yang, Y., Mao, X., Yang, S., & Wu, M. (2023). NorMASS: A normative MAS-based modeling approach for simulating incentive mechanisms of Q&A communities. Plos one, 18(2), e0281431.
  • Yao, Z., & Sun, C. (2023, March). Characteristic Analysis and Strategy Research on Synergetic Development of Construction Enterprises of Different Scales——Take Northwest China as an example. In Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December 9-11, 2022, Chongqing, China.
  • Yasik, Y. L. (2023). Dunia Maya (Virtual World) Berbasis Agent Based Modeling-ABM untuk Pemodelan Perilaku Konsumen. Prosiding FRIMA (Festival Riset Ilmiah Manajemen dan Akuntansi), (6), 558-571.
  • Yeğenoğlu, A., Romero, C. J., Martín, A. P., van der Vlag, M., Klijn, W., Hater, T., ... & Diaz-Pier, S. Gradient free optimization of neuroscience models at different scales with L2L. meta, 1, 2.
  • Yeni, S., Grgurina, N., Saeli, M., Hermans, F., Tolboom, J., & Barendsen, E. (2023). Interdisciplinary Integration of Computational Thinking in K-12 Education: A Systematic Review. Informatics in Education.
  • YESHITLA, H. D. (2023). Development of large scale Agent Based Modeling Simulator with Microservice architecture (Doctoral dissertation, 부경대학교).
  • Yimin, F. E. N. G., Chenchu, Z. H. O. U., Qiang, Z. O. U., Yusheng, L. I. U., Jiyuan, L. Y. U., & Xinfeng, W. U. (2023). A goal-based approach for modeling and simulation of different types of system-of-systems. Journal of Systems Engineering and Electronics, 34(3), 627-640.
  • Yin, C., Ji, W., Ye, T., Mao, T., & Zhang, K. (2023, December). Study on Self-Organizing Aggregation Method for Massive Flexible Resources and Its Simulation. In 2023 4th International Conference on Advanced Electrical and Energy Systems (AEES) (pp. 452-459). IEEE.
  • Yin, J., Wang, D., Li, H., Li, Y., & Shang, Y. Spatial Optimization of Rural Settlements in Ecologically Fragile Regions Based on a Multi-Agent Model: Evidence from Different Types of Towns. Available at SSRN 4650929.
  • Ying, Y., Antfolk, J., & Santtila, P. (2023). An Agent-Based Model of Sex and Sexual Orientation Differences in Short-Term Mating Behaviors as a Result of Mating Preferences. The Journal of Sex Research, 1-9.
  • Younas, M. I., Iqbal, M. J., Aziz, A., & Sodhro, A. H. (2023). Toward QoS Monitoring in IoT Edge Devices Driven Healthcare—A Systematic Literature Review. Sensors, 23(21), 8885.
  • Yu, C., & Wellman, H. M. (2023). Where do differences in theory of mind development come from? An agent-based model of social interaction and theory of mind. Frontiers in Developmental Psychology, 1.
  • Yu, H., Li, Y., & Wang, W. (2023). Optimal innovation strategies of automakers with market competition under the dual-credit policy. Energy, 128403.
  • Yu, S. (2023). Evaluating architectural layouts for occupancy patterns and interactions using agent-based modelling as a methodology for workplace design. Automation in Construction, 155, 105025.
  • Yu, S., & Hou, Z. (2023). Melodie: Agent-based Modeling in Python. Journal of Open Source Software, 8(83), 5100.
  • Yuan, C., Yan, S., Li, C., & Zhang, H. (2023, November). Multi-agent-based emergency supplies dispatch. In Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023) (Vol. 12923, pp. 163-170). SPIE.
  • Yuguo, J., Yu, H., Ampaw, E. M., Wang, C., & Jiang, P. (2023). Innovating for a greener world: Simulating low-carbon innovation in manufacturing companies from the lens of community succession. Journal of Cleaner Production, 140053.
  • Zebua, R. S. Y. (2023). Teknik Menjaring Literatur di Berbagai Literature Databases (Vol. 1). XREI Institute.
  • Zhang, A., Zhen, Q., Zheng, C., Li, J., Zheng, Y., Du, Y., ... & Zhang, Q. (2023). Assessing the impact of architectural and behavioral interventions for controlling indoor COVID-19 infection risk: An agent-based approach. Journal of Building Engineering, 106807.
  • Zhang, C., Wu, X., Zhao, S., Madani, H., Chen, J., & Chen, Y. (2023). A dynamical system model to analyze the low carbon transition in energy-economic system. Journal of Economy and Technology, 1, 1-15.
  • Zhang, E., Jiang, H., & Zhang, X. (2023, March). A slotted and OFDM federated protocol for safety message broadcasting in VANETs. In Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022) (Vol. 12586, pp. 64-68). SPIE.
  • Zhang, H., Zhu, P., & Yao, Z. (2023). An Agent-Based Model to Simulate the Diffusion of New Energy Vehicles. Complexity, 2023.
  • Zhang, J. (2023). How do trust and decentralization impact adoption?: an agent-based model for diffusion of blockchain-based COVID-19 contact tracing apps (Doctoral dissertation, University of British Columbia).
  • Zhang, J. (2023). Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique. Journal of Advanced Transportation, 2023.
  • Zhang, J., & Zhang, J. (2023). Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective. Discrete Dynamics in Nature and Society, 2023.
  • Zhang, J., Rong, L., & Gong, Y. (2023, November). Construction and Simulation of Major Infectious Disease Transmission Model Based on Individual-Place Interaction. In International Symposium on Knowledge and Systems Sciences (pp. 178-195). Singapore: Springer Nature Singapore.
  • Zhang, J., Rong, L., & Gong, Y. Check for updates Construction and Simulation of Major Infectious Disease Transmission Model Based on Individual-Place Interaction. In Knowledge and Systems Sciences: 22nd International Symposium, KSS 2023, Guangzhou, China, December 2–3, 2023, Proceedings (p. 178). Springer Nature.
  • Zhang, Q., Qiu, T., & Fu, Y. (2023, February). 3D Model-Based Design Method for Complex Cable Network Three-Dimensional Marking of Spacecraft. In Signal and Information Processing, Networking and Computers: Proceedings of the 10th International Conference on Signal and Information Processing, Networking and Computers (ICSINC) (pp. 866-872). Singapore: Springer Nature Singapore.
  • Zhang, W. H., Yuan, Q., & Cai, H. (2023). Unravelling urban governance challenges: Objective assessment and expert insights on livability in Longgang District, Shenzhen. Ecological Indicators, 155, 110989.
  • Zhang, Y., Li, S. H., & Liu, H. Y. (2023). Simulation Research on Conflicts of Matching Persons and Posts in High-risk Construction Based on Prospect Theory. Operations Research and Management Science, 31(12), 179.
  • Zhang, Z., & Wu, Z. (2023, April). A Sunlight Duration Time Driven Multi-objective Optimization Method for the Layout of High-Rise Residential Quarters Based on NSGA2 Algorithm. In Hybrid Intelligence: Proceedings of the 4th International Conference on Computational Design and Robotic Fabrication (CDRF 2022) (pp. 138-149). Singapore: Springer Nature Singapore.
  • Zhao, J., He, J., Liu, X., & Zhang, Y. (2023, August). Evaluation and optimization of data systems. In Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023) (Vol. 12784, pp. 839-847). SPIE.
  • Zheng, G., Zhang, Y., Peng, T., Guo, C., Guo, C., & Liu, X. (2023, April). Research on Distributed Internet of Things Trusted Operation Based on Dynamic Trusted Metrics. In 2023 8th International Conference on Computer and Communication Systems (ICCCS) (pp. 689-694). IEEE.
  • Zheng, W., Cao, Y., & Tan, H. (2023). Secure sharing of industrial IoT data based on distributed trust management and trusted execution environments: a federated learning approach. Neural Computing and Applications, 1-11.
  • Zheng, X. (2023). Complex behavior of individuals and collectives in a social system: An introduction to exploratory computational experimental methodology based on multi-agent modeling. Annals of Operations Research, 1-25.
  • Zhijian, W. (2023). Nash equilibrium selection by eigenvalue control. arXiv preprint arXiv:2302.09131.
  • Zhong, X., Yang, Y., Deng, F., & Liu, G. (2023). Rumor Propagation Control With Anti-Rumor Mechanism and Intermittent Control Strategies. IEEE Transactions on Computational Social Systems, 1-13.
  • Zhou, Y., Cao, J., & Zhang, Y. (2023). Research on Network Security Situation Awareness based on Netlogo.
  • Zhu, M., Yang, G., Jiang, Y., & Wang, X. (2023). Agent-Based Modeling for Water–Energy–Food Nexus and Its Application in Ningdong Energy and Chemical Base. Sustainability, 15(14), 11428.
  • Zhu, X., Huang, J., & Qi, C. (2023). Modeling and Analysis of Malware Propagation for IoT Heterogeneous Devices. IEEE Systems Journal.
  • Zixin, L. (2023). APPLICATION OF MULTI-AGENT SYSTEM IN URBAN RENEWAL DESIGN (Doctoral dissertation, University of Pécs).
  • Zixin, L., Várady, G., & Zagorácz, M. B. (2023). Multi-agent simulation of pedestrian activity in historic district. Pollack Periodica, 18(2), 137-143.
  • Zohar, A. R., & Levy, S. T. Teaching Molecules How to React: Middle School Students’ Learning through Computational Modeling of Chemical Reactions Using MMM Platform. Proceedings of the 18th Chais Conference for the Study of Innovation and Learning Technologies: Learning in the Digital Era, 18E-24E.
  • Zou, F., Jiang, H., Che, E., Wang, J., & Wu, X. (2023). Quantitative evaluation of emergency shelters in mountainous areas among multiple scenarios: Evidence from Biancheng, China. International Journal of Disaster Risk Reduction, 103665.
  • Zouhri, S., & El Baroudi, M. (2023). Mathematical formalism for agent-based model of proteins interaction inside cancer cell. Commun. Math. Biol. Neurosci., 2023, Article-ID.
  • Μπενίση, Α., Γκιόλμας, Α., Στούμπα, Α., Χαλκίδης, Ά., Μπόικος, Η., Ψωμά, Β., ... & Παπαναγιώτου, Α. Τ. (2023). Εξελιγμένη μορφή του μοντέλου της NetLοgo για τη φωτιά στο δάσος: Μία διδακτική προσέγγιση σε μαθητές Δημοτικού. 13o Πανελλήνιο Συνέδριο της Διδακτικής των Φυσικών Επιστημών και Νέων Τεχνολογιών στην Εκπαίδευση, 13.
  • Алексеева, Е. А. (2023). Проекты компьютерной эпистемологии. Философия науки и техники Philosophy of Science and Technology, 28(2), 88-101.
  • АНДРИАНОВА, Л., ПАВЛОВА, З., ХАКИМЬЯНОВ, М., & ХАЗИЕВА, Р. ИНФОРМАТИВНОЕ ПРЕДСТАВЛЕНИЕ ДИСЦИПЛИНЫ «МУЛЬТИАГЕНТНЫЕ СИСТЕМЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА» В ВОПРОСАХ И ОТВЕТАХ. Международный центр научного партнерства" Новая Наука" КОНФЕРЕНЦИЯ: ПРЕПОДАВАТЕЛЬ ГОДА 2023 Петрозаводск, 13 декабря 2023 года Организаторы: Международный центр научного партнерства" Новая Наука".
  • Антонова, Е. М (2023). Медицинская информатика: влияние времени, проблемы и возможные пути решения. ЦИТИСЭ, 3, 18-27.
  • Безбородова, О. Е. (2023). ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ ВЗАИМОДЕЙСТВИЯ ЧЕЛОВЕКА И ОБЪЕКТА ТЕХНОСФЕРЫ В ИНФОРМАЦИОННО-ИЗМЕРИТЕЛЬНЫХ И УПРАВЛЯЮЩИХ СИСТЕМАХ ОБЕСПЕЧЕНИЯ ЭКОЛОГИЧЕСКОГО БЛАГОПОЛУЧИЯ ЧЕЛОВЕКА. Модели, системы, сети в экономике, технике, природе и обществе, (1 (45)), 164-177.
  • Белолуцкая, А. К., Вачкова, С. Н., & Патаракин, Е. Д. Связь цифрового компонента обучения и развития детей дошкольного и школьного возраста: обзор исследований и международных образовательных практик, 18(2), 37-55.
  • Гнатчук, Є. Г., Засорнова, І. О., & Рей, К. С. (2023). СИСТЕМА ПІдТРИМКИ ПРИйНяТТя РІшЕНь ПРО МОЖлИВІСТь ВАКцИНАцІЇ ВІд COVID-19. ВЧЕНІ ЗАПИСКИ, 1202376.
  • Дудко, В. В., & Патаракин, Е. Д. (2023). Исследование научных школ университета средствами библиометрического картирования. Территория новых возможностей. Вестник Владивостокского государственного университета экономики и сервиса, 15(1 (65)), 150-167.
  • Каталевский, Д. (2023). Новые управленческие подходы для предотвращения краха сложных социальноэкономических систем. Форсайт, 17(3), 56-67.
  • Макареня, Т. А., Маннаа, А. С., Калиниченко, А. И., & Петренко, С. В. (2023). Когнитивное моделирование социально-экономических систем: ретроспективный анализ инструментов и информационных систем. Вестник ВГУ. Серия: Системный анализ и информационные технологии, (3), 84-94.
  • Тимофеев, Г. А. (2023). Поиск подходящей архитектуры для разработки цифрового двойника гибридных энергетических систем в изолированных от сетевых энергосистем средах с использованием ТРИЗ-эволюционного подхода. Вестник НГУ. Серия: Информационные технологии, 20(4), 76-99.
  • Шепель, А. С. ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ. ТЕОРИЯ И ПРАКТИКА (ИММОД-2023). Издательство АН РТ КОНФЕРЕНЦИЯ: ОДИННАДЦАТАЯ ВСЕРОССИЙСКАЯ НАУЧНО-ПРАКТИЧЕСКАЯ КОНФЕРЕНЦИЯ ПО ИМИТАЦИОННОМУ МОДЕЛИРОВАНИЮ И ЕГО ПРИМЕНЕНИЮ В НАУКЕ И ПРОМЫШЛЕННОСТИ «ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ. ТЕОРИЯ И ПРАКТИКА» ИММОД-2023 Казань, 18–20 октября 2023 года Организаторы: Казанский государственный энергетический университет.
  • נורית ברגר-גיל .(2023) למידת מושגים בכימיה באמצעות בניית מודלים חישוביים ומעוגני גוף בגישת מערכות מורכבות (Doctoral dissertation, University of Haifa, Israel).
  • 김민수, 이지환, & 김영진. (2023). 대규모 인구동태 시뮬레이션을 통한 에이전트 기반 고성능 시뮬레이터의 구현에 관한 연구. 한국전자거래학회지, 28(1), 109-121.
  • 우선희. (2023). 노인요양시설 화재위험평가를 통한 인명 안전성 확보 방안 연구 (Doctoral dissertation, 부경대학교).
  • 조성진, 최희정, 임종서, 이혜영, & 김선미. (2023). 해양공간계획 이행을 위한 행위자기반 시뮬레이션 활용방안 연구-정책 현안 및 기술 수요 분석을 중심으로. 해양정책연구, 38(1), 153-179.
  • 주럴러, & 이향숙. (2023). 경쟁우위 관점에서 본 전자상거래 플랫폼에서의 SCF 적용에 관한 연구. Journal of the Korean Society of Supply Chain Management ISSN, 23(1), 39-54.
  • 刘家霖, 张显玉, & 庞达. (2023). 基于达尔文演化动力学的适应性治疗策略在肿瘤治疗中的研究进展. 中国癌症杂志, 33(4), 397-402.
  • 刘捷, 王曈, 孙恒飞, & 沐波. (2023). 考虑驾驶员个性的博弈协商机制与出行路径选择研究. 交通运输研究, 9(1), 86.
  • 刘舫, 吕天, 刘心阁, 叶盛, 郭锐, 张烈, ... & 刘永进. (2023). Research on the Construction and Interactive Feedback of Online Exhibition Hall with the Introduction of Intelligent Virtual Agent. Journal of Software, 1-18.
  • 名倉卓弥, & 秋山英三. (2023). SNS におけるトピックス数の増加が意見の分極化とエコーチェンバーに与える影響. 人工知能学会論文誌, 38(4), B-N11_1.
  • 安井一真, 宇都宮陽一, & 奥田隆史. (2023). 足の速さによる有利不利を小さくする鬼ごっこルールの検証. 電気学会論文誌 C (電子・情報・システム部門誌), 143(12), 1145-1153.
  • 杨鹤林. (2023). 促进开放科学: 国外高校图书馆 2022 年 “爱数据周” 活动分析与启示. Journal of Academic Libraries, 41(3).
  • 森田裕之, 西口真央, 白井康之, & 後藤裕介. (2023). タクシープローブデータを用いたスカイタクシー実用化における空路設定問題に関する研究. 経済研究 The Journal of Economic Studies/大阪府立大学経済学研究科 編, 68(1-4), 1-36.
  • 櫻井祐子. (2023). 私のブックマーク: 人工知能とゲーム理論. 人工知能, 38(5), 750-756.
  • 沈颂, & 沈国峰. (2023). 一种应对 COVID-19 疫情的社会接触自动检测方法. Application Research of Computers/Jisuanji Yingyong Yanjiu, 40(4).
  • 浅野俊幸. (2023). シミュレーション教育のための NetLogo の利用. 湘南工科大学紀要, 57(1), 27-31.
  • 石云, 朱晓雯, 李建华, 马小燕, 赵娜, & 佘洁. (2023). 基于多智能体的黄土高原沟壑区农村居民点优化布局. 经济地理, 43(7), 170-178.
  • 薛领, 彭志斌, & 赵威. (2023). 空间集聚和知识溢出的微观机理与动态模拟. 复杂系统与复杂性科学, 20(1), 18-26.
  • 项凤涛, 苏炯铭, 谷学强, & 张万鹏 (2023). 基于智能体建模的新冠肺炎疫情传播问题研究. 智能科学与技术学报, 5(1), 51-57.
  • 黄秋怡, 郑小平, & 王瑞梅 (2023). 农村交通基础设施改善能够缓解农业要素错配吗?. 中国农业大学学报, 28(3), 279-292.
  • 齋藤美紀, 阿部健太, & 林久志. (2023). P2P 型企業間人材共有 PF におけるリソース・トークンの偏りの対処法: 銀行方式. 人工知能学会第二種研究会資料, 2023(SAI-048), 02.

2022

  • Fuchkina, E., Bielik, M., Schneider, S., Ossenberg-Engels, T., & Hämmerle, A. (2022). Space Matcher-An interactive toolbox for assisting in spatializing & testing office programmes using graph centralities.
  • Mo, J., & Polly, P. D. The role of dispersal, selection intensity, and extirpation risk in resilience to climate change: A trait‐based modelling approach. Global Ecology and Biogeography.
  • Sanz, V., & Urquia, A. (2022). Combining PDEVS and Modelica for describing agent-based models. SIMULATION, 00375497221094873.
  • Vermeer, W.H., Smith, J.D., Wilensky, U. et al. High-Fidelity Agent-Based Modeling to Support Prevention Decision-Making: an Open Science Approach. Prev Sci 23, 832–843 (2022). https://doi.org/10.1007/s11121-021-01319-3
  • 丁伟, 明振军, 王国新, & 阎艳. (2022). 基于多层次 LSTM 网络的多智能体攻防效能动态预测模型. 兵工学报, 0.
  • Abhishek, B., & Hirve, S. (2022). Overview of Social Network Analysis and Different Graph File Formats. Social Network Analysis: Theory and Applications, 1-18.
  • Abrahamson, D. (2022). Enactive perception as mathematics learning. In M.-C. Shanahan, B. Kim, M. A. Takeuchi, K. Koh, A. P. Preciado-Babb, & P. Sengupta (Eds.), The Learning Sciences in conversation: Theories, methodologies, and boundary spaces (pp. 153–170). Routledge.
  • Abrahamson, D., & Mechsner, F. (2022). Toward synergizing educational research and movement sciences: A dialogue on learning as developing perception for action. Educational Psychology Review. https://doi.org/0.1007/s10648-022-09668-3
  • Abrahamson, D., Dutton, E., & Bakker, A. (2022).Towards an enactivist mathematics pedagogy. In S. A. Stolz (Ed.), The body, embodiment, and education: An interdisciplinary approach (pp. 156–182). Routledge.
  • Accolla, C., Schmolke, A., Jacobson, A., Roy, C., Forbes, V. E., Brain, R., & Galic, N. TRACE Document. Ecology and Evolution, 25, 479-486.
  • Adam, C., & Arduin, H. (2022, May). Finding and explaining optimal screening strategies with limited tests during the COVID-19 epidemics. In 19th International Conference on Information Systems for Crisis Response and Management ISCRAM.
  • Adebayo, S. A., Sathasiva, S., & Ali, M. K. M. (2022). HornSAT Solver Using Agent-Based Modelling in Hopfield Network. In Intelligent Systems Modeling and Simulation II (pp. 251-263). Springer, Cham.
  • Adedigba, A. A. (2022). Towards integrated sustainable solid waste management in Nigerian cities. [Doctoral Thesis, Northumbria University].
  • Adeolu, A. (2022). Learning Computational Thinking Practices Through Agent-Based Modeling in an Informal Setting. Journal of Research in Science Mathematics and Technology Education, 17-39.
  • Agyemang, F. S., Silva, E., & Fox, S. (2022). Modelling and simulating ‘informal urbanization’: An integrated agent-based and cellular automata model of urban residential growth in Ghana. Environment and Planning B: Urban Analytics and City Science, 23998083211068843.
  • Aitken, S. (2022). An exploration of local rules to map spawning processes to regular hardware architectures (Doctoral dissertation, University of York).
  • Akhatova, A., Kranzl, L., Schipfer, F., & Heendeniya, C. B. (2022). Agent-Based Modelling of Urban District Energy System Decarbonisation—A Systematic Literature Review. Energies 2022, 15, 554.
  • Akhtar, S. M., Nazir, M., Saleem, K., Ahmad, R. Z., & Javed, A. R. (2022). S. Band S and Mosavi A (2022) A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems. Front. Public Health, 10, 849185.
  • Aktas, M., & Wolf, S. M. (2022). Diagnostische Fragen zur Zwei-und Mehrsprachigkeit bei Kindern mit kognitiven Beeinträchtigungen. Zwei-und Mehrsprachigkeit bei Kindern mit kognitiven Beeinträchtigungen, 27.
  • Al Ghamdi, M. A. (2022). A Novel Approach to Printed Arabic Optical Character Recognition. Arabian Journal for Science and Engineering, 47(2), 2219-2237.
  • Al-Janabi, S., Alkaim, A., & Rahem, A. (2022). An alternative technique to reduce time, cost and human effort during natural or manufactured disasters. IAES International Journal of Robotics and Automation, 11(1), 10.
  • Al-Shaery, A. M., Hejase, B., Tridane, A., Farooqi, N. S., & Al Jassmi, H. (2022). Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities. Scientific Reports, 12(1), 1-9.
  • Alajlan, A. (2022). Predicting Human Movement in Crowds (Doctoral dissertation, University of Idaho).
  • Alam, A., & Khurshid, F. (2022). Teachers’ Knowledge of ICT and e-learning in Pakistan: The wave of e-learning during COVID-19. International Research Journal of Education and Innovation, 3(1), 34-46.
  • Alami, K. COVID-19 IMPACT ON TUCSON FIRE DEPARTMENT RESOURCES (Doctoral dissertation, UNIVERSITY OF ARIZONA).
  • Alencar de Paula, R. (2022). Contributions to Continuous Improvement Management Using its Variables to Measure Productivity, Employee Engagement and Participatory Management (Doctoral dissertation, Polytechnique Montréal).
  • Alexander, S., & Block, P. (2022). Integration of seasonal precipitation forecast information into local-level agricultural decision-making using an agent-based model to support community adaptation. Climate Risk Management, 100417.
  • Alhady, S. S. N. (2022). Simulating Solitary Foraging Behaviour of Chimpanzee in Hunting Red Colobus Monkeys Using Agent-Based Modelling Approach. Intelligent Manufacturing and Mechatronics: Proceedings of SympoSIMM 2021, 387.
  • Ali Kumar, D. S. N. K. P., Shah Newaz, S. H., Rahman, F. H., Lee, G. M., Karmakar, G., & Au, T. W. Green Demand Aware Fog Computing: A Prediction-based Dynamic Resource Provisioning Approach. Electronics.
  • Ali, A., & Farooqui, M. F. (2022, May). Interaction among Multiple Intelligent Agent Systems in web mining. In 2022 3rd International Conference for Emerging Technology (INCET) (pp. 1-8). IEEE.
  • Alkhatib, A. A., Abu Maria, K., Alzu'bi, S., & Abu Maria, E. (2022). Novel system for road traffic optimisation in large cities. IET Smart Cities.
  • Alkhatib, A. A., Abu Maria, K., Alzu'bi, S., & Abu Maria, E. (2022). Novel system for road traffic optimisation in large cities. IET Smart Cities.
  • Alkhatib, A. A., Maria, K. A., AlZu'bi, S., & Maria, E. A. (2022). Smart Traffic Scheduling for Crowded Cities Road Networks. Egyptian Informatics Journal.
  • Alkhatib, A. A., Maria, K. A., AlZu'bi, S., & Maria, E. A. (2022). Smart Traffic Scheduling for Crowded Cities Road Networks. Egyptian Informatics Journal.
  • Allahmoradi, E., Mirzamohammadi, S., Bonyadi Naeini, A., Maleki, A., Mobayen, S., & Skruch, P. (2022). Policy Instruments for the Improvement of Customers’ Willingness to Purchase Electric Vehicles: A Case Study in Iran. Energies, 15(12), 4269.
  • Almaguer-Sustegui, D. S., Islas-Moreno, C., Padilla-Longoria, P., Prado-Zäyago, M. A., & Vizuet-Morales, D. F. (2022). Manejo biológico de una plaga usando un modelo multiagentes. Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI, 10(Especial), 140-146.
  • Almeida, F. P. S. (2022). Predictive long-term asset maintenance strategy: development of a fuzzy logic condition-based control system (Doctoral dissertation).
  • AlMuhaideb, S., Touir, A., Alshraihi, R., Altwaijry, N., & Qasem, S. (2022). Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem. Applied Sciences, 12(7), 3630.
  • Alobeidyeen, A., & Du, L. Information Dissemination Dynamics Through Vehicle-to-Vehicle Communication Built Upon Traffic Flow Dynamics Over Roadway Networks. Available at SSRN 4077909.
  • Alsammak, I. L. H., Mahmoud, M. A., Aris, H., AlKilabi, M., & Mahdi, M. N. (2022). The Use of Swarms of Unmanned Aerial Vehicles in Mitigating Area Coverage Challenges of Forest-Fire-Extinguishing Activities: A Systematic Literature Review. Forests, 13(5), 811.
  • Altamimi, M. (2022). Big Data in E-government: Classification and Prediction using Machine Learning Algorithms. Iraqi Journal of Intelligent Computing and Informatics (IJICI), 1(2), 41-55.
  • Alvarado, V., Hsu, S. C., Wu, Z., Zhuang, H., Lee, P. H., & Guest, J. S. (2022). Roadmap from Microbial Communities to Individuality Modeling for Anaerobic Digestion of Sewage Sludge. Environmental Science & Technology.
  • Álvarez Arce, J. R. (2022). Modelización basada en el individuo de sistemas de tratamiento biológico en biopilas de suelos contaminados por hidrocarburos de petróleo (Bachelor's thesis, Quito: UCE).
  • Alves, F., Rocha, A. M. A., Pereira, A. I., & Leitão, P. Conceptual Multi-Agent System Design for Distributed Scheduling Systems. In Smart and Sustainable Manufacturing Systems for Industry 4.0 (pp. 129-148). CRC Press.
  • Alzaeemi, S. A., Sathasivam, S., Velavan, M., & Mamat, M. Agent-based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network. International Journal of Engineering and Advanced Technology (IJEAT), 9(4), 1872-1879.
  • Amadae, S. M., & Watts, C. J. (2022). Red Queen and Red King Effects in cultural agent-based modeling: Hawk Dove Binary and Systemic Discrimination. The Journal of Mathematical Sociology, 1-28.
  • Ambrosius, F. H., Kramer, M. R., Spiegel, A., Bokkers, E. A., Bock, B. B., & Hofstede, G. J. (2022). Diffusion of organic farming among Dutch pig farmers: An agent-based model. Agricultural Systems, 197, 103336.
  • Amparore, E. G. (2022). Stochastic modelling and evaluation using GreatSPN. ACM SIGMETRICS Performance Evaluation Review, 49(4), 87-91.
  • Ampatzidis, G., & Armeni, A. (2022). Designing a learning environment to teach about COVID-19/Σχεδιασμός ενός μαθησιακού περιβάλλοντος διδασκαλίας για τη νόσο COVID-19. Διεθνές Συνέδριο για την Ανοικτή & εξ Αποστάσεως Εκπαίδευση, 11(8Β), 169-175.
  • Anceaume, E., Djari, A., & Tucci-Piergiovanni, S. (2022). An agent-based simulation study of Sycomore++, a scalable and self-adapting graph-based permissionless distributed ledger.
  • Andringa, S. P., & Yorke-Smith, N. (2022). Flexible Enterprise Optimization with Constraint Programming. In Enterprise Engineering Working Conference (pp. 58-73). Springer, Cham
  • Angione, C., Silverman, E., & Yaneske, E. (2022). Using machine learning as a surrogate model for agent-based simulations. PLOS ONE, 17(2), e0263150.
  • Angourakis, A., Bates, J., Baudouin, J. P., Giesche, A., Walker, J. R., Ustunkaya, M. C., ... & Petrie, C. A. (2022). Weather, land and crops in the Indus Village model: A simulation framework for crop dynamics under environmental variability and climate change in the Indus Civilisation. Quaternary, 5(2), 25.
  • Antelmi, A., Cordasco, G., D’Ambrosio, G., De Vinco, D., & Spagnuolo, C. (2023). Experimenting with Agent-Based Model Simulation Tools. Applied Sciences, 13(1), 13.
  • Anubhuti, Kaur, H. (2023). Role of Multi-agent Systems in Health Care: A Review. Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-19-4052-1_37
  • Arbelaez-Velasquez, C. A., Giraldo, D., & Quintero, S. (2022). Analysis of a Teleworking Technology Adoption Case: An Agent-Based Model. Sustainability, 14(16), 9930.
  • Arima, Y. (2022). Psychology of Group and Collective Intelligence. Springer Nature.
  • Asgarpour, S., Hartmann, A., Augustijn, E. W. P., & Dorée, A. (2022). The Other Side of the Interdependency Coin: Identifying Coordination and Investment Opportunities for Infrastructure Systems. Journal of Infrastructure Systems, 28(2), 04022011.
  • Asif, M. J. (2022). Zakat Charity and Wealth Distribution: An Agent Based Computational Model. International Journal of Zakat, 7(1), 63-74.
  • Assaraf, O. B. Z., & Knippels, M. C. P. (2022). Lessons Learned: Synthesizing Approaches That Foster Understanding of Complex Biological Phenomena. Fostering Understanding of Complex Systems in Biology Education: Pedagogies, Guidelines and Insights from Classroom-based Research, 249.
  • Avil, M. G., Taghipourian, M. J., Farrokhseresht, B., & Aghajani, H. (2022). Analysis of Rumor Management in the Context of Social Networks with a Meta-Combined Method from the Perspective of Islamic Concepts. Health, 5(4), 173-191.
  • Avila-Garzon, C., Balaguera, M., & Tabares-Morales, V. (2022). An Agent-Based Social Simulation for Citizenship Competences and Conflict Resolution Styles. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-23.
  • Axelrod, D. E. (2022). Chronotherapy of Early Colon Cancer: Advantage of Morning Dose Schedules. Cancer Informatics, 21, 11769351211067697.
  • AYBUĞA, K., & IŞILDAR, A. G. Y. (2022). Agent-Based Approach on Water Resources Management: A Modified Systematic Review. Turkish Journal of Water Science and Management, 6(2), 202-236.
  • Azadi, F. (2022). Comprehensive Arterial Traffic Control for Fully Automated and Connected Vehicles (Doctoral dissertation, University of Pittsburgh).
  • Azadi, F., Mitrovic, N., & Stevanovic, A. Z. (2022). Combined flexible lane assignment and reservation-based intersection control in field-like traffic conditions. Transportmetrica A: Transport Science, 1-36.
  • Azarov, I., Helmlinger, G., Kosinsky, Y., & Peskov, K. (2022). Elaborating on anti CTLA-4 mechanisms of action using an agent-based modeling approach. Frontiers in Applied Mathematics and Statistics, 8, 993581.
  • Azizi, K. (2022). Application of Local Knowledge for Better Characterization and Modeling of Urban Pluvial Flooding (Doctoral dissertation, The University of Memphis).
  • Backhaus, A. E., Lister, A., Tomkins, M., Adamski, N. M., Simmonds, J., Macaulay, I., ... & Uauy, C. (2022). High expression of the MADS-box gene VRT2 increases the number of rudimentary basal spikelets in wheat. Plant Physiology.
  • Baden-Böhm, F., Thiele, J., & Dauber, J. (2022). Response of honeybee colony size to flower strips in agricultural landscapes depends on areal proportion, spatial distribution and plant composition. Basic and Applied Ecology.
  • Bahrami, N., Sadr, S. M. K., Afshar, A., & Afshar, M. H. (2022). Application of Agent Based Models as a Powerful Tool in the Field of Water Resources Management. In Computational Intelligence for Water and Environmental Sciences (pp. 491-506). Springer, Singapore.
  • Baktash, A., Huang, A., de la Mora Velasco, E., Jahromi, M. F., & Bahja, F. (2022). Agent-based modelling for tourism research. Current Issues in Tourism, 1-13.
  • Bamaqa, A., Sedky, M., Bosakowski, T., Bastaki, B. B., & Alshammari, N. O. (2022). SIMCD: SIMulated Crowd Data for Anomaly Detection and Prediction. Expert Systems with Applications, 117475.
  • Barbierato, L., Rando Mazzarino, P., Montarolo, M., Macii, A., Patti, E., & Bottaccioli, L. (2022). A comparison study of co-simulation frameworks for multi-energy systems: the scalability problem. Energy Informatics, 5(4), 1-26.
  • Barelli, E., & Levrini, O. (2022). Computational simulations at the interface of physics and society: A teaching-learning module for high-school students. Il nuovo cimento C, 45(6), 1-4.
  • Barnes, B., Dunn, S., & Wilkinson, S. (2022). Replicating capacity and congestion in microscale agent-based simulations. Travel Behaviour and Society, 29, 308-318.
  • Barnes, E. M. (2022). Realistic evacuation simulation through micro and macro scale agent-based modelling including demographics, agent patience and evacuation route capacities (Doctoral dissertation, Newcastle University).
  • Barnett-Neefs C, Sullivan G, Zoellner C, Wiedmann M, Ivanek R (2022). Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses. PLOS ONE 17(3): e0265251. https://doi.org/10.1371/journal.pone.0265251
  • Barnett-Neefs, C., Wiedmann, M., & Ivanek, R. (2022). Examining Patterns of Persistent Listeria Contamination in Packinghouses Using Agent-Based Models. Journal of Food Protection, 85(12), 1824–1841. https://doi.org/10.4315/JFP-22-119
  • Barnett, T., Valdez-Tullett, J., & Bjerketvedt, L. M. (2022). Close encounters: visibility and accessibility of Atlantic rock art in Scotland. Abstractions Based on Circles: Papers on prehistoric rock art presented to Stan Beckensall on his 90th birthday, 63.
  • Barrón-Estrada, M. L., Zatarain-Cabada, R., Romero-Polo, J. A., & Monroy, J. N. (2022). Patrony: A mobile application for pattern recognition learning. Education and Information Technologies, 27(1), 1237-1260.
  • Basha, S. M., Neto, A. V. L., Alshathri, S., Abd Elaziz, M., Mohisin, S. H., & De Albuquerque, V. H. C. (2022). Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia. Computational Intelligence and Neuroscience, 2022.
  • Bayley, T. (2022). A Quantitative Exploration of the Mechanisms Relating Obesity, Depression and Socioeconomic Position (Doctoral dissertation, University of Sheffield).
  • Bearss, E. M., Cantrell, W. A., Hall, C. W., Pinckard, J. E., & Petty, M. D. (2022, April). Wolf sheep predation: reimplementing a predator-prey ecosystem model as an instructional exercise in agent-based modeling. In Proceedings of the 2022 ACM Southeast Conference (pp. 38-43).
  • Beernink, S., Bloemendal, M., Kleinlugtenbelt, R., & Hartog, N. (2022). Maximizing the use of aquifer thermal energy storage systems in urban areas: effects on individual system primary energy use and overall GHG emissions. Applied Energy, 311, 118587.
  • Bekker, R. A., Kim, S., Pilon-Thomas, S., & Enderling, H. (2022). Mathematical modeling of radiotherapy and its impact on tumor interactions with the immune system. Neoplasia, 28, 100796.
  • Belda, A., Giancola, E., Williams, K., Dabirian, S., Jradi, M., Volpe, R., ... & Eicker, U. (2022). Reviewing challenges and limitations of energy modelling software in the assessment of PEDs using case studies. In Sustainability in Energy and Buildings 2021 (pp. 465-477). Springer, Singapore.
  • Bellaj, B., Ouaddah, A., Bertin, E., Crespi, N., Mezrioui, A., & Bellaj, K. (2022). BTrust: A New Blockchain-Based Trust Management Protocol for Resource Sharing. Journal of Network and Systems Management, 30(4), 1-31.
  • Ben Zvi Assaraf, O., & Knippels, M. C. P. (2022). Lessons Learned: Synthesizing Approaches That Foster Understanding of Complex Biological Phenomena. In Fostering Understanding of Complex Systems in Biology Education (pp. 249-278). Springer, Cham.
  • Benally, J., Palatnik, A., Ryokai, K., & Abrahamson, D. (2022). Learning through negotiating conceptually generative perspectival complementarities: The case of geometry. For the Learning of Mathematics, 42(3), 34–41.
  • Benitez Castillo, J. P. (2022). Simulación basada en el individuo utilizando NetLogo de un sistema en serie de reactores CSTR de lodos activados (Bachelor's thesis, Quito: UCE).
  • Benmir, M., Chabbar, S., Aboulaich, R., & Ismaili, N. (2022, October). A Hybrid Model of Tumor Growth Under Chemotherapy Medicine. In Colloque Africain sur la Recherche en Informatique et en Mathématiques Appliquées (CARI).
  • Bennai, M. T. (2022). Development of a self-adaptive multi-agent system for medical image processing (Doctoral dissertation, Université M'hamad Bougara: Faculté des Sciences).
  • Bennett, M. R. (2022). Climate Change Is Normal. In Our Dynamic Earth: A Primer (pp. 51-82). Springer, Cham.
  • Berceanu, C., & Patrascu, M. (2022). Initial Conditions Sensitivity Analysis of a Two-Species Butterfly-Effect Agent-Based Model. In European Conference on Multi-Agent Systems (pp. 60-78). Springer, Cham.
  • Berea, A., Liu, R., & Santiago, M. F. (2022). Universal constraints to life derived from artificial agents and games. In New Frontiers in Astrobiology (pp. 305-317). Elsevier.
  • Bernard, J. (2022). Building the Bio-CS Bridge: Expanding High School Curriculum that Integrates Biology and Computer Science (Doctoral dissertation, Worcester Polytechnic Institute).
  • Bhat, V., AL, S., Muraly, A., Rao, P., & Chatterjee, J. (2022). An agent-based model to examine neuroinvasion by SARS-CoV-2.
  • Björklöf, C. (2022). Investigating the collective behaviour of the stock market using Agent-Based Modelling.
  • Boavida-Portugal, I. (2022). Future land use/cover change and tourism development: integrating land use policy and tourist decision behaviour. In Mapping and Forecasting Land Use (pp. 243-264). Elsevier.
  • Bogdanowski, A., Frank, K., Banitz, T., Muhsal, L. K., & Kost, C. (2022). McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems. PLOS Computational Biology, 18(1), e1009777.
  • Boogers, S., & Daems, D. (2022). SAGAscape: Simulating Resource Exploitation Strategies in Iron Age to Hellenistic Communities in Southwest Anatolia. Journal of Computer Applications in Archaeology 5(1), 169-187. https://doi.org/10.5334/jcaa.90
  • Borgonovo, E., Pangallo, M., Rivkin, J., Rizzo, L., & Siggelkow, N. (2022). Sensitivity analysis of agent-based models: a new protocol. Computational and Mathematical Organization Theory, 1-43.
  • Bórquez-Paredes, D., Beghelli, A., Leiva, A., Jara, N., Lozada, A., Morales, P., ... & Olivares, R. (2022). Agent-based distributed protocol for resource discovery and allocation of virtual networks over elastic optical networks. Journal of Optical Communications and Networking, 14(8), 667-679.
  • Bouabdallah, I., & Mellah, H. (2022). Handling Trust in A Cloud Based Multi Agent System. arXiv preprint arXiv:2201.01807.
  • Bourceret, A., Amblard, L., & Mathias, J. D. (2022). Adapting the governance of social–ecological systems to behavioural dynamics: An agent-based model for water quality management using the theory of planned behaviour. Ecological Economics, 194, 107338.
  • Brady, C., Jen, T., Vogelstein, L., & Dim, E. (2022, June). Designing with Feeling: How Students Constructed Embodied Participatory Simulations for Groups of Younger Learners to Understand and Care About Sustainability in Ecosystems. In Interaction Design and Children (pp. 315-326).
  • Breitwieser, L., Hesam, A., De Montigny, J., Vavourakis, V., Iosif, A., Jennings, J., ... & Bauer, R. (2022). BioDynaMo: A modular platform for high-performance agent-based simulation. Bioinformatics, 38(2), 453-460.
  • Brinkmann, T., Steinfeldt, M., Arndt, C., Carstens, A., & Spuziak-Salzenberg, D. (2022). High-quality recycling through self-learning and resilient recycling networks using a combination of agent-based modelling and life cycle assessment. In E3S Web of Conferences (Vol. 349, p. 12004). EDP Sciences.
  • Brito, A. M. (2022). Una visión de complejidad a los sistemas urba-nos. La modelación basada en agentes (ABM) para la recreación de escenarios urbanos y sus posibilidades. Diseño y complejidad, 215.
  • Brocardo, J., Vale, I., & Menezes, L. (2022). A investigação em resolução de problemas, raciocínio, comunicação e modelação: Uma análise de 30 anos de publicações na revista Quadrante. Quadrante, 31(2), 63-93.
  • Bron, M. (2022). Understanding the influence of local government strategies targeting creative industries in Bandung, Indonesia, using Agent-Based Modelling (Master's thesis).
  • BULUMULLA, C. B. (2022). Integrating social network diffusion into BDI-based simulations: application focus on large-scale evacuations (Doctoral dissertation, RMIT University).
  • Bulumulla, C., Singh, D., Padgham, L., & Chan, J. (2022). Multi-level simulation of the physical, cognitive and social. Computers, Environment and Urban Systems, 93, 101756.
  • Bunin, S., Celestin, W., Hornback, A., & Rugaber, S. (2022, June). Incorporating Habitats in Conceptual Models and Agent-Based Simulations: Expanding the Virtual Ecological Research Assistant (VERA). In Proceedings of the Ninth ACM Conference on Learning@ Scale (pp. 472-474).
  • Butner, J. D., Dogra, P., Chung, C., Pasqualini, R., Arap, W., Lowengrub, J., ... & Wang, Z. (2022). Mathematical modeling of cancer immunotherapy for personalized clinical translation. Nature Computational Science, 2(12), 785-796.
  • Byer, N. W., & Reid, B. N. (2022). The emergence of imperfect philopatry and fidelity in spatially and temporally heterogeneous environments. Ecological Modelling, 468, 109968.
  • Calabrò, G., Le Pira, M., Giuffrida, N., Fazio, M., Inturri, G., & Ignaccolo, M. (2022). Modelling the dynamics of fragmented vs. consolidated last-mile e-commerce deliveries via an agent-based model. Transportation Research Procedia, 62, 155-162.
  • Calabrò, G., Le Pira, M., Giuffrida, N., Inturri, G., Ignaccolo, M., & Correia, G. H. D. A. (2022). Fixed-Route vs. Demand-Responsive Transport Feeder Services: An Exploratory Study Using an Agent-Based Model. Journal of Advanced Transportation, 2022.
  • Canals, C., Maroulis, S., Canessa, E., Chaigneau, S., & Mizala, A. (2022). Mechanisms Underlying Choice-Set Formation: The Case of School Choice in Chile. Social Science Computer Review, 08944393221088659.
  • Canessa, E., Chaigneau, S. E., & Moreno, S. (2022). Using agreement probability to study differences in types of concepts and conceptualizers. Behavior Research Methods, 1-20.
  • Cantin, G., Silva, C. J., & Banos, A. (2022). Mathematical analysis of a hybrid model: Impacts of individual behaviors on the spreading of an epidemic. Networks & Heterogeneous Media.
  • Carley, L. R. (2022, September). OSIRIS: Organization Simulation in Response to Intrusion Strategies. In Social, Cultural, and Behavioral Modeling: 15th International Conference, SBP-BRiMS 2022, Pittsburgh, PA, USA, September 20–23, 2022, Proceedings (Vol. 13558, p. 134). Springer Nature.
  • Carolina, N. (2022). Algoritma Path Planning Terkoordinasi Untuk Multi Robot Smart Warehouse (Doctoral dissertation, Universitas Pertamina).
  • Carson, D. B., & Carson, D. A. (2022). Understanding the demographic future of small Arctic villages using agent-based modelling. More than'Nature': Research on Infrastructure and Settlements in the North, 3, 263.
  • Carvajal León, B. F. (2022). Modelización basada en el individuo, de un reactor CSTR con recirculación de lodos activados (Bachelor's thesis, Quito: UCE).
  • Carvalho, I., Bernardi, F. A., Neiva, M. B., Lima, V. C., de Oliveira, L. L., Miyoshi, N. S. B., ... & Alves, D. (2022). COVID-19 BR: A web portal for COVID-19 information in Brazil. Procedia computer science, 196, 525-532.
  • Casadei, R., Fortino, G., Pianini, D., Placuzzi, A., Savaglio, C., & Viroli, M. (2022). A Methodology and Simulation-based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge. IEEE Internet of Things Journal.
  • Castelli, R. P (2022). ANALISI COMPARATIVA DI SIMULAZIONI PHET DI SISTEMI MECCANICI CLASSICI E SIMULAZIONI NETLOGO DI SISTEMI COMPLESSI (Doctoral dissertation)
  • Castro-Ríos, G. A., & Noguera-Hidalgo, Á. L. (2022). ¿ Los seguidores eligen a sus líderes? Explicación desde la simulación basada en agentes. Revista Venezolana de Gerencia (RVG), 27(100), 1594-1612.
  • Cayaban, C. J., Tacardon, E., Sario, M. C., & Intal, G. L. System Analysis and Design of Company XYZ’s RFQ/RFP Processes.
  • Cegielski, W. H. Agent-Based Modeling for Archaeology: Simulating the Complexity of Societies. IZA ROMANOWSKA, COLIN D. WREN, and STEFANI A. CRABTREE. 2021. Santa Fe Institute Press, Santa Fe, New Mexico. xiii+ 429 pp. 0.00 (PDF), ISBN 978-1-947864-25-2. American Antiquity, 1-2.
  • Čeh, I., Štorga, M., & Delač, G. (2022). Agent-Based Modelling: Parallel and Distributed Simulation of Product Development Team. Tehnički vjesnik, 29(4), 1424-1432.
  • Chappin, E. J., Schleich, J., Guetlein, M. C., Faure, C., & Bouwmans, I. (2022). Linking of a multi-country discrete choice experiment and an agent-based model to simulate the diffusion of smart thermostats. Technological Forecasting and Social Change, 180, 121682.
  • Chatalova, L. (2022). Resource Sufficiency in a Sustainable Bioeconomy: A Predator–Prey Perspective. In Bioeconomy and Sustainability (pp. 209-224). Springer, Cham.
  • Chatha, K. A., & Jalil, M. N. (2022). Complexity in Three-Echelon Supply Chain Network and Manufacturing Firm’s Operational Performance. Computers & Industrial Engineering, 108196.
  • Chemweno, P., Sullivan, B. P., Bermperidis, G., & Thiede, S. (2022). Exploring the Added-Value of Integrating Real-Time Location Systems for Tracking Critical Maintenance Tools. Procedia CIRP, 107, 902-907.
  • Chen, B., & Poquet, O. (2022). Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect. Journal of Learning Analytics, 9(1), 1-12.
  • Chen, C., Mostafizi, A., Wang, H., Cox, D., & Chand, C. (2022). An integrative agent‐based vertical evacuation risk assessment model for near‐field tsunami hazards. Risk Analysis.
  • Chen, D. (2022, July). Exploring Online Physician–Patient Interactions Through Information Sharing with Agent-Based Modeling. In International Conference on Logistics, Informatics and Service Sciences (pp. 59-73). Singapore: Springer Nature Singapore.
  • Chen, H., Chen, C., Li, H., Zhang, J., & Yang, Z. (2022). A Simulation Study on the Processes of Intra-Group Informal Interaction Affecting Workers’ Safety Behaviors. International journal of environmental research and public health, 19(16), 10048.
  • Chen, J., Zhang, X., Peng, X., Xu, D., & Peng, J. (2022). Efficient routing for multi-AGV based on optimized Ant-agent. Computers & Industrial Engineering, 108042.
  • Chen, J., Zhao, L., Xiao, F., Horn, M. & Wilensky, U. (2022). Self-Governed Collaborative Inquiry in Action: A Case Study of a Large-Scale Online Youth Community. Paper accepted to CSCL 2022. Hiroshima, Japan: International Society of the Learning Sciences.
  • Chen, L., Chen, Z., Lin, L., Ye, Q., Guo, S., & Lin, J. (2022). Augmenting deep land use prediction with randomized simulation. Computer Animation and Virtual Worlds, 33(3-4), e2071.
  • Chen, W., Ding, Y., Zhang, Y., Tian, Z., & Wei, S. (2022). Risk Assessment and Prevention Strategy of Virus Infection in the Context of University Resumption. Buildings, 12(6), 806.
  • Chen, Y., Xu, L., Zhang, X., Wang, Z., Li, H., Yang, Y., ... & Li, D. (2023). Socio-econ-ecosystem multipurpose simulator (SEEMS): An easy-to-apply agent-based model for simulating small-scale coupled human and nature systems in biological conservation hotspots. Ecological Modelling, 476, 110232.
  • Chersoni, G., DellaValle, N., & Fontana, M. (2022). Modelling thermal insulation investment choice in the EU via a behaviourally informed agent-based model. Energy Policy, 163, 112823.
  • Chetcuti, J., Kunin, W. E., & Bullock, J. M. (2022). Species' movement influence responses to habitat fragmentation. Diversity and Distributions.
  • Chettry, V., & Manisha, K. (2022). Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India. In Modeling, Simulation and Optimization (pp. 387-397). Springer, Singapore.
  • Chichorro, F., Correia, L., & Cardoso, P. (2022). Biological traits interact with human threats to drive extinctions: A modelling study. Ecological Informatics, 101604.
  • Childers, G., Linsky, C. L., Payne, B., Byers, J., & Baker, D. (2022). K-12 Educators’ Self-Confidence in Designing and Implementing Cybersecurity Lessons. Computers and Education Open, 100119.
  • Chopra, A. (2022). Decision Making for Populations (Doctoral dissertation, Massachusetts Institute of Technology).
  • Christensen, C., & Salmon, J. (2022). An agent-based modeling approach for simulating the impact of small unmanned aircraft systems on future battlefields. The Journal of Defense Modeling and Simulation, 19(3), 481-500.
  • Civico, M. (2022). Complexity in language matters Concept and uses of agent-based modelling. Advances in Interdisciplinary Language Policy, 9, 381.
  • Clark, R., & Kimbrough, S. O. (2022). On Modeling Evolution in Continuous Spaces. In Conference of the Computational Social Science Society of the Americas (pp. 23-42). Springer, Cham.
  • Collaborative, H. R. (2022). Smart Industry–Better Management. Advanced manufacturing, 1(6), 8.
  • Collard, P. (2022). The “flat peer learning” agent-based model. Journal of Computational Social Science, 5(1), 161-187.
  • Correa-Martinez, Y. C., & Seck, M. (2022). A generic representation of supply network resilience using simulation based experimentation. Journal of Simulation, 1-34.
  • Corsini, R. R., Costa, A., Fichera, S., & Pluchino, A. (2022). A configurable computer simulation model for reducing patient waiting time in oncology departments. Health Systems, 1-15.
  • Costanzo, A., van Haeringen, E., & Hemelrijk, C. K. (2022). Effect of time-delayed interactions on milling: a minimal model. Europhysics Letters.
  • Costas, J., Puche, J., Ponte, B., & Gupta, M. C. (2022). An agent-based simulator for quantifying the cost of uncertainty in production systems. Simulation Modelling Practice and Theory, 102660.
  • Cotfas, L. A., Delcea, C., Iancu, L. D., Ioanăş, C., & Ponsiglione, C. (2022). Large Event Halls Evacuation using an Agent-Based Modeling Approach. IEEE Access.
  • Covitt, B. A., & Anderson, C. W. (2022). Untangling Trustworthiness and Uncertainty in Science. Science & Education, 1-26.
  • Crouse, K. N., Desai, N. P., Cassidy, K. A., Stahler, E. E., Lehman, C. L., & Wilson, M. L. (2022). Larger territories reduce mortality risk for chimpanzees, wolves, and agents: Multiple lines of evidence in a model validation framework. Ecological Modelling, 471, 110063.
  • Crouse, K.N., Desai, N.P., Cassidy, K.A., Stahler, E.E., Lehman, C.L., Wilson, M.L. (2022). Larger territories reduce mortality risk for chimpanzees, wolves, and agents: Multiple lines of evidence in a model validation framework. Ecological Modelling 471. https://doi-org.turing.library.northwestern.edu/10.1016/j.ecolmodel.2022.110063
  • Cui, Y., Zhao, G., & Zhang, D. (2022). Improving students' inquiry learning in web‐based environments by providing structure: Does the teacher matter or platform matter?. British Journal of Educational Technology.
  • D'Angelo, G., & Ferretti, S. (2022). Adaptive Parallel and Distributed Simulation of Complex Networks. Journal of Parallel and Distributed Computing.
  • da Silva, A. C. G., de Lima, C. L., da Silva, C. C., Moreno, G. M. M., Silva, E. L., Marques, G. S., ... & dos Santos, W. P. (2022). Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis, 333-357.
  • Daly, A. J., De Visscher, L., Baetens, J. M., & De Baets, B. (2022). Quo vadis, agent-based modelling tools?. Environmental Modelling & Software, 105514.
  • Dangelo, V., Rodríguez, G. L., Sklate, M. F., & Pairetti, C. (2022). Robótica en FabLab: Introducción a la programación para estudiantes de Ingeniería Mecánica. Memorias de las JAIIO, 8(8), 6-23.
  • Dar, A. R., Shah, M. A., & Ahmed, M. A Meta Sensor-Based Autonomous Vehicle Safety System for Collision Avoidance Using Li-Fi Technology. Intelligent Cyber-Physical Systems for Autonomous Transportation, 237.
  • Datseris, G., & Parlitz, U. (2022). Dynamics on Networks, Power Grids, and Epidemics. In Nonlinear Dynamics (pp. 157-173). Springer, Cham.
  • Datseris, G., Vahdati, A. R., & DuBois, T. C. (2022). Agents. jl: a performant and feature-full agent-based modeling software of minimal code complexity. Simulation, 00375497211068820.
  • Datta, S., Rokade, S., & Rajput, S. P. (2022). Unsignalized Intersection Capacity Estimation Through Traffic Rule Re-adjustments Using Agent-Based Cellular Automata Simulations. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-27.
  • Davey, B., Peel, A., Horn, M., & Wilensky, U. (2022). Learning Natural Selection through Computational Models in a High School AP Biology Classroom. In Proceedings of the 16th International Conference of the Learning Sciences-ICLS 2022, pp. 1916-1917. International Society of the Learning Sciences.
  • de Campos Silva, A. D., & de Carvalho, L. L. (2022). Recifes de coral: A importância da tecnologia e dos jogos didáticos no processo de Educação Ambiental no Ensino Básico. Ambiente & Educação, 27(2), 1-35.
  • de Castro, M. G. A., & García-Peñalvo, F. J. (2022). Metodologías educativas de éxito: proyectos Erasmus+ relacionados con e-learning o TIC. Campus Virtuales, 11(1), 95-114.
  • De La Paz, S., Butler, C., Levin, D. M., & Felton, M. K. (2022). Effects of a Cognitive Apprenticeship on Transfer of Argumentative Writing in Middle School Science. Learning Disability Quarterly, 07319487221119365.
  • de Lima, H. P., Teseo, S., de Lima, R. L. C., Ferreira-Châline, R. S., & Châline, N. (2022). Temporary prey storage along swarm columns of army ants: an adaptive strategy for successful raiding?. Biology Letters, 18(2), 20210440.
  • de Oliveira Simoyama, F., Sarti, F. M., & Battisti, M. C. G. (2022). Effects of disclosing inspection scores of health facilities. Socio-Economic Planning Sciences, 81, 101183.
  • De Vizia, C., Macii, A., Patti, E., & Bottaccioli, L. (2022). A hierarchical and modular agent-oriented framework for power systems co-simulations. Energy Informatics, 5(4), 1-21.
  • de Vries, O. I. (2022). Simulating Social Interaction in Times of COVID Restrictions (Doctoral dissertation).
  • De Winne, K. (2022). Simulation-Based Port Competition: Feasibility and Implementation of an Agent-Based Framework. Competition and Regulation in Network Industries, 17835917211064920.
  • Delcea, C., Cotfus, L. A., Mieriwiak, R., & Ioanfăş, C. (2022). Grey Clustering of the Variations in Reverse Pyramid Boarding Method Considering Pandemic Restrictions. Journal of Grey System, 34(1).
  • Delcea, C., Milne, R. J., & Cotfas, L. A. (2022). Evaluating Classical Airplane Boarding Methods for Passenger Health during Normal Times. Applied Sciences, 12(7), 3235.
  • Deng, S. (2022, January). Public opinion propagation model and simulation based on Impact model and improved SIR. In ICETIS 2022; 7th International Conference on Electronic Technology and Information Science (pp. 1-5). VDE.
  • Des Jarlais, D., Bobashev, G., Feelemyer, J., & McKnight, C. (2022). Modeling HIV transmission among persons who inject drugs (PWID) at the “End of the HIV Epidemic” and during the COVID-19 pandemic. Drug and alcohol dependence, 238, 109573.
  • Detotto, C., Idda, C., Innocenti, E., Yuheng, L., Parker, D. C., & Prunetti, D. (2022, July). A resuable, extensible Netlogo building block of land and housing markets in a touristic region. In Biennial iEMSs (international Environmental Modelling and Software society) conference.
  • Dhanare, R., Nagwanshi, K. K., & Varma, S. (2022). A Study to Enhance the Route Optimization Algorithm for the Internet of Vehicle. Wireless Communications and Mobile Computing, 2022.
  • Dhou, K., & Cruzen, C. (2022). A creative chain coding technique for bi-level image compression inspired by the NetLogo HIV agent-based modeling simulation. Journal of Computational Science, 101613.
  • Dhou, K., & Cruzen, C. (2022). An innovative chain coding mechanism for information processing and compression using a virtual bat-bug agent-based modeling simulation. Engineering Applications of Artificial Intelligence, 113, 104888.
  • DIACONESCU, A., HOUZE, E., DESSALLES, J. L., VANGHELUWE, H., & FRANCESCHINI, R. (2022). Multi-Scale Model-based Explanations for Cyber-Physical Systems: the Urban Traffic Case.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. A comparison of occupational accidents in the manufacturing and construction sector through data mining techniques. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. Collaborative Planning as a determining factor at the group level in Collaborative Intelligence. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. Decentralized, Fast and Scalable Almost-Global Convergence in Single-Optimum Coordination Problems. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. Industry 4.0 and Airspace Operations Research with EU financial contribution. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díez-Echavarría, L., Gutiérrez-Gutiérrez, G., & Ríos-Echeverri, D. C. (2022). Competitive Dynamics between Physical and Virtual Markets in Multiplex Networks. Periodica Polytechnica Social and Management Sciences.
  • Dimitrov, N. (2022). Understanding the Effects of Fox Movement on the Spread of Sarcoptic Mange in Urban Settings–An Individual-based Modelling Approach (Doctoral dissertation).
  • Dimka, J., & Sattenspiel, L. (2022). “We didn't get much schooling because we were fishing all the time”: Potential impacts of irregular school attendance on the spread of epidemics. American Journal of Human Biology, 34(1), e23578.
  • Diouf, E. G., Brévault, T., Ndiaye, S., Faye, E., Chailleux, A., Diatta, P., & Piou, C. (2022). An agent-based model to simulate the boosted Sterile Insect Technique for fruit fly management. Ecological Modelling, 468, 109951.
  • do Amaral, J. V. S., de Carvalho Miranda, R., Montevechi, J. A. B., dos Santos, C. H., & Gabriel, G. T. (2022). Metamodeling-based simulation optimization in manufacturing problems: a comparative study. The International Journal of Advanced Manufacturing Technology, 1-20.
  • Dobson, G. B. (2022). Cyber-Forces, Interactions, Terrain: An agent-based framework for simulating cyber team performance (Doctoral dissertation, Carnegie Mellon University).
  • Dokin, B., Aletdinova, A., & Zheshko, A. (2022). Simulation modeling of the machine and tractor fleet to improve the technical base of the agro-industrial complex. In BIO Web of Conferences (Vol. 52, p. 00014). EDP Sciences.
  • Domino, K., Miszczak, J.A. (2022). Will you infect me with your opinion?, Physica A: Statistical Mechanics and its Applications Vol. 608, 128289 DOI:10.1016/j.physa.2022.128289 arXiv:2208.13426
  • Dong, Y., & Yang, T. (2022). Evolutionary game analysis of promoting the development of green logistics under government regulation. JUSTC, 52(9), 1-13.
  • Dong, Y., Zhen, R., & Yang, T. (2022). Evolutionary game analysis of low-carbon behavior credit supervision of logistics enterprises. JUSTC, 52(10), 1-13.
  • Dos Santos Ribeiro, R. (2022). Como los modelos computacionales permiten representar la epidemia covid-19, ayudando en la comprensión de su dinámica y en la exploración de estrategias de control (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Dragomir, O. E. (2022). MODELLING AND SIMULATION OF DISTRIBUTED SYSTEMS USING INTELLIGENT MULTI-AGENTS. Journal of Science and Arts, 22(2), 471-482.
  • Drechsler, M., Wätzold, F., & Grimm, V. (2022). The hitchhiker's guide to generic ecological-economic modelling of land-use-based biodiversity conservation policies. Ecological Modelling, 465, 109861.
  • Du, H., Han, Q., Sun, J., & Wang, C. C. (2022). Adoptions of prefabrication in residential sector in China: agent-based policy option exploration. Engineering, Construction and Architectural Management.
  • Duan, T., Wang, W., & Wang, T. (2022, August). A Review for Unmanned Swarm Gaming: Framework, Model and Algorithm. In 2022 8th International Conference on Big Data and Information Analytics (BigDIA) (pp. 164-170). IEEE.
  • Dudenhöffer, J. H., Luecke, N. C., & Crawford, K. M. (2022). Changes in precipitation patterns can destabilize plant species coexistence via changes in plant–soil feedback. Nature Ecology & Evolution, 1-9.
  • Dugarte-Peña, G. L., Sánchez-Segura, M. I., Medina-Domínguez, F., de Amescua, A., & González, C. (2022). An instance-based-learning simulation model to predict knowledge assets evolution involved in potential digital transformation projects. Knowledge Management Research & Practice, 1-22.
  • Easter, C., Leadbeater, E., & Hasenjager, M. J. (2022). Behavioural variation among workers promotes feed-forward loops in a simulated insect colony. Royal Society Open Science, 9(3), 220120.
  • Ebrie, A. S., & Kim, Y. J. Investigating Market Diffusion of Electric Vehicles with Experimental Design of Agent-Based Modeling Simulation. Available at SSRN 4019513.
  • Edgerton, E., Wang, H. H., Grant, W. E., & Masser, M. (2022). Aquatic Plant Invasion and Management in Riverine Reservoirs: Proactive Management via a Priori Simulation of Management Alternatives. Diversity, 14(12), 1113.
  • Edris, A. (2022). Identify the Individuals Status During Crowds Movement (Doctoral dissertation, University of Idaho).
  • Egger, R. (2022). Software and Tools. In Applied Data Science in Tourism (pp. 547-588). Springer, Cham.
  • Ehiagwina, F. O., Iromini, N. A., Olatinwo, I. S., Raheem, K., & Mustapha, K. A State-of-the-Art Survey of Peer-to-Peer Networks: Research Directions, Applications and Challenges. management, 14, 19-22.
  • Eilam, B., & Omar, S. Y. (2022). Science Teachers’ Construction of Knowledge About Simulations and Population Size Via Performing Inquiry with Simulations of Growing Vs. Descending Levels of Complexity. Fostering Understanding of Complex Systems in Biology Education: Pedagogies, Guidelines and Insights from Classroom-based Research, 205.
  • Eilam, B., & Omar, S. Y. (2022). Science Teachers’ Construction of Knowledge About Simulations and Population Size Via Performing Inquiry with Simulations of Growing Vs. Descending Levels of Complexity. In Fostering Understanding of Complex Systems in Biology Education (pp. 205-226). Springer, Cham.
  • Eili, M. Y., & Rezaeenour, J. (2022). An approach based on process mining to assess the quarantine strategies' effect in reducing the COVID-19 spread. Library Hi Tech, (ahead-of-print).
  • El Karkri, J., & Benmir, M. (2022). Some key concepts of mathematical epidemiology. In Mathematical Analysis of Infectious Diseases (pp. 137-162). Academic Press.
  • Elgammal, I., Alhothali, G. T., & Sorrentino, A. (2022). Segmenting Umrah performers based on outcomes behaviors: a cluster analysis perspective. Journal of Islamic Marketing.
  • Elkamel, M., Valencia, A., Zhang, W., Zheng, Q. P., & Chang, N. B. (2022). Multi-Agent Modeling for Linking a Green Transportation System with an Urban Agriculture Network in an Urban Food-Energy-Water Nexus. Sustainable Cities and Society, 104354.
  • Elkhouly, R., Tamaki, E., & Iwasaki, K. (2022). Mitigating crowded transportation terminals nearby mega-sports events. Behaviour & Information Technology, 1-17.
  • Esmaeili Avval, A., Dehghanian, F., & Pirayesh, M. (2022). Auction design for the allocation of carbon emission allowances to supply chains via multi-agent-based model and Q-learning. Computational and Applied Mathematics, 41(4), 1-41.
  • Esquivel, K. E., Hesselbarth, M. H., & Allgeier, J. E. Mechanistic support for increased primary production around artificial reefs. Ecological Applications, e2617.
  • Fabio, R. A., D'Agnese, C., & Calabrese, C. (2022). Peace attitude and friendliness influence cooperative choices in context of uncertainty. Peace and Conflict: Journal of Peace Psychology.
  • Fain, B. G., & Dobrovolny, H. M. (2022). GPU acceleration and data fitting: Agent-based models of viral infections can now be parameterized in hours. Journal of Computational Science, 101662.
  • Faiza, S., & Habib, A. H. (2022). Modeling and Simulation of Urban Mobility in a Smart City. In International Conference on Artificial Intelligence and its Applications (pp. 379-394). Springer, Cham.
  • Farahbakhsh, S., Snellinx, S., Mertens, A., Belderbos, E., Bourgeois, L., & Van Meensel, J. (2023). What's stopping the waste-treatment industry from adopting emerging circular technologies? An agent-based model revealing drivers and barriers. Resources, Conservation and Recycling, 190, 106792.
  • Farooq, O., & Singh, P. (2022). Data Analytics and Modeling in IoT-Fog Environment for Resourceconstrained IoT-Applications: A Review. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 15(7), 968-991.
  • Farris, A. V., & Tosun, G. (2022). Computing in Precollege Science, Engineering, and Mathematics Education. In Oxford Research Encyclopedia of Education.
  • Faweya, O., Desai, P. S., & Higgs III, C. F. (2022). Towards an agent-based model to simulate osseointegration in powder-bed 3D printed implant-like structures. Journal of the Mechanical Behavior of Biomedical Materials, 126, 104915.
  • Feng, B., Li, W., & Wang, L. Signal Optimization of Electronic Communication Network Based on Internet of Things. Journal of Sensors, 2022.
  • Ferguson, J. P. (2022). A Peircean Socio-Semiotic Analysis of Science Students’ Creative Reasoning as/Through Digital Simulations. Research in Science Education, 1-31.
  • Ferrão, I. G., Espes, D., Dezan, C., & Branco, K. R. L. J. C. (2022). Security and safety concerns in air taxis: a systematic literature review. Sensors, 22(18), 6875.
  • Fiore, I., Greco, A., & Pluchino, A. (2022). On Damage Identification in Planar Frames of Arbitrary Size. Shock and Vibration, 2022.
  • Fioretti, G. (2022). Emergence of Industrial Stylized Facts out of Innovative and Imitative Entrepreneurship. Available at SSRN 3581357.
  • Fitrianip, W., Edriani, A. F., Hardiansyah, R., Lestyanti, R., & Mase, L. Z. Implementation of Agent Based Modelling to Observe the Evacuating Behavior at Faculty of Engineering Building, University of Bengkulu, Indonesia. In Journal of the Civil Engineering Forum (pp. 179-192).
  • Fitzpatrick, B. G., Federico, P., Kanarek, A., & Lenhart, S. (2022). Control of a consumer‐resource agent‐based model using partial differential equation approximation. Optimal Control Applications and Methods, 43(1), 178-197.
  • Flache, A. (2022). GESIS Spring Seminar 2022: Simulation Techniques in the Social Sciences.
  • Flache, A., Mäs, M., & Keijzer, M. A. (2022). Computational approaches in rigorous sociology: agent-based computational modeling and computational social science. In Handbook of Sociological Science (pp. 57-72). Edward Elgar Publishing.
  • Flood, V. J., Shvarts, A., & Abrahamson, D. (in press). Responsive teaching for embodied learning with technology. In S. Macrine & J. Fugate (Eds.), Movement matters: How embodied cognition informs teaching and learning. MIT Press.
  • Franceschini, P. B., & Neves, L. O. (2022). A critical review on occupant behaviour modelling for building performance simulation of naturally ventilated school buildings and potential changes due to the COVID-19 pandemic. Energy and Buildings, 111831.
  • Francos, R. M., & Bruckstein, A. M. (2022). Search for Smart Evaders with Swarms of Sweeping Agents-a Resource Allocation Perspective. Journal of Intelligent & Robotic Systems, 106(4), 1-34.
  • Fu, H., Zhang, H., Zhang, M., & Hou, C. (2022). Modeling and Dynamic Simulation of the Public’s Intention to Reuse Recycled Water Based on Eye Movement Data. Water, 15(1), 114.
  • Fuhao, Z., & Qiuhong, Z. (2022). Comparison of the Response Efficiency Between the Fractal and Traditional Emergency Organizations Based on System Dynamic Simulation. Sustainable Operations and Computers.
  • Fuhrmann, T., Levy, S., Wilensky, U., Blikstein, P., Bumbacher, E., Saba, J., Langbeheim, E., Hel-Or, H., Fernandez, C., de Deus Lopes, R., Klopfer, E., Wendel, D., Wagh, A., & Wilkerson, M. (2022). Developing Accessible and Sustainable Computational Modeling Tools in Learning Science: What is Next? Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Füllsack, M. (2022). LSTM-certainty as early warning signal for critical transitions. Systems Science & Control Engineering, 10(1), 562-571.
  • Fust, P., & Schlecht, E. (2022). Importance of timing: Vulnerability of semi-arid rangeland systems to increased variability in temporal distribution of rainfall events as predicted by future climate change. Ecological Modelling, 468, 109961.
  • Gabbar, M. A., & Hasson, S. T. (2022, June). Analyzing the Connectivity of the Wireless Sensor Networks. In 2022 10th International Conference on Smart Grid (icSmartGrid) (pp. 374-379). IEEE.
  • Gaidarski, I., & Kutinchev, P. (2022). Transformation of UML Design Models of In-formation Security System into Agent-based Simulation Models. Information and Security. 53(1), 65-77
  • Galan, S.F. (2022). Modeling Complex and Intelligent Systems with NetLogo. (Bellisco Ediciones.)
  • Galety, M. G., Al Atroshi, C., Balabantaray, B., & Mohanty, S. N. (Eds.). (2022). Social Network Analysis: Theory and Applications. John Wiley & Sons.
  • Gao, J., Zhang, W., Guan, T., & Feng, Q. (2022). Evolutionary game study on multi-agent collaboration of digital transformation in service-oriented manufacturing value chain. Electronic Commerce Research, 1-22.
  • Garg, V., Tiwari, R., & Shukla, A. (2022, June). Comparative Analysis of Fruit Fly-Inspired Multi-Robot Cooperative Algorithm for Target Search and Rescue. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC) (pp. 444-450). IEEE.
  • Ge, X., & Huang, K. (2022). Designing Online Learning Environments to Support Problem-Based Learning. In Handbook of Open, Distance and Digital Education (pp. 1-18). Singapore: Springer Nature Singapore.
  • Gebrehiwot, A. A., Hashemi-Beni, L., Kurkalova, L. A., Liang, C. L., & Jha, M. K. (2022). Using ABM to Study the Potential of Land Use Change for Mitigation of Food Deserts. Sustainability, 14(15), 9715.
  • Gerard, L., Wiley, K., Debarger, A. H., Bichler, S., Bradford, A., & Linn, M. C. (2022). Self-directed science learning during COVID-19 and beyond. Journal of Science Education and Technology, 31(2), 258-271.
  • Gerdes, L., Rengs, B., & Scholz-Wäckerle, M. (2022). Labor and environment in global value chains: an evolutionary policy study with a three-sector and two-region agent-based macroeconomic model. Journal of Evolutionary Economics, 1-51.
  • Ghafoori, H. R., Sadeghi-Niaraki, A., Alesheikh, A. A., & Choi, S. M. (2022). Ubiquitous GIS based outdoor evacuation assistance: An effective response to earthquake disasters. International Journal of Disaster Risk Reduction, 81, 103232.
  • GHOREISHI, M. (2022). Socio-hydrology from Local to Large Scales: An Agent-based Modeling Approach (Doctoral dissertation, University of Saskatchewan Saskatoon).
  • Ghoreishi, M., Elshorbagy, A., Razavi, S., Blöschl, G., Sivapalan, M., & Abdelkader, A. (2022). Cooperation in a Transboundary River Basin: a Large Scale Socio-hydrological Model of the Eastern Nile. Hydrology and Earth System Sciences Discussions, 1-24.
  • Ghribi, C., Cali, E., Hirsch, C., & Jahnel, B. (2022, March). Agent-based simulations for coverage extensions in 5G networks and beyond. In 2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN) (pp. 1-7). IEEE.
  • Ghumrawi, K. A. (2022). Applying and Accelerating Large-Scale Population Simulations (Doctoral dissertation, Miami University).
  • Gibson, M., Pereira, J. P., Slade, R., & Rogelj, J. (2022). Agent-Based Modelling of Future Dairy and Plant-Based Milk Consumption for UK Climate Targets. Journal of Artificial Societies and Social Simulation, 25(2).
  • Gignoux, J., Davies, I, D., Flint, S.R. (2022). 3Worlds, a simulation platform for ecosystem modeling. Ecological Modeling 2022, 473, 110121. https://doi.org/10.1016/j.ecolmodel.2022.110121
  • Gilbert, N., & Elsenbroich, C. (2022). WATERING Crop Growth Reusable Building Block.
  • Girish, S. (2022). Emergent patterns of affiliative behaviour in group-living lemurs (Doctoral dissertation).
  • Gisen, D. C., Schütz, C., & Weichert, R. B. (2022). Development of behavioral rules for upstream orientation of fish in confined space. PLOS ONE, 17(2), e0263964.
  • Goldenbogen, B., Adler, S. O., Bodeit, O., Wodke, J. A., Escalera‐Fanjul, X., Korman, A., ... & Klipp, E. (2022). Control of COVID‐19 Outbreaks under Stochastic Community Dynamics, Bimodality, or Limited Vaccination. Advanced Science, 2200088.
  • Golmaryami, S., Ferreira, P., & Nunes, M. L. (2022, June). Consumer willingness to participate in demand-side management: An agent-based modeling approach. In Proceedings of the 5th International Conference on Energy and Environment—Bringing Together Economics and Engineering (ICEE), Porto, Portugal (pp. 2-3).
  • Gomes, I., Bot, K., Ruano, M. D. G., & Ruano, A. (2022). Recent Techniques Used in Home Energy Management Systems: A Review. Energies, 15(8), 2866.
  • González Cuevas, A., & Suppi, R. (2022). ABM simulation focused on urban mobility. In X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022).
  • Gouvea, J., Appleby, L., Fu, L., & Wagh, A. (2022). Motivating and Shaping Scientific Argumentation in Lab Reports. CBE—Life Sciences Education, 21(4), ar71.
  • Graham, S. (2022). Mapping the Landscape of our Ignorance. Simulating Roman Economies: Theories, Methods, and Computational Models, 293.
  • Grajdura, S., Borjigin, S., & Niemeier, D. (2022). Fast-moving dire wildfire evacuation simulation. Transportation Research Part D: Transport and Environment, 104, 103190.
  • Grayson, K. L., Hiliker, A. K., & Wares, J. R. (2022). R Markdown as a dynamic interface for teaching: Modules from math and biology classrooms. Mathematical Biosciences, 108844.
  • Grebennik, I., Hubarenko, Y., & Ananiev, M. (2022). Information Technologies for Assessing the Effectiveness of the Quarantine Measures. In International Conference on Information Technology in Disaster Risk Reduction (pp. 160-175). Springer, Cham.
  • Greif, H. (2022). Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence. Minds and Machines, 1-23.
  • Grewe, J., & Griva, I. (2022). Optimizing Heterogeneous Maritime Search Teams using an Agent-based Model and Nonlinear Optimization Methods. In Proceedings of the 11th International Conference on Operations Research and Enterprise Systems (ICORES 2022), 200-207.
  • Griesemer, M., & Sindi, S. S. (2022). Rules of engagement: a guide to developing agent-based models. In Microbial Systems Biology (pp. 367-380). Humana, New York, NY.
  • Grigoryan, G., Etemadidavan, S., & Collins, A. J. (2022). Computerized agents versus human agents in finding core coalition in glove games. SIMULATION, 00375497221093652.
  • Grimm, V., Berger, U., & Filatova, T. (2022). Reusable building blocks for agent-based modelling: benefits, challenges, and a template for their release.
  • Gu, H., Feng, L., & Zhen, X. (2022). Study on the stability of anaerobic digestion of food waste and the waste mushroom substrate based on SBR reactor and Netlogo simulation. Journal of Material Cycles and Waste Management, 1-17.
  • Guia, S. S., Laouid, A., Hammoudeh, M., Bounceur, A., Alfawair, M., & Eleyan, A. (2022). Co-Simulation of Multiple Vehicle Routing Problem Models. Future Internet, 14(5), 137.
  • Gulyás, L. (2022). Spatial Clustering by Schelling’s Ants. In Conference on Computational Collective Intelligence Technologies and Applications (pp. 579-586). Springer, Cham.
  • Gunckel, K. L., Covitt, B. A., Berkowitz, A. R., Caplan, B., & Moore, J. C. (2022). Computational thinking for using models of water flow in environmental systems: Intertwining three dimensions in a learning progression. Journal of Research in Science Teaching.
  • Gunckel, K. L., Covitt, B. A., Love, G., Cooper-Wagoner, J. A., & Moreno, D. (2022). Unplugged to Plugged In Breadcrumb. The Science Teacher, 89(3).
  • Güngör, Ö., Günneç, D., Salman, F. S., & Yücel, E. Prediction of Migration Paths Using Agent-Based Simulation Modeling: The Case of Syria.
  • Haacke, H. (2022). Modelling urban dynamics of a diverse elderly population with an empirically grounded agent-based model [Doctoral dissertation, Humboldt-Universitat]. Humboldt-Universitat Zu Berlin. https://doi.org/10.18452/24986
  • Hahn Utrero, T. (2022). ¿ Influyen el número de orígenes y los umbrales de confianza en las creencias en la dinámica de difusión de rumores? Una propuesta teórica desde un modelo basado en agentes. Papers: revista de sociologia, 107(2), e2994-e2994.
  • Hajian Heidary, M. (2022). Agent-based simulation-optimization model for a bi-objective stochastic multi-period supply chain design problem. Journal of Industrial Engineering and Management Studies, 8(2), 175-195.
  • Hajiesmaeili, M., Addo, L., Watz, J., Railsback, S. F., & Piccolo, J. J. (2022). Individual‐based modelling of hydropeaking effects on brown trout and Atlantic salmon in a regulated river. River Research and Applications.
  • Hakim, G., & Braun, R. (2022). Wireless Sensor Network Routing for Energy Efficiency. In Advances in Systems Engineering: Proceedings of the 28th International Conference on Systems Engineering, ICSEng 2021, December 14-16, Wrocław, Poland 28 (pp. 329-343). Springer International Publishing.
  • Hamza, M., Iqbal, W., Ahmad, A., Babar, M., & Khan, S. (2022). A social qualitative trust framework for Fog computing. Computers and Electrical Engineering, 102, 108195.
  • Hanine, Y., Malick, M. E. M., Tkiouat, M., Lahrichi, Y., & Alaoui, Y. L. (2022, May). A New Financial Group Lending Based on Smart Contracts: An Agent-based Simulation. In 2022 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-5). IEEE.
  • Hashemi Aslani, Z., Omidvar, B., & Karbassi, A. (2022). Integrated model for land-use transformation analysis based on multi-layer perception neural network and agent-based model. Environmental Science and Pollution Research, 1-14.
  • Hassanpour, S., Gonzalez, V., Liu, J., Zou, Y., & Cabrera-Guerrero, G. (2022). A hybrid hierarchical agent-based simulation approach for buildings indoor layout evaluation based on the post-earthquake evacuation. Advanced Engineering Informatics, 51, 101531.
  • Hassen, F. S., Kalla, M., & Dridi, H. (2022). Using agent-based model and Game Theory to monitor and curb informal houses: A case study of Hassi Bahbah city in Algeria. Cities, 125, 103617.
  • HAYOUN, S., & YAHYAOUI, T. (2022). Une simulation à base d’agents du modèle de Harris et Todaro dans l’explication de la migration des travailleurs au Maroc. Revue Française d'Economie et de Gestion, 3(7).
  • He, C., Jia, G., McCabe, B., Chen, Y., Zhang, P., & Sun, J. (2022). Psychological decision-making process of construction worker safety behavior: an agent-based simulation approach. International journal of occupational safety and ergonomics, 1-13.
  • Hebing, Z., & Xiaojing, Z. (2022). E-Commerce Credit Network Control Strategy from a Critical Perspective. Mathematical Problems in Engineering, 2022.
  • Heck, M. V. (2022). Agent-based modelling of the Housesparrow (Master's thesis).
  • Herget, F., Kleppmann, B., Ahrweiler, P., Gruca, J., & Neumann, M. (2022, April). How Perceived Complexity Impacts on Comfort Zones in Social Decision Contexts—Combining Gamification and Simulation for Assessment. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 203). Springer Nature.
  • Hernandez, I., Cohen, D., Gruschow, K., Nowak, A., Gelfand, M. J., & Borkowski, W. (2022). The importance of being unearnest: Opportunists and the making of culture. Journal of Personality and Social Psychology.
  • Herrmann, B., Lang, C., & Philippe, L. (2022, November). Data Synchronization in Distributed Simulation of Multi-Agent Systems. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection: 20th International Conference, PAAMS 2022, L'Aquila, Italy, July 13–15, 2022, Proceedings (Vol. 13616, p. 50). Springer Nature.
  • Heydari Fard, S. (2022). Strategic injustice, dynamic network formation, and social movements. Synthese, 200(5), 1-25.
  • Hisseine, M. A., Chen, D., & Yang, X. (2022). The Application of Blockchain in Social Media: A Systematic Literature Review. Applied Sciences, 12(13), 6567.
  • Hoffmann, B., Urquhart, N., Chalmers, K., & Guckert, M. (2022). An empirical evaluation of a novel domain-specific language–modelling vehicle routing problems with Athos. Empirical Software Engineering, 27(7), 1-52.
  • Hölzchen, E., Hertler, C., Willmes, C., Anwar, I. P., Mateos, A., Rodríguez, J., ... & Timm, I. J. (2022). Estimating crossing success of human agents across sea straits out of Africa in the Late Pleistocene. Palaeogeography, Palaeoclimatology, Palaeoecology, 110845.
  • Horn, C., Potter, R., & Peternell, M. (2022). Water Flows and Water Accumulations on Bedrock as a Structuring Element of Rock Art. Journal of Archaeological Method and Theory, 1-27.
  • Hoseini, M., Azar, A., Azarfar, A., & Ebadi, A. (2022). An Agent-Based Simulation of Insurance Supply chain Risk Detection and Assessment. Scientific Journal of System Management Studies, 2(4), 11-44.
  • Hosseini, S., & Zandvakili, A. (2022). Information dissemination modeling based on rumor propagation in online social networks with fuzzy logic. Social Network Analysis and Mining, 12(1), 1-18.
  • Housh, K., Hmelo-Silver, C. E., & Yoon, S. A. (2022). Theoretical Perspectives on Complex Systems in Biology Education. In Fostering Understanding of Complex Systems in Biology Education: Pedagogies, Guidelines and Insights from Classroom-based Research (pp. 1-16). Cham: Springer International Publishing.
  • Hu, B., Zhou, C., Tian, Y. C., Du, X., & Hu, X. (2022). Attack Intention Oriented Dynamic Risk Propagation of Cyberattacks on Cyber-Physical Power Systems. IEEE Transactions on Industrial Informatics.
  • Hu, Q., Medina, A., Siciliano, M. D., & Wang, W. Network Structures and Network Effects Across Management and Policy Contexts: A Systematic Review. Public Administration.
  • Huang, G., Yu, X., Long, Q., Huang, L., & Luo, S. (2022). The impact of economic freedom on COVID-19 pandemic control: the moderating role of equality. Globalization and Health, 18(1), 1-17.
  • Huang, J., Cui, Y., Zhang, L., Tong, W., Shi, Y., & Liu, Z. (2022). An Overview of Agent-Based Models for Transport Simulation and Analysis. Journal of Advanced Transportation, 2022.
  • Huang, Q., Zheng, X., Zhang, M., & Zhang, X. (2022). Agent-based modeling of the word-of-mouth effect on promoting brand-name agricultural products. Journal of Economic Interaction and Coordination, 1-22.
  • Hulme, A., Thompson, J., Nielsen, R.O., Read, G.J.M., & Salmon, P.M. (2022). Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling. British Journal of Sports Medicine, 53(9), https://doi.org/10.1136/bjsports-2017-098871.
  • Hunter, E., & Kelleher, J. D. (2022). Understanding the assumptions of an SEIR compartmental model using agentization and a complexity hierarchy. Journal of Computational Mathematics and Data Science, 4, 100056.
  • Hunter, E., & Kelleher, J. D. (2022). Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland. Algorithms, 15(8), 270.
  • Hutama, I. A. W., & Nakamura, H. (2022). DISASTER EVACUATION ROUTE CHOICES FOR INFORMAL SETTLEMENTS A CONCEPTUAL FRAMEWORK. SEATUC journal of science and engineering, 3(1), 16-29.
  • Ibbotson, P., Jimenez-Romero, C., & Page, K. M. (2022). Dying to cooperate: the role of environmental harshness in human collaboration. Behavioral Ecology, 33(1), 190-201.
  • Ibrahim, A. M. (2022). The conditional defector strategies can violate the most crucial supporting mechanisms of cooperation. Scientific Reports, 12(1), 1-10.
  • Idros, N., Othman, W. A. F. W., Wahab, A. A. A., Alhady, S. S. N., & Bakar, E. A. (2022). Modeling 2-D Solitary Hunting Behavior of Chimpanzee. In Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications (pp. 1077-1082). Springer, Singapore.
  • Ignatenko, O. (2022). Exploratory analysis and models for strategic learning towards equilibrium. Educational Dimension.
  • Imen, B., & Hakima, M. (2022). Is it Trustworthy?: Trusting Clients in a Cloud Based Multi Agent System. In World Conference on Information Systems and Technologies (pp. 155-164). Springer, Cham.
  • Islam, N., Bhuiya, R., Samiur, M. D., Drishty, A. S., Saha, S. S., & Akash, U. D. (2022). Surveillance in Maritime Scenario Using Deep-Learning and Swarm Intelligence (Doctoral dissertation, Brac University).
  • IUA, D. A. (2022). Analisis Pengaruh Risk Attitude terhadap Decision-Making Competence Individu pada Penggunaan Model Simulasi Berbasis Agen (Doctoral dissertation, Universitas Gadjah Mada).
  • Ivanova, Y. (2022). A Methodology for Empirical Research and Analysis in the Field of Cybersecurity. Yearbook Telecommunications 2022, 9, 45-52.
  • Izewski, N. (2022). A NetLogo COVID-19 Virus Simulation Model for Determining Better Strategies at Handling a Virus Outbreak.
  • Jahanbani, M., Vahidnia, M. H., & Aspanani, M. (2022). Geographical agent-based modeling and satellite image processing with application to facilitate the exploration of minerals in Behshahr, Iran. Arabian Journal of Geosciences, 15(9), 1-14.
  • Jensen, P. (2022). Introducing simple models of social systems. American Journal of Physics.
  • Jeong, S., Elliott, J. B., Feng, Z., & Feldon, D. F. (2022). Understanding Complex Ecosystems Through an Agent-Based Participatory Watershed Simulation. Journal of Science Education and Technology, 31(5), 691-705.
  • Jiménez, A. F., Cárdenas, P. F., & Jiménez, F. (2022). Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales. Computers and Electronics in Agriculture, 192, 106635.
  • Jin, K. S., Lee, S. M., & Kim, Y. C. (2022). Adaptive and optimized agent placement scheme for parallel agent‐based simulation. ETRI Journal, 44(2), 313-326.
  • Jin, X., Chen, C., & Zhang, M. (2022). Research on Synergy between Entrepreneurial Service and Financial Support in Crowd Innovation Space Ecosystem. Sustainability, 14(10), 5966.
  • Jing, S., & Yongzhu, W. (2022, March). Application of Small World Network in the Dissemination of Unsafe Behaviors with Masks. In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) (Vol. 6, pp. 1477-1482). IEEE.
  • João Emmanuel, D. Simulation of the Implementation of Domestic Solar Systems Using Multi-agent Systems from Web Scraping. In Proceedings of the 7th Brazilian Technology Symposium (BTSym’21): Emerging Trends in Human Smart and Sustainable Future of Cities (Volume 1) (p. 88). Springer Nature.
  • Jones, B., & Swanson, H. (2022). A Framework for Assessing Teacher’s Readiness for Pedagogical Transformation. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Juárez, G. E., Menéndez, F. D., Lafuente, C. H., Pérez, J., Franco, L., & Rivero, C. R. (2022, March). Analysis and simulation of social behavior during the COVID-19 pandemic in Argentina, using intelligent agents. In 2022 IEEE World Engineering Education Conference (EDUNINE) (pp. 1-6). IEEE.
  • Jung, C., Ahad, A., Jeon, Y., & Kwon, Y. (2022, May). SWARMFLAWFINDER: Discovering and Exploiting Logic Flaws of Swarm Algorithms. In 2022 IEEE Symposium on Security and Privacy (SP) (pp. 1808-1825). IEEE.
  • Júnior, E. C. B., Rios, V. P., Dodonov, P., Vilela, B., & Japyassú, H. F. (2022). Effect of behavioural plasticity and environmental properties on the resilience of communities under habitat loss and fragmentation. Ecological Modelling, 472, 110071.
  • Kadaverugu, R., Biniwale, R., & Matli, C. Process-Based Scenario Analyses of Future Socio-Environmental Systems: Recent Efforts and a Salient Research Agenda for Decision-Making. Modeling and Simulation of Environmental Systems, 319-330.
  • Kafai, Y. B., Xin, Y., Fields, D., & Tofel‐Grehl, C. (2022). Teaching and learning about respiratory infectious diseases: A scoping review of interventions in K‐12 education. Journal of Research in Science Teaching, 59(7), 1274-1300.
  • Kaligotla, C., Yücesan, E., & Chick, S. E. (2022). Diffusion of competing rumours on social media. Journal of Simulation, 16(3), 230-250.
  • Kamath, R., Sun, Z., & Hermans, F. (2022). Policy instruments for green-growth of clusters: Implications from an agent-based model. Environmental Innovation and Societal Transitions, 43, 257-269.
  • Kamiebisu, R., Saso, T., Nakao, J., Liu, Z., Nishi, T., & Matsuda, M. (2022). Use cases of the platform for structuring a smart supply chain in discrete manufacturing. Procedia CIRP, 107, 687-692.
  • Kane, A., Ayllón, D., O’Sullivan, R. J., McGinnity, P., & Reed, T. E. (2022). Escalating the conflict? Intersex genetic correlations influence adaptation to environmental change in facultatively migratory populations. Evolutionary Applications.
  • Kang, X., Wu, Y., Yan, D., Zhu, Y., Yao, Y., & Sun, H. (2022). A novel approach for occupants' horizontal and vertical movement modeling in non-residential buildings using Immersive Virtual Environment (IVE). Sustainable Cities and Society, 104193.
  • Kaniyamattam, K. (2022). 71 Agent-Based Modeling: A Historical Perspective and Comparison to Other Modeling Techniques. Journal of Animal Science, 100(Supplement_3), 32-33.
  • Kappenberger, J., Theil, K., & Stuckenschmidt, H. (2022). Evaluating the Impact of AI-Based Priced Parking with Social Simulation. In International Conference on Social Informatics (pp. 54-75). Springer, Cham.
  • Karkalas, S. (2022). Simplifying authoring and facilitating component reuse of programming tutors (Doctoral dissertation, Birkbeck, University of London).
  • Kaur, H. (2023). Role of Multi-agent Systems in Health Care: A Review. Emerging Technologies in Data Mining and Information Security, 367-378.
  • Kaur, N., & Kaur, H. (2022). A Multi-agent Based Evacuation Planning for Disaster Management: A Narrative Review. Archives of Computational Methods in Engineering, 1-29.
  • Kawai, Y. (2022). Agent-Based Tsunami Crowd Evacuation Simulation for Analysis of Evacuation Start Time and Disaster Rate in Zushi City. In International Conference on Information Technology in Disaster Risk Reduction (pp. 63-75). Springer, Cham.
  • Kelter, J. Wit, J., Conboy, W., Potvin, J., & Wilensky, U. (2022). Poster: A General-Purpose ‘Economic Petri Dish’ ABM with ‘Land’ and ‘Organization’ to Test Indexed Pricing Methods for Stability and Resilience. The Computational Social Science Society of the Americas (CSS) 2022.
  • Kelter, J., Wilensky, U., & Potvin, J. (2022). Introducing Land Constraints to Macroeconomic Agent-based Models. Proceedings of the 2022 Conference of The Computational Social Science Society of the Americas.
  • Khan, A. A., & Abonyi, J. (2022). Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy. Sustainability, 14(15), 9796.
  • Khan, M. A., El Sayed, H., Malik, S., Zia, M. T., Alkaabi, N., & Khan, J. (2022). A Journey towards Fully Autonomous Driving-Fueled by a Smart Communication System. Vehicular Communications, 100476.
  • Khanolkar, O. (2022). An Exploration Of The Relation between Neighborhood Resource, Crime, And The Development Of Paranoia. Modern Psychological Studies, 28(1), 4.
  • Khanolkar, O. (2022). An Exploration Of The Relation between Neighborhood Resource, Crime, And The Development Of Paranoia. Modern Psychological Studies, 28(1), 4.
  • Khodr, H., Kothiyal, A., Bruno, B., & Dillenbourg, P. (2022). An Assessment Framework for Complex Systems Understanding. In Proceedings of the 16th international conference on Learning sciences (No. CONF).
  • Kianpour, M., Kowalski, S. J., & Øverby, H. (2022). Advancing the concept of cybersecurity as a public good. Simulation Modelling Practice and Theory, 102493.
  • Kieu, G. P. S. M. Simulating civil emergency evaluation with Inverse Generative Social Science. In International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) (p. 24).
  • Kilani, R., Zouinkhi, A., Bajic, E., & Abdelkrim, M. N. (2022, June). Socialization of smart communicative objects in Industrial Internet of Things. In 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022.
  • Killeen, P., Kiringa, I., & Yeap, T. (2022). Unsupervised Dynamic Sensor Selection for IoT-based Predictive Maintenance of a Fleet of Public Transport Buses. ACM Transactions on Internet of Things.
  • Kim, K., Kaviari, F., Pant, P., & Yamashita, E. (2022). An agent-based model of short-notice tsunami evacuation in Waikiki, Hawaii. Transportation Research Part D: Transport and Environment, 105, 103239.
  • Kim, Y., & Cho, N. (2022). A Simulation Study on Spread of Disease and Control Measures in Closed Population Using ABM. Computation, 10(1), 2.
  • Kinner, T., & Whitaker, E. T. (2022). A Framework for the Design and Development of Adaptive Agent-Based Simulations to Explore Student Thinking and Performance in K-20 Science. In International Conference on Human-Computer Interaction (pp. 190-206). Springer, Cham.
  • Kobbaey, T., & Bilquise, G. (2023). Agent-Based Simulations for Aircraft Boarding: A Critical Review. In International Conference on Emerging Technologies and Intelligent Systems (pp. 42-52). Springer, Cham.
  • Kooshknow, S. M. M., Herber, R., & Ruzzenenti, F. (2022). Are electricity storage systems in the Netherlands indispensable or doable? Testing single-application electricity storage business models with exploratory agent-based modeling. Journal of Energy Storage, 48, 104008.
  • Koralewski, T. E., Wang, H. H., Grant, W. E., Brewer, M. J., & Elliott, N. C. (2022). Evaluation of Areawide Forecasts of Wind-borne Crop Pests: Sugarcane Aphid (Hemiptera: Aphididae) Infestations of Sorghum in the Great Plains of North America. Journal of Economic Entomology.
  • Kořínek, M., & Štekerová, K. (2022). Smart Cities: GIS Data for Realistic Simulations.
  • Kurchyna, V., Rodermund, S., Berndt, J. O., Spaderna, H., & Timm, I. J. (2022). Health and Habit: An Agent-based Approach. In German Conference on Artificial Intelligence (Künstliche Intelligenz) (pp. 131-145). Springer, Cham.
  • Kurchyna, V., Rodermund, S., Berndt, J.O., Spaderna, H., Timm, I.J. (2022). KI 2022: Advances in Artificial Intelligence. KI 2022. Lecture Notes in Computer Science(), vol 13404. Springer, Cham. https://doi.org/10.1007/978-3-031-15791-2_12
  • Kurdi, H., Alzuhair, A., Alotaibi, D., Alsweed, H., Almoqayyad, N., Albaqami, R., ... & Islam, A. A. A. (2022). Crowd Evacuation in Hajj Stoning Area: Planning through Modeling and Simulation. Sustainability, 14(4), 2278.
  • Kusumah, H., & Wasesa, M. (2023). Unraveling the Most Influential Determinants of Residential Segregation in Jakarta: A Spatial Agent-Based Modeling and Simulation Approach. Systems, 11(1), 20.
  • Laatabi, A., Becu, N., Marilleau, N., Amalric, M., Pignon-Mussaud, C., Anselme, B., ... & Rousseaux, F. (2022). LittoSIM-GEN: A generic platform of coastal flooding management for participatory simulation. Environmental Modelling & Software, 105319.
  • Lamarins, A., Fririon, V., Folio, D., Vernier, C., Daupagne, L., Labonne, J., ... & Oddou‐Muratorio, S. (2022). Importance of interindividual interactions in eco‐evolutionary population dynamics: The rise of demo‐genetic agent‐based models. Evolutionary Applications.
  • Lambert, S. G., Fiedler, B. L., Hershenow, C. S., Abrahamson, D., & Gorlewicz, J. L. (2022). A tangible manipulative for inclusive quadrilateral learning. Journal on Technology & Persons with Disabilities. [Winner: Best Submission—Dr. Arthur I. Karshmer Award for Assistive Technology Research]
  • Lancel, S., Chapurlat, V., Dray, G., & Martin, S. (2022). Emergency evacuation in a supermarket during a terrorist attack: towards a possible modelling of the influence of affordances on the evacuation behavior of agents in a complex virtual environment. Journal of Safety Science and Resilience.
  • Lancel, S., Chapurlat, V., Dray, G., & Martin, S. (2022). Emergency evacuation in a supermarket during a terrorist attack: towards a possible modelling of the influence of affordances on the evacuation behavior of agents in a complex virtual environment. Journal of Safety Science and Resilience.
  • Lang, D., & Ertsen, M. W. (2022). Conceptualising and Implementing an Agent-Based Model of an Irrigation System. Water, 14(16), 2565.
  • Lapp, M., & Long, C. (2022). A new approach to agent-based models of Community Resource Management based on the analysis of cheating, monitoring, and sanctioning. Ecological Modelling, 468, 109946.
  • Latif, R., Ahmed, M. U., Tahir, S., Latif, S., Iqbal, W., & Ahmad, A. (2022). A novel trust management model for edge computing. Complex & Intelligent Systems, 8(5), 3747-3763.
  • Lawless, W.F. Interdependent Autonomous Human–Machine Systems: The Complementarity of Fitness, Vulnerability and Evolution. Entropy 2022, 24, 1308. https://doi.org/10.3390/e24091308
  • Lee, S. (2022). Networks and Organizing Processes in Online Social Media. Media and Communication, 10(2), 1-4.
  • Lehmann, A., Mazzetti, P., Santoro, M., Nativi, S., Masò, J., Serral, I., ... & Giuliani, G. (2022). Essential earth observation variables for high-level multi-scale indicators and policies. Environmental Science & Policy, 131, 105-117.
  • Leon, F. (2022). ActressMAS, a. NET Multi-Agent Framework Inspired by the Actor Model. Mathematics, 10(3), 382.
  • Leonard‐Duke, J., Hung, C., Sharma, A., & Peirce, S. M. (2022). Multi‐scale Computational Model of Endothelial Cell‐Pericyte Coupling in Idiopathic Pulmonary Fibrosis. The FASEB Journal, 36.
  • Leoni, S. (2022). An Agent-Based Model for Tertiary Educational Choices in Italy. Research in Higher Education, 63(5), 797-824.
  • Levy, M., Dabholkar, S., Zhao, L., Juhl, S., Levites, L., Mills, J., Wu, S., Peel, A., Horn, M.S., & Wilensky, U. (2022). Conceptualizing and operationalizing equity-focus in designing Computational Thinking (CT) integrated science and mathematics curricula. Paper accepted to the Annual Meeting of the American Educational Research Association (AERA) 2022. San Diego, CA.
  • Levy, M., Peel, A., Dabholkar, S., Zhao, L., Juhl, S., Levites, L., Mills, J., Wu, S., Horn, M.S., & Wilensky, U. (2022). Co-Designing to Understand Equity-Focus in Computational Thinking (CT) Integrated Science Curricula. the 2022 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Li, C., Wang, H., & Song, R. (2022). Mobility-Aware Offloading and Resource Allocation in NOMA-MEC Systems via DC. IEEE Communications Letters.
  • Li, L., Wan, Y., Plewczynski, D., & Zhi, M. Simulation Model on Network Public Opinion Communication Model of Major Public Health Emergency and Management System Design. Scientific Programming, 2022.
  • Li, M., Huang, P., Chen, Z., & Chen, W. (2022, December). Patient Empowerment in Improving Health Care Workers’ Hand Hygiene Compliance: Evidence from an Agent-based Simulation. In 2022 International Conference on Advanced Enterprise Information System (AEIS) (pp. 95-100). IEEE.
  • Li, S., Hui, B., Jin, C., Liu, X., Xu, F., Su, C., & Li, T. (2022). Considering Farmers’ Heterogeneity to Payment Ecosystem Services Participation: A Choice Experiment and Agent-Based Model Analysis in Xin’an River Basin, China. International journal of environmental research and public health, 19(12), 7190.
  • Li, X., Li, J., Huang, Y., He, J., Liu, X., Dai, J., & Shen, Q. (2022). Construction enterprises’ adoption of green development behaviors: An agent-based modeling approach. Humanities and Social Sciences Communications, 9(1), 1-11.
  • Liang, D., Cong, Z., & Cao, G. (2022). Examination of Diffusion Patterns of Tornado Warning using Agent-based Model and Simulation. Weather, Climate, and Society.
  • Libkind, S., Baas, A., Halter, M., Patterson, E., & Fairbanks, J. (2022). An Algebraic Framework for Structured Epidemic Modeling. arXiv preprint arXiv:2203.16345.
  • Liew, C. W., Polanco, L., Manalang, K., & Kurt, R. A. (2022). An experimental and computational approach to unraveling interconnected TLR signaling cascades. Informatics in Medicine Unlocked, 100939.
  • Lin, J. W., Cheng, T. S., & Linn, G. (2022). The impacts of modelling-based SSI teaching module on preservice teachers’ decision making–a case of Dongfeng Highway route selection. International Journal of Science Education, 1-21.
  • Linares Martínez, F., Miguel Quesada, F. J., & Kohl, M. (2022). Patrones de homofilia resilientes en redes de amistad juvenil: estudio de caso mediante un experimento de simulación computacional. Revista Española de Investigaciones Sociologicas, (117).
  • Lindgren, R., & DeLiema, D. (2022). Viewpoint, embodiment, and roles in STEM learning technologies. Educational technology research and development, 1-26.
  • Liu, B., Shao, Y. F., Liu, G., & Ni, D. (2022). An Evolutionary Analysis of Relational Governance in an Innovation Ecosystem. SAGE Open, 12(2), 21582440221093044.
  • Liu, C., Liu, Z., & Chai, Y. (2022). Review of Virtual Simulation of Crowd Motion for Urban Emergency Management. Transportation Research Record, 03611981221141429.
  • Liu, J., Cao, L., Zhang, D., Chen, Z., Lian, X., Li, Y., & Zhang, Y. (2022). Optimization of Site Selection for Emergency Medical Facilities considering the SEIR Model. Computational Intelligence and Neuroscience, 2022.
  • Liu, J., Zhang, M., Xia, Y., Zheng, H., & Chen, C. (2022). Using agent-based modeling to assess multiple strategy options and trade-offs for the sustainable urbanization of cultural landscapes: A case in Nansha, China. Landscape and Urban Planning, 228, 104555.
  • Liu, X., Dong, J., Cui, P., Wang, M., & Guo, X. (2022). Collaborative Supply Mechanism of Government-Subsidized Rental Housing from the Perspective of Tripartite Evolutionary Game in Metropolitan Cities of China. Computational Intelligence and Neuroscience, 2022.
  • Liu, Y. (2022). Energy consumption inequality in China: What can an agent-based model tell us?. Energy & Environment, 0958305X221120257.
  • Liu, Y., Xiong, Z., Hu, Q., Niyato, D., Zhang, J., Miao, C., ... & Tian, Z. (2022). VRepChain: A Decentralized and Privacy-preserving Reputation System for Social Internet of Vehicles Based on Blockchain. IEEE Transactions on Vehicular Technology.
  • Liu, Y., Zhang, C., Yan, Y., Zhou, X., Tian, Z., & Zhang, J. (2022). A semi-centralized trust management model based on blockchain for data exchange in iot system. IEEE Transactions on Services Computing.
  • Liu, Z., & Yang, G. (2022). Large-scale traffic flow simulation based on intelligent PSO. In MATEC Web of Conferences (Vol. 355). EDP Sciences.
  • Livia-Diana, I., & Delcea, C. (2022). Risk Evaluation in Public Spaces Evacuation. In Eurasian Business and Economics Perspectives (pp. 127-141). Springer, Cham.
  • Lu, P., Chen, D., & Li, B. (2022). Simulating Rise and Fall Cycles of Vietnam Empires. Fundamental Research.
  • Lu, P., Chen, D., Li, Y., Wang, X., & Yu, S. (2022). Agent-Based Model of Mass Campus Shooting: Comparing Hiding and Moving of Civilians. IEEE Transactions on Computational Social Systems.
  • Lu, P., Li, Y., Wen, F., & Chen, D. (2022). Social Knowledge Enhances Collective Safety: Computational Models and Simulations. IEEE Transactions on Computational Social Systems.
  • Lu, P., Wen, F., Li, Y., & Chen, D. (2022). Individual behaviors, social learning, and swarm intelligence: Real case and counterfactuals. Expert Systems with Applications, 207, 117878.
  • Lu, P., Zhang, Z., Liu, C., & Li, M. (2022). Unification conditions of human civilization patterns: based on multi-agent modeling of early Chinese history (770 BC to 476 BC). Archaeological and Anthropological Sciences, 14(10), 1-16.
  • Lu, X. (2022, December). Research on traffic state of private car in urban area based on agent cellular automata model. In International Conference on Smart Transportation and City Engineering (STCE 2022) (Vol. 12460, pp. 856-863). SPIE.
  • Lu, X., Ma, H., & Wang, Z. (2022). Analysis of OODA Loop based on Adversarial for Complex Game Environments. arXiv preprint arXiv:2203.15502.
  • Lu, Y., Liu, S., & Li, C. (2022). Understanding the Effect of Management Factors on Construction Workers’ Unsafe Behaviors Through Agent-Based Modeling. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-13.
  • Luan, Z., Zhengjun, L., & Dequn, Z. (2022). Agent-based Research on Power Absorption Simulation Analysis of Renewable Energy. Journal of System Simulation, 34(1), 170.
  • Lucas, L., Helikar, T., & Dauer, J. (2022). Revision as an essential step in modeling to support predicting, observing, and explaining cellular respiration system dynamics. International Journal of Science Education, 1-28.
  • Lungeanu, A., DeChurch, L. A., & Contractor, N. S. (2022). Leading teams over time through space: Computational experiments on leadership network archetypes. The Leadership Quarterly, 101595.
  • Lv, Y., Shi, M., & Hu, Q. (2022). COVID-19 SIR network modeling and prediction. Highlights in Science, Engineering and Technology, 1, 433-440.
  • Lyu, X., Han, Q., & de Vries, B. (2022). A hypothetical urban layout generation model for exploring land use impacts on travel behavior. Travel Behaviour and Society, 28, 317-329.
  • Lyubchik, O. A., Yadlovska, O. S., Vavzhenchuk, S. Y., Korolchuk, O., & Stakhiv, O. (2022). Agent-based models: an effective tool in Ukrainian state formation and legal regulation. Revista Científica General José María Córdova, 20(38), 341-353.
  • M’hammed, S., Baudry, D., & Mustafee, N. Modelling and simulation of operation and maintenance strategy for offshore wind farms based on. Journal of Intelligent Manufacturing.
  • Ma, N., Huang, Z., & Qi, Y. (2022). Simulation Study on Complex Systems of Forest Biomass Power Generation Supply Chain in China. Computational Intelligence and Neuroscience, 2022.
  • Ma, X., Wang, D., Zheng, N., & Zhang, S. (2022). Aggregation and Adjustment mechanisms for disaster relief task allocation with uneven distribution. Journal of Industrial & Management Optimization.
  • Ma, Y., & Shen, Z. (2022). Agent-Based Simulation for Decision-Making Support of Spatial Strategy for Large-Scale Shopping Center Development. In Strategic Spatial Planning Support System for Sustainable Development (pp. 101-128). Springer, Cham.
  • Ma, Y., & Shen, Z. (2022). Simulation of Urban Growth and Household Aggregation for Planning Support of Local Spatial Strategic Plan. In Strategic Spatial Planning Support System for Sustainable Development (pp. 39-72). Springer, Cham.
  • Ma, Y., & Shen, Z. (2022). The Environment for Accommodating Agents and Representing Urban Planning Conditions. In Strategic Spatial Planning Support System for Sustainable Development (pp. 21-38). Springer, Cham.
  • Madamba, T., Moreira, R. G., Castell‐Perez, E., Banerjee, A., & da Silva, D. (2022). Agent‐based simulation of cross‐contamination of Escherichia coli O157: H7 on lettuce during processing and temperature fluctuations during storage in a produce facility. Part 2: Model implementation. Journal of Food Process Engineering, e13983.
  • Mahmood, B., & Mahmood, Y. (2022). Network-Based Method for Dynamic Burden-Sharing in the Internet of Things (IoT). In International Conference on Emerging Technology Trends in Internet of Things and Computing (pp. 79-90). Springer, Cham.
  • Mahmoud, R. M., & Youssef, A. M. (2022). A computational framework for supporting architectural education of spaces’ furnishing design. International Journal of Architectural Computing, 14780771221097683.
  • Males, L., Sumic, D., & Rosic, M. (2022, May). A Simulation Model of Autonomous Ship Firefighting. In 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT) (Vol. 1, pp. 130-134). IEEE.
  • Malhotra, R. (2022). More Than Habitat Loss and Fragmentation: The Effect of Human-Modified Landscapes on the Spatiotemporal Use and Interactions of Mesocarnivores (Doctoral dissertation).
  • Malik, J., Mahdavi, A., Azar, E., Putra, H. C., Berger, C., Andrews, C., & Hong, T. (2022). Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings. Building and Environment, 109016.
  • Malik, S., Khan, M. A., El-Sayed, H., Khan, J., & Ullah, O. (2022). How Do Autonomous Vehicles Decide?. Sensors, 23(1), 317.
  • Mallick, R. B. (2022). A Probabilistic Understanding of the Effect of Voids and Layer Thickness on Interconnectivity of Voids in Asphalt Mixes: An Agent-Based Modeling Approach. Journal of Transportation Engineering, Part B: Pavements, 148(2), 06022001.
  • Maltseva, S., Kornilov, V., Barakhnin, V., & Gorbunov, A. (2022). Self-Organization in Network Sociotechnical Systems. Complexity, 2022.
  • Mamada, R. (2022). Spatial Cournot Competitions Revisited: The Effect of the Internalization of the Costs of Point Source Pollution and Congestion by Spatial Cournot Duopolists. Available at SSRN 4221551.
  • Manastîrschi, S., Iapăscurtă, V., & Belîi, A. (2022). System dynamics models for clinical anesthesia (on the example of propofol). Conferinţa ştiinţifică anuală" Cercetarea în biomedicină și sănătate: calitate, excelență și performanță", 2022.
  • Manson, S., An, L., Clarke, K. C., Heppenstall, A., Koch, J. (2022). Methodological Issues of Spatial Agent-Based Models. Journal of Artificial Societies and Social Simulation, 23(1). https://doi.org/10.18564/JASSS.4174
  • Maqbool, A., Mirza, A., Afzal, F., Shah, T., Khan, W. Z., Zikria, Y. B., & Kim, S. W. (2022). System-Level Performance Analysis of Cooperative Multiple Unmanned Aerial Vehicles for Wildfire Surveillance Using Agent-Based Modeling. Sustainability, 14(10), 5927.
  • Maqsood, Z., Clark, J. C., Martin, E. M., Cheung, Y. F. H., Morán, L. A., Watson, S. E., ... & Watson, S. P. (2022). Experimental validation of computerised models of clustering of platelet glycoprotein receptors that signal via tandem SH2 domain proteins. PLOS Computational Biology, 18(11), e1010708.
  • Mariam, S. (2022). AGENT-BASED MODELING ON PURCHASE DECISIONS: THE IMPACT OF SOCIAL MEDIA PHENOMENA. Jurnal Ekonomi, 11(03), 1749-1756.
  • Marín Gutiérrez, D. (2022). Integration of social aspects in a multi-agent platform running in a supercomputer (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Martin, A. A., & Barnas, A. F. (2022). Sitting ducks: Strategies to increase recruitment in common eiders (Somateria mollissima) facing polar bear (Ursus maritimus) predation.
  • Martínez, F. L., Quesada, F. J. M., & Kohl, M. (2022). Patrones de homofilia resilientes en redes de amistad juvenil: estudio de caso mediante un experimento de simulación computacional. REIS: Revista Española de Investigaciones Sociológicas, (177), 43-68.
  • Martínez, Y. N. E., Santos, F. E. B., & Chavarria, P. S. (2022). La integración de las TIC en la educación superior: Aprendizajes a partir del contexto covid-19. Ciencia Latina Revista Científica Multidisciplinar, 6(2), 4260-4277.
  • Maru, V., Krishnan, K., Nannapaneni, S., & Arishi, A. (2022). Decentralized Supply Chain Network for Emerging Issues Using Mechanism Design and Agent-Based Modeling. Industrial and Systems Engineering Review, 10(1), 28-33.
  • Marvuglia, A., Bayram, A., Baustert, P., Gutiérrez, T. N., & Igos, E. (2022). Agent-based modelling to simulate farmers’ sustainable decisions: Farmers’ interaction and resulting green consciousness evolution. Journal of Cleaner Production, 332, 129847.
  • Masuda, S., Bahr, K., Tsuchiya, N., & Takemori, T. (2022). Agent based simulation with data driven parameterization for evaluation of social acceptance of a geothermal development: a case study in Tsuchiyu, Fukushima, Japan. Scientific Reports, 12(1), 1-13.
  • Mayerhoffer, D. M., & Schulz, J. (2022). Perception and privilege. Applied Network Science, 7(1), 1-25.
  • Mayes, R., Owens, D., Dauer, J., & Rittschof, K. (2022). A Quantitative Reasoning Framework and the Importance of Quantitative Modeling in Biology. Applied and Computational Mathematics, 11(1), 1-17.
  • Mazzetti, P., Nativi, S., Santoro, M., Giuliani, G., Rodila, D., Folino, A., ... & Lehmann, A. (2022). Knowledge formalization for Earth Science informed decision-making: The GEOEssential Knowledge Base. Environmental Science & Policy, 131, 93-104.
  • McCulloch, J., Ge, J., Ward, J. A., Heppenstall, A., Polhill, J. G., & Malleson, N. (2022). Calibrating Agent-Based Models Using Uncertainty Quantification Methods. Journal of Artificial Societies and Social Simulation, 25(2).
  • McGough, A., Kavak, H., & Mahabir, R. (2022). Revisiting Linus’ Law in OpenStreetMap: An Agent-Based Approach. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 123-133). Springer, Cham.
  • McIntire, E. J., Chubaty, A. M., Cumming, S. G., Andison, D., Barros, C., Boisvenue, C., ... & Stewart, F. E. (2022). PERFICT: A Re‐imagined foundation for predictive ecology. Ecology Letters.
  • McMullen, P. R. (2022). A Heuristic Search Approach to Multidimensional Scaling. American Journal of Operations Research, 12(5), 179-193.
  • Medeiros-Sousa, A. R., Laporta, G. Z., Mucci, L. F., & Marrelli, M. T. (2022). Epizootic dynamics of yellow fever in forest fragments: An agent-based model to explore the influence of vector and host parameters. Ecological Modelling, 466, 109884.
  • Meegada, S. S., & Kandaswamy, S. (2022, April). Comparison of Viral Information Spreading Strategies in Social Media. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 247). Springer Nature.
  • Meese, M. E., Mortuza, M. A., Wharton, M. M., Hala, D., Kaiser, K., Wells, D., ... & Quigg, A. (2022). Project Title: The Fate and Toxicity of Microplastics and Persistent Pollutants in the Shellfish and Fish of Matagorda Bay.
  • Meles, T. H., & Ryan, L. (2022). Adoption of renewable home heating systems: An agent-based model of heat pumps in Ireland. Renewable and Sustainable Energy Reviews, 169, 112853.
  • Mhamdi, H., Soufiene, B. O., Zouinkhi, A., Ali, O., & Sakli, H. (2022). On this page. Computational Intelligence and Neuroscience, 2, 3.
  • Miao, Q., Lin, H., Hu, J., & Wang, X. Privacy-Preserved Mobile Crowdsensing for Intelligent Transportation Systems. Intelligent Cyber-Physical Systems for Autonomous Transportation, 267.
  • Mias, A. (2022). UAB Open Labs. Open Education Week.
  • Miguel, L. B. J., Gonzalez-R, P. L., Jose, L., Canca, D., & Calle, M. (2022). A multi-agent approach to the truck multi-drone routing problem. Expert Systems with Applications, 116604.
  • Milne, R. J., Cotfas, L. A., & Delcea, C. (2022). Minimizing health risks as a function of the number of airplane boarding groups. Transportmetrica B: Transport Dynamics, 10(1), 901-922.
  • Minaei, M., & Vahidnia, M. H. (2022). Flood prevention solutions using remote sensing and agent-based modeling (Case study: Shoush city). Journal of Natural Environmental Hazards, 1-1.
  • Mirzaei, A. (2022). Development of a Dynamic Model for Health Information Seeking Behaviour (Doctoral dissertation).
  • Mohammed, A., & Ukai, T. (2022). Agent-based modelling for spatiotemporal patterns of urban land expansion around university campuses. Modeling Earth Systems and Environment, 1-15.
  • Monaco, G. Simionato, M. G. C. A. Cimino, G. Vaglini, S. Senatore and G. Caricato, "Using Artificial Immune System to Prioritize Swarm Strategies for Environmental Monitoring," 2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2022, pp. 104-110, doi: 10.1109/CogSIMA54611.2022.9830665.
  • Monti, C., Pangallo, M., Morales, G. D. F., & Bonchi, F. (2022). On learning agent-based models from data. arXiv preprint arXiv:2205.05052.
  • Moore, J.C., R.B. Boone, A. Koyama, and K. Holfeder. (2022). Enzymatic and detrital influences on the structure, function, and dynamics of spatially-explicit model ecosystems. Biochemistry.
  • Moradi, M., Fard, K. R., & Akhlaqi, M. Y. (2022). A Recommender System Method for Children’s Education Using Mobile Technology. IEEE Access.
  • Morales‐Márquez, J., & Meloni, F. Soil fauna and its potential use in the ecological restoration of dryland ecosystems. Restoration Ecology, e13686.
  • Morán‐López, T., Benadi, G., Lara‐Romero, C., Chacoff, N., Vitali, A., Pescador, D., ... & Morales, J. M. Flexible diets enable pollinators to cope with changes in plant community composition. Journal of Ecology.
  • Mousavi, S. F., Sepehri, M. M., Khasha, R., & Mousavi, S. H. (2022). Improving vascular access creation among hemodialysis patients: An agent-based modeling and simulation approach. Artificial Intelligence in Medicine, 102253.
  • Moya, D., Copara, D., Amores, J., Muñoz, M., & Pérez-Navarro, Á. (2022). Characterization of energy consumption agents in the residential sector of Ecuador based on a national survey and geographic information systems for modelling energy systems. Enfoque UTE, 13(2), 68-97.
  • Mrela, A., Sokolov, O., Osinska, V., & Duch, W. (2022). Analysis of Dynamics of Emergence and Decline of Scientific Ideas Based on Optimistic and Pessimistic Fuzzy Aggregation Norms. In Asian Conference on Intelligent Information and Database Systems (pp. 327-339). Springer, Singapore.
  • Muñoz, G. A., Gil-Costa, V., & Marin, M. (2022). Efficient simulation of natural hazard evacuation for seacoast cities. International Journal of Disaster Risk Reduction, 103300.
  • Muoghalu, C. N., Achebe, P. N., & Aigbodioh, F. A. (2022). Effect of Increasing Node Density on Performance of Threshold-sensitive Stable Election Protocol. Int. J. Advanced Networking and Applications, 13(06), 5183-5187.
  • Musaeus, L. H., Sørensen, M. L. S. K., Palfi, B. S., Iversen, O. S., Klokmose, C. N., & Petersen, M. G. (2022, October). CoTinker: Designing a Cross-device Collaboration Tool to Support Computational Thinking in Remote Group Work in High School Biology. In Nordic Human-Computer Interaction Conference (pp. 1-12).
  • Musaeus, L. H., Tatar, D., & Musaeus, P. (2022). Computational Modelling in High School Biology: A Teaching Intervention. Journal of Biological Education, 1-17.
  • Musaeus, P. Computational Thinking-et TC på CT. Dansk Universitetspædagogisk Tidsskrift, 17(32), 137-141.
  • Narayanan, B. L. (2022). Complex network theoretical approach to investigate the interdependence between factors affecting subsurface radionuclide migration (Doctoral dissertation).
  • Narkar, A. R., Tong, Z., Soman, P., & Henderson, J. H. (2022). Smart biomaterial platforms: Controlling and being controlled by cells. Biomaterials, 121450.
  • Naugle, A., Russell, A., Lakkaraju, K., Swiler, L., Verzi, S., & Romero, V. (2022). The ground truth program: simulations as test beds for social science research methods. Computational and Mathematical Organization Theory, 1-19.
  • Nazir, M., Haque, H. M. U., & Saleem, K. (2022). A semantic knowledge based context-aware formalism for smart border surveillance system. Mobile Networks and Applications, 27(5), 2036-2048.
  • Nelissen, R. M., Muñoz, I. A., Muñoz, D. C., Kramer, M. R., & Hofstede, G. J. (2022, April). Efficient Redistribution of Scarce Resources Favours Hierarchies. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 3). Springer Nature.
  • Nesar, A. B., Mahmud, T., & Hossain, F. (2022, June). Simulating the Behaviour and Displacement of Women in Water-Stressed Areas. In 2022 IEEE World AI IoT Congress (AIIoT) (pp. 372-378). IEEE.
  • Nespeca, V., Comes, T., & Brazier, F. (2022, April). A Methodology to Develop Agent-Based Models for Policy Design in Socio-Technical Systems Based on Qualitative Inquiry. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 453). Springer Nature.
  • Netz, C., Ramesh, A., Gismann, J., Gupte, P. R., & Weissing, F. J. (2022). Details matter when modelling the effects of animal personality on the spatial distribution of foragers. Proceedings of the Royal Society B, 289(1970), 20210903.
  • Neuenfeldt-Júnior, A., & de Oliveira, B. (2022). An agent-based approach to simulate the containership stowage problem. Soft Computing, 1-15.
  • Neumayr, R. (2022). Agent-Based Semiology-Simulating office occupation patterns with conversation-based social models.
  • Neves, J. E. D. A., Pedro, P. S. M., de Freitas Gomes Hernandez, M., & Junior, L. A. F. (2023). Simulation of the Implementation of Domestic Solar Systems Using Multi-agent Systems from Web Scraping. In Brazilian Technology Symposium (pp. 88-96). Springer, Cham.
  • Nezamoddini, N., & Gholami, A. (2022). A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities. Smart Cities, 5(1), 318-347.
  • Ngo, V. (2022). SOCIAL NETWORK AND THE DIFFUSION OF INVESTMENT BELIEFS: THEORETICAL EXPERIMENT AND THE CASES OF GAMESTOP SAGA. Applied Finance Letters, 11, 36-49.
  • Niehorster-Cook, L. M. (2022). The Spreading-Activation Framework Does not Explain the Effects of Degree and Clustering on Spoken Word Recognition. In Conference of the Computational Social Science Society of the Americas (pp. 112-123). Springer, Cham.
  • Niemann, J. H. (2022). Learning Reduced Models for Large-Scale Agent-Based Systems (Doctoral dissertation).
  • Nikravan, M., & Kashani, M. H. (2022). A review on trust management in fog/edge computing: Techniques, trends, and challenges. Journal of Network and Computer Applications, 103402.
  • Nizamutdinov, M. M., Akhmetzyanova, M. I., & Aitova, Y. S. (2022). Modeling approaches and tools of the mutual influence of the migration activity of the population and economic development of the territories. Экономика промышленности, 15(3), 368.
  • Noeldeke, B. (2022). Promoting Agroforestry in Rwanda: the Effects of Policy Interventions Derived from the Theory of Planned Behaviour (No. dp-693). Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  • Noeldeke, B. C. E. (2022). Simulating human behaviour in social-ecological systems: farmers’ adoption of agricultural innovations (Doctoral dissertation, Hannover: Institutionelles Repositorium der Leibniz Universität Hannover).
  • Nogare, D. D., Chitnis, A.B. (2022). NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium
  • Norman, M. D., Silvey, P. E., Koehler, M. T., & Joe, K. C. (2022). Engineering Decentralized Enterprises: Emergent Mission Accomplishment Without Centralized Command and Control. In Conference of the Computational Social Science Society of the Americas (pp. 124-151). Springer, Cham.
  • Norouzi, D., Bafandeh Zendeh, A., & Honarmand Azimi, M. (2022). Modeling of overdue receivables in the city bank Using factor-based simulation (Northwestern provinces of the country). Journal of System Management.
  • Norton, D. E. (2022). A Course on Mathematical Modeling for the Life Sciences. PRIMUS, 32(2), 154-167.
  • Notestine, J. (2022). Sensitivity and Active Subspace Analysis. North Carolina State University Dissertation.
  • Nouri, A., Saghafian, B., Bazargan-Lari, M. R., Delavar, M., & Hassanjabbar, A. (2022). Impact of Penalty Policy on Farmers’ Overexploitation Based on Agent-Based Modeling Framework. Journal of Water Resources Planning and Management, 148(5), 04022015.
  • Nourisa, J., Zeller‐Plumhoff, B., & Willumeit‐Römer, R. (2022). CppyABM: An open‐source agent‐based modeling library to integrate C++ and Python. Software: Practice and Experience.
  • Novakovic, A., & Marshall, A. H. (2022). The CP-ABM Approach for Modelling COVID-19 Infection Dynamics and Quantifying the Effects of Non-Pharmaceutical Interventions. Pattern Recognition, 108790.
  • Nugroho, A. A., & Asrol, M. (2022). The Impact of Effectiveness of Luggage Arrangement on the Airplane Passengers' Boarding Process. Periodica Polytechnica Transportation Engineering.
  • Nurwidiana, N., Sopha, B. M., & Widyaparaga, A. (2022). Simulating Socio-Technical Transitions of Photovoltaics Using Empirically Based Hybrid Simulation-Optimization Approach. Sustainability, 14(9), 5411.
  • Nwokoye, C. H., & Madhusudanan, V. (2022). Epidemic Models of Malicious-Code Propagation and Control in Wireless Sensor Networks: An Indepth Review. Wireless Personal Communications, 1-30.
  • Nwokoye, C. H., Mbeledogu, N. N., Paul, R. U., & Ugwunna, C. (2022, October). Complementing malware epidemic agent-based models with routing protocols of communication networks using NetLogo. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 833-847). Singapore: Springer Nature Singapore.
  • NYAMI, R., TSHIBUABUA, F., BUSHABU, O. K., BULEWU, B. I., KABAMBI, J. N., ILONDO, J. M., & KABWIKA, J. M. (2022). Analyse et Conception par la méthode GAIA d’un Système Multi-Agent pour la simulation de l’assainissement de l’environnement urbain en RDC. Revue Internationale du Chercheur, 3(2).
  • Obiako, I. V. (2022). Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets and Alternative Fueling Infrastructures in Closed Sociotechnical Environments (Doctoral dissertation, University of South Alabama).
  • Ofițeru, I. D., & Picioreanu, C. (2022). No model is perfect, but some are useful. Science, 376(6596), 914-916.
  • Ogegbo, A. A., & Ramnarain, U. (2022). A systematic review of computational thinking in science classrooms. Studies in Science Education, 58(2), 203-230.
  • Ogunsakin, R., & Mehandjiev, N. (2022). Towards Autonomous Production: Enhanced Meta-heuristics Algorithm. Procedia Computer Science, 200, 1575-1581.
  • Oliveira, H., Mendes, F., & Henriques, A. (2022). A investigação sobre o ensino e a aprendizagem de temas matemáticos publicada em 30 anos da revista Quadrante. Quadrante, 31(2), 32-62.
  • Oliver II, E. H. (2022). Assessing Critical Supply Chain Resilience against Critical Infrastructure Disruptions: A Model-Based Systems Engineering Perspective (Doctoral dissertation, The George Washington University).
  • Orozco-Rivera, J., Ceballos, Y., & Castillo-Grisales, J. A. (2022). Análisis del alto flujo vehicular para una vía de acceso a Medellín usando simulación basada en agentes. Revista UIS Ingenierías, 21(1), 73-82.
  • Pacaux-Lemoine, M. P., Sallak, M., Sacile, R., Flemisch, F., & Leitão, P. (2022). Introduction to the special section humans and industry 4.0. Cognition, Technology & Work, 1-5.
  • Papamichael, I., Pappas, G., Siegel, J. E., & Zorpas, A. A. (2022). Unified waste metrics: A gamified tool in next-generation strategic planning. Science of The Total Environment, 154835.
  • Pavlović, B., Ivezić, D., & Živković, M. (2022). Transition pathways of household heating in Serbia: Analysis based on an agent-based model. Renewable and Sustainable Energy Reviews, 163, 112506.
  • Pedroso Fabrin, B. H., & Ferrari, D. (2022). Investigation of fomite exposure risk to infectious diseases during aircraft boarding process using agent-based modeling. In AIAA AVIATION 2022 Forum (p. 3617).
  • Peel, A., Hao, D., Horn, M.S., Wilensky, U. (2022). How teachers integrated CT into science and math co-designed curricular units. Paper submitted to the Annual Meeting of the American Educational Research Association (AERA) 2022. Chicago, IL.
  • Peel, A., Kelter, J., Zhao, L., Horn, M., & Wilensky, U. (2022). Designing learning environments with iterative conjecture mapping to support teachers’ computational thinking learning. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Peel, A., Kelter, J., Zhao, L., Horn, M.S., & Wilensky, U. (2022). A Design-Based Research Methodology Utilizing Conjecture Mapping to Frame Embedded Co-design Cycles. the 2022 Annual Meeting of the National Association of Research in Science Teaching (NARST). Vancouver, British Columbia.
  • Peel, A., Kelter, J., Zhao, L., Horn, M.S., Wilensky, U.(2022). Conjecture Mapping: An Approach to Conducting Design-Based Research with Embedded Co-design Cycles. Paper accepted to the Annual Meeting of the American Educational Research Association (AERA) 2022. San Diego, CA.
  • Peel, A., Sadler, T. D., & Friedrichsen, P. (2022). Algorithmic Explanations: an Unplugged Instructional Approach to Integrate Science and Computational Thinking. Journal of Science Education and Technology, 1-14.
  • Pellegrino, M. (2022). Evaluation and comparison of calibration techniques for urban mobility behaviour ABM (Master's thesis).
  • Pellegrino, M., Lombardo, G., Cagnoni, S., & Poggi, A. (2022). High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation. Future Internet, 14(3), 83.
  • Peng, Y., Lopez, J. M. R., Santos, A. P., Mobeen, M., & Scheffran, J. (2022). Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil. Cities, 104161.
  • Pereira, J. C., Ferrari, D., Giarola, R., & Pedroso Fabrin, B. H. (2022). Modeling Panic Behavior in Aircraft Evacuation Simulation. In AIAA AVIATION 2022 Forum (p. 3830).
  • Pérez-Martínez, H., Bauzá, F. J., Soriano-Paños, D., Gómez-Gardeñes, J., & Floría, L. M. (2022). Emergence, survival, and segregation of competing gangs. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(8), 083114.
  • Perez, O. (2022). Transnational networked authority. Leiden Journal of International Law, 1-29.
  • Phillips, C. V., & Glover, M. (2022). How Much Ongoing Smoking Reduction is an Echo of the Initial Mass Education?. American Journal of Health Behavior, 46(1), 84-95.
  • Pietzsch B.W., Peter F.J. and Berger U. (2021) The Effect of Sanitation Felling on the Spread of the European Spruce Bark Beetle—An Individual-Based Modeling Approach. Front. For. Glob. Change 4:704930. https://doi.org/10.3389/ffgc.2021.704930
  • Pietzsch, B.W., Wudel, C. & Berger, U. Nonparametric upscaling of bark beetle infestations and management from plot to landscape level by combining individual-based with Markov chain models. Eur J Forest Res (2022). https://doi.org/10.1007/s10342-022-01512-1
  • Pike, T. D., Golden, S., Lowdermilk, D., Luong, B., & Rosado, B. (2022). Growing the simulation ecosystem: introducing Mesa Data to provide transparent, accessible, and extensible data pipelines for simulation development. SIMULATION, 00375497221077425.
  • Piou, C. (2022). La modélisation écologique pour la gestion des populations de locustes (Doctoral dissertation, Université de Montpellier).
  • Pitman, L., Nandakumar, G., & Richman, J. (2022). A Gamified Synthetic Environment for Evaluation of Counter-disinformation Solustions. Journal of Simulation Engineering.
  • Plikynas, D., Miliauskas, A., Laužikas, R., Dulskis, V., & Sakalauskas, L. (2022). The cultural impact on social cohesion: an agent-based modeling approach. Quality & Quantity, 1-32.
  • Poaquiza Yumbulema, K. M. (2022). Modelización basada en el individuo de un reactor PFR de lodos activados en fase aerobia y anaerobia utilizando el programa Netlogo (Bachelor's thesis, Quito: UCE).
  • Ponziani, F. A. (2022). Water-Based Shields Deployment on Terrain during Wildfire Spread: A Modelling Approach Using Distributed Information through Autonomous Agents. Environmental Sciences Proceedings, 17(1), 83.
  • Prandi, L., & Primiero, G. (2022). A logic for biassed information diffusion by paranoid agents in social networks. Journal of Logic and Computation.
  • Prata, J. C., Silva, C., Serpa, D., Soares, A. M., Gravato, C., & Silva, A. L. P. Mechanisms Influencing the Impact of Microplastics on Freshwater Benthic Invertebrates: Uptake Dynamics and Adverse Effects on Chironomus Riparius. Available at SSRN 4223058.
  • Pruett, L. J., Taing, A. L., Singh, N. S., Peirce, S. M., & Griffin, D. R. (2022). In silico optimization of heparin microislands in microporous annealed particle (MAP) hydrogel for endothelial cell migration. Acta Biomaterialia.
  • Qiu, H., Chen, Y., Zhang, H., Yi, W., & Li, Y. (2022). Evolutionary digital twin model with an agent-based discrete-event simulation method. Applied Intelligence, 1-17.
  • Queen, O., Jodoin, V., Pearcy, L. B., & Strickland, W. C. (2022). Agent-based Dynamics of a SPAHR Opioid Model on Social Network Structures. arXiv preprint arXiv:2202.12261.
  • Raab, R., Lenger, K., Stickler, D., Granigg, W., & Lichtenegger, K. (2022, May). An Initial Comparison of Selected Agent-Based Simulation Tools in the Context of Industrial Health and Safety Management. In Proceedings of the 2022 8th International Conference on Computer Technology Applications (pp. 106-112).
  • Rabb, N., Cowen, L., de Ruiter, J. P., & Scheutz, M. (2022). Cognitive cascades: How to model (and potentially counter) the spread of fake news. PloS one, 17(1), e0261811.
  • Radeef, H. R., Hassan, N. A., Abidin, A. R. Z., Mahmud, M. Z. H., Ismail, C. R., Abbas, H. F., & Mashros, N. (2022). Mixture design and test parameter effect on fracture performance of asphalt: a review. ASEAN Engineering Journal, 12(1), 27-39.
  • RAHMANI, A., & KOHILI, M. (2022). Modélisation et simulation système multi-agent de la Propagation d’une épidémie covid-19 (Doctoral dissertation, UNIVERSITE AHMED DRAIA-ADRAR).
  • Rahmoeller, M., & Steinweg, J. M. (2022). Implementation of a New Quantitative Biology Course: Assessment of Students’ Abilities and Confidence. PRIMUS, 32(3), 346-366.
  • Rajbanshi, B., & Guruacharya, A. (2022). Panama: An Open-Source Educational App for Ion Channel Biophysics Simulation. Frontiers in neuroinformatics, 16.
  • Rajendran, V., Ramasamy, R. K., & Mohd-Isa, W. N. (2022). Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach. Future Internet, 14(2), 56.
  • Ramadhana, W., Astawaa, I. G. S., Astutia, L. G., Putria, L. A. A. R., Suputraa, I. P. G. H., & Santiyasaa, I. W. Pengembangan Aplikasi Optimasi Rute Destinasi Wisata di Banyuwangi Menggunakan Modern Android Development (MAD) Pattern. Jurnal Elektronik Ilmu Komputer Udayana p-ISSN, 2301, 5373.
  • Ramírez-Ávila, G. M., Depickère, S., Deneubourg, J. L., & Kurths, J. (2022). A simple game and its dynamical richness for modeling synchronization in firefly-like oscillators. The European Physical Journal Special Topics, 1-10.
  • Rankin, Naomi. (2022). An Agent-Based Model of COVID-19 on the Diamond Princess Cruise Ship. SIAM Undergraduate Research Online. 15. 10.1137/21S1462520.
  • Rappel, O., Ben-Asher, J., & Bruckstein, A. (2022). Exploration and Coverage with Swarms of Settling Agents. arXiv preprint arXiv:2209.05512.
  • Rates, C. A., Mulvey, B. K., Chiu, J. L., & Stenger, K. (2022). Examining ontological and self-monitoring scaffolding to improve complex systems thinking with a participatory simulation. Instructional Science, 1-23.
  • Ray, S. K., Alani, M. M., & Ahmad, A. (2022). Big Data for Educational Service Management. In Big Data and Blockchain for Service Operations Management (pp. 139-161). Springer, Cham.
  • Razzaq, S., Dar, A. R., Shah, M. A., Khattak, H. A., Ahmed, E., El-Sherbeeny, A. M., ... & Rauf, H. T. (2022). Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles. Applied Sciences, 12(3), 1049.
  • Razzouqi, M., & Boulmakoul, A. Multiagent Modeling for Pedestrian Risk Assessment. In Smart Trajectories (pp. 317-330). CRC Press.
  • Reda, M., Noël, C., Settembre, N., Chambert, J., Lejeune, A., Rolin, G., & Jacquet, E. (2022). An agent-based model of vibration-induced intimal hyperplasia. Biomechanics and Modeling in Mechanobiology, 1-25.
  • Regnath, F., Berger, C., & Mahdavi, A. (2022, May). The impact of occupants' energy awareness and thermal preferences on buildings' performance. In CLIMA 2022 conference.
  • Reinhardt, O., Warnke, T., & Uhrmacher, A. M. (2022). A Language for Agent-based Discrete-event Modeling and Simulation of Linked Lives. ACM Transactions on Modeling and Computer Simulation (TOMACS), 32(1), 1-26.
  • Reinhardt, O., Warnke, T., & Uhrmacher, A. M. (2022). Agent-Based Modelling and Simulation with Domain-Specific Languages. In Towards Bayesian Model-Based Demography (pp. 113-134). Springer, Cham.
  • Ribas-Xirgo, L. (2022). A state-based multi-agent system model of taxi fleets. Multimedia Tools and Applications, 1-20.
  • Ribeiro, S., Breda, A., & Rocha, E. (2022). COMPUTATIONAL THINKING IN UPPER-SECONDARY EDUCATION: A SYSTEMATIC LITERATURE REVIEW. INTED2022 Proceedings, 2650-2659.
  • Richman, J., Pitman, L., & Nandakumar, G. S. (2022). A Gamefied Synthetic Environment for Evaluation of Counter-Disinformation Solutions. Journal of Simulation Engineering, 3, 7-1.
  • Rivera-Rogel, D., Cajamarca, A. O., & Beltrán-Flandoli, A. M. (2022). Experiencia de TV educativa en Ecuador en el marco de la pandemia. Edição/Edition, 265.
  • Rivera, M., Toledo-Jacobo, L., Romero, E., Oprea, T. I., Moses, M. E., Hudson, L. G., ... & Grimes, M. M. (2022). Agent-based modeling predicts Rac1 is critical for ovarian cancer metastasis. Molecular Biology of the Cell, mbc-E21.
  • Robeva, R., Comar, T. D., & Eaton, C. D. (2022). Can We Bridge the Gap? Mathematics and the Life Sciences, Part 1–Calculus-Based Modules, Programs, Curricula. PRIMUS, 32(2), 117-123.
  • Robins, A., Burrows, A., & Borowczak, M. (2022, August). On the Development of Cybersecurity and Computing Centric Professional Developments and the Subsequent Implementation of Topics in K12 Lesson Plans (RTP). In 2022 ASEE Annual Conference & Exposition.
  • Robles Cuesta, T. M. (2022). Modelización basada en el individuo utilizando NetLogo de un reactor biológico de membrana (MBR) aerobio (Bachelor's thesis, Quito: UCE).
  • Röchert, D., Cargnino, M., & Neubaum, G. (2022). Two sides of the same leader: an agent-based model to analyze the effect of ambivalent opinion leaders in social networks. Journal of Computational Social Science, 1-47.
  • Roci, M., Salehi, N., Amir, S., Shoaib-ul-Hasan, S., Asif, F. M., Mihelič, A., & Rashid, A. (2022). Towards Circular Manufacturing Systems implementation: A Complex Adaptive Systems perspective using modelling and simulation as a quantitative analysis tool. Sustainable Production and Consumption.
  • Rodriguez-Ulloa, R. (2022). Cybernetic governance of the Peruvian State: a proposal. AI & SOCIETY, 1-23.
  • Rojas-Domínguez, A., Arroyo-Duarte, R., Rincón-Vieyra, F., & Alvarado-Mentado, M. (2022). Modeling cancer immunoediting in tumor microenvironment with system characterization through the ising-model Hamiltonian. BMC bioinformatics, 23(1), 1-25.
  • Romanowska, I., Carrignon, S., Coto-Sarmiento, M., Montanier, J. M., & Rubio-Campillo, X. (2022). From Counting Pots to Reconstructing Economy: Computational Tools Developed in the EPNet Project. Arqueología y Téchne: Métodos formales, nuevos enfoques: Archaeology and Techne: Formal methods, new approaches, 27.
  • Romero Romero, M. H. (2022). Modelización del ciclo de vida de la mosca soldado negro (Hermetia illucens) desarrollándose sobre desechos orgánicos (Bachelor's thesis, Quito: UCE).
  • Rubio, M. Á. G., Millán, N. D. C. O., Soto, M. D. C. S., & Parra, J. M. F. (2022). La simulación computacional como propuesta para el apoyo en la toma de decisiones contra la deserción escolar en Tijuana Baja California. Revista Ibérica de Sistemas e Tecnologias de Informação, (E47), 277-287.
  • Ruiz-Ledezma, E. R., Acosta-Magallanes, F., & del Socorro Valero-Cázarez, M. (2022). Una Aproximación Interdisciplinar STEM con Recursos Tecnológicos para el Tratamiento de Conceptos de Física y Matemáticas. Cultura Científica y Tecnológica, 19(2), E13-E22.
  • Rukomojnikov, K. P., Sergeeva, T. V., Gilyazova, T. A., & Komisar, V. P. (2022, May). Computer modeling to support management and organizational decisions in the use of a forest harvester. In Computer Applications for Management and Sustainable Development of Production and Industry (CMSD2021) (Vol. 12251, pp. 144-148). SPIE.
  • Rup, C., Bajic, E., & Mekki, K. (2022, October). Snowball: An Asynchronous Probabilistic Protocol for Neighbour Discovery in Mobile BLE Network. In IEEE 8th World Forum on Internet of Things.
  • Saba, J., Hel-Or, H., & Levy, S. T. תוכרעמ לש םייבושיח םילדומ תיינב ידי לע הדימל םייניבה תביטח ידימלת ברקב עדמב תובכרומ.‎
  • Saba, J., Langbeheim, E., Hel‐Or, H., & Levy, S. T. (2022). Identifying aspects of complex and technological systems in the mental models of students who constructed computational models of electric circuits. Journal of Research in Science Teaching.
  • Sadaf, M., Jabbar, A., & Zaman, A. (2022). An An Agent Based Model for Combining the Climatic, Physical and Behavioral Response to Logging, Salinity, and Farmers Earnings in Irrigated Agriculture of Pakistan. Pakistan Journal of Economic Studies (PJES), 5(1), 135-165.
  • Saeed, A. K. (2022). Agent-based simulations on Catalan interprovincial migrations.
  • Sagar, S., Mahmood, A., Sheng, Q. Z., Pabani, J. K., & Zhang, W. E. (2022). Understanding the Trustworthiness Management in the Social Internet of Things: A Survey. arXiv preprint arXiv:2202.03624.
  • Saha, B., Martínez-García, M., Bhattacharya, S. N., & Joshi, R. (2022). Overcoming Choice Inertia through Social Interaction—An Agent-Based Study of Mobile Subscription Decision. Games, 13(3), 47.
  • Salau, K. R., Baggio, J. A., Shanafelt, D. W., Janssen, M. A., Abbott, J. K., & Fenichel, E. P. (2022). Taking a moment to measure networks—an approach to species conservation. Landscape Ecology, 1-19.
  • Salawu, G. A. (2022). The impact of disruptive technology on the manufacturing process, and productivity, in an advanced manufacturing environment (Doctoral dissertation).
  • Saleem, K., Saleem, M., Zeeshan, R., Javed, A. R., Alazab, M., Gadekallu, T. R., & Suleman, A. (2022). Situation-aware BDI Reasoning to Detect Early Symptoms of Covid 19 using Smartwatch. IEEE Sensors Journal.
  • Salmon, P. M., Stanton, N. A., Walker, G. H., Hulme, A., Goode, N., Thompson, J., & Read, G. J. (2022). Agent-Based Modelling (ABM). In Handbook of Systems Thinking Methods (pp. 253-269). CRC Press.
  • Sànchez-Marrè, M. (2022). Tools for IDSS Development. In Intelligent Decision Support Systems (pp. 533-582). Springer, Cham.
  • Santos, M. V., Mota, I., & Campos, P. (2022). Analysis of online position auctions for search engine marketing. Journal of Marketing Analytics, 1-17.
  • Sarin, A. (2022). The Kolam Drawing: A Point Lattice System. Design Issues, 38(3), 34-54.
  • Sarnatskyi, V., & Baklan, I. (2023). CTrace: Language for Definition of Epidemiological Models with Contact-Tracing Transmission. In International Scientific Conference “Intellectual Systems of Decision Making and Problem of Computational Intelligence” (pp. 426-448). Springer, Cham.
  • Scharf, A., Mitteldorf, J., Armstead, B., Schneider, D., Jin, H., Kocsisova, Z., ... & Kornfeld, K. (2022). A laboratory and simulation platform to integrate individual life history traits and population dynamics. Nature Computational Science, 2(2), 90-101.
  • Schimpf, C., & Castellani, B. (2022). Approachable modeling and smart methods: a new methods field of study. International Journal of Social Research Methodology, 1-15.
  • Schmitt, J. (2022). Von komplexen Systemen und theoretischen Riesen. In Mechanismen der Polarisierung von Parteiensystemen (pp. 115-301). Springer VS, Wiesbaden.
  • Schneider, A. (2022). Meinungsdynamik und-manipulation durch Social Bots: Eine Untersuchung sozialer Online-Netzwerke auf Basis eines agentenbasierten Modells (Vol. 28). Tectum Wissenschaftsverlag.
  • Schooltink, F. (2022). A simulation of the ecological impact of three smartphone strategies (Bachelor's thesis, University of Twente).
  • Schroeder, S. A., Vendome, C., Giabbanelli, P. J., & Montfort, A. M. (2022, December). Towards reusable building blocks to develop COVID-19 simulation models. In 2022 Winter Simulation Conference (WSC) (pp. 569-580). IEEE.
  • Schutte, S., & Kelling, C. (2022). A Monte Carlo analysis of false inference in spatial conflict event studies. PloS one, 17(4), e0266010.
  • Seid, E. A., & Jin, K. Y. (2022). Agent-Based Modeling for Market Penetration of Electric Vehicles. 대한산업공학회 춘계공동학술대회 논문집, 766-779.
  • Sells, S. N., Mitchell, M. S., Ausband, D. E., Luis, A. D., Emlen, D. J., Podruzny, K. M., & Gude, J. A. (2022). Economical defence of resources structures territorial space use in a cooperative carnivore. Proceedings of the Royal Society B, 289(1966), 20212512.
  • Semboloni, F. (2022). From complex dynamics to the architecture of the city. In Lake Como School of Advanced Studies Complexity and Emergence: Ideas, Methods, with special attention to Economics and Finance (pp. 137-162). Springer, Cham.
  • Şendurur, P., & Sendurur, E. (2022). Students as Gamers: Design, Code, and Play. In Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning (pp. 868-887). IGI Global.
  • Shaaban, M., Scheffran, J., Elsobki, M. S., & Azadi, H. (2022). A Comprehensive Evaluation of Electricity Planning Models in Egypt: Optimization versus Agent-Based Approaches. Sustainability, 14(3), 1563.
  • Shane, R. (2022, September). Revisiting Linus’ Law in OpenStreetMap: An Agent-Based Approach. In Social, Cultural, and Behavioral Modeling: 15th International Conference, SBP-BRiMS 2022, Pittsburgh, PA, USA, September 20–23, 2022, Proceedings (Vol. 13558, p. 123). Springer Nature.
  • Shapiro, B., & Crooks, A. (2022). Drone strikes and radicalization: an exploration utilizing agent-based modeling and data applied to Pakistan. Computational and Mathematical Organization Theory, 1-19.
  • Sharma, D., Chaturvedi, S., Chaudhary, V., Kaul, A., & Mishra, A. K. (2022). Emerging Scope of Computer-Aided Drug Design in Targeting ROS in Cancer Therapy.
  • Shen, Y., Yang, F., Gao, M., & Dong, W. (2022). Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model. arXiv preprint arXiv:2205.02332.
  • Shiang, C. W., & Hussain, N. (2022). Modelling of Crowd Evacuation with Communication Strategy using Social Force Model. Journal of Optimization in Industrial Engineering, 15(1), 233-241.
  • Shin, H. (2022). Quantifying the Health Effects of Exposure to Non-Exhaust Road Emissions using Agent-based Modelling (ABM). MethodsX, 101673.
  • Shin, H. C., Vallury, S., Janssen, M. A., & Yu, D. J. (2022). Joint effects of voluntary participation and group selection on the evolution of altruistic punishment. PloS one, 17(5), e0268019.
  • Shin, H., & Bithell, M. (2022). Exposure to Non-exhaust Emission in Central Seoul Using an Agent-based Framework. In Advances in Social Simulation (pp. 343-354). Springer, Cham.
  • Shin, J., Dobson, G. B., Carley, K. M., & Carley, L. R. (2022). OSIRIS: Organization Simulation in Response to Intrusion Strategies. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 134-143). Springer, Cham.
  • Shoger, J. L. (2025). From Analogue to Digital Command and Control Decision-Making: The Law of Requisite Variety and Fuzzy Methods in Quantifying Situation Assessment (Doctoral dissertation, State University of New York at Binghamton).
  • Shu, Q., Rötzer, T., Detter, A., & Ludwig, F. (2022). Tree Information Modeling: A Data Exchange Platform for Tree Design and Management. Forests 2022, 13, 1955.
  • Shukla, M. K., Singh, L., Vidya, S., Quasim, H., & Bhandari, R. (2022). Pollination System for Greenhouse Flowering Plants Using Nano Drones. In Advances in Mechanical Engineering and Technology (pp. 157-162). Springer, Singapore.
  • Silva Junior, W. F. D. Cultura de células 3D in silico por meio de técnicas de Modelagem Baseada em Agentes: aplicações em engenharia de tecidos (Doctoral dissertation, Universidade de São Paulo).
  • Silva, A. J. P. D. Visualização em química e temporalidade: um estudo de caso sobre práticas e percepções na formação inicial de professores (Doctoral dissertation, Universidade de São Paulo).
  • Silva, V., Calderon, A., Todorovic, L., & Martins, L. (2022). Incorporating Future Earthquake Risk in Disaster Risk Management. In European Conference on Earthquake Engineering and Seismology (pp. 179-196). Springer, Cham.
  • Sirizzotti, M. (2022). Agent-based Modelling and Big Data: Applications for Maritime Traffic Analysis.
  • Sissa, G., & Franza, G. (2022, June). From micro behaviors to macro effects–Agent Based Modeling of environmental awareness spread and its effects on critical resource consumption. In 2022 International Conference on ICT for Sustainability (ICT4S) (pp. 99-108). IEEE.
  • Sivakumar, N., Warner, H. V., Peirce, S. M., & Lazzara, M. J. (2022). A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion. PLOS Computational Biology, 18(11), e1010701.
  • SOCIAL, W. (2022). Construction of Dialogue among Couples in Yazd: Moving towards a Grounded Theory. SOCIAL WELFARE, 22(84).
  • Sokolov, O., Mrela, A., Bieniek-Majka, M., Osinska, V., & Duch, W. (2022, September). Model of Trust Dissemination of Products Based on Fuzzy Aggregation Norms. In 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS) (pp. 741-744). IEEE.
  • Song, C., Shao, Q., Zhu, P., Dong, M., & Yu, W. (2022). An emergency aircraft evacuation simulation considering passenger overtaking and luggage retrieval. Reliability Engineering & System Safety, 108851.
  • Sørensen, M. L. S. K., Fog, B. V., Musaeus, L. H., & Petersen, M. G. (2022, October). KnitxCode: Exploring a Craftsmanship-driven Approach to Computational Thinking. In Adjunct Proceedings of the 2022 Nordic Human-Computer Interaction Conference (pp. 1-5).
  • Sotnik, G., Shannon, T., & Wakeland, W. (2022). A new agent-based model offers insight into population-wide adoption of prosocial common-pool behavior. The Journal of Mathematical Sociology, 1-28.
  • Sprinz, J. (2022). " Y'all are just too sensitive": A computational ethics approach to understanding how prejudice against marginalized communities becomes epistemic belief. arXiv preprint arXiv:2207.01017.
  • Srinivasan, A., & Namilae, S. Infection Risk Mitigation Using Pedestrian Dynamics. In Architectural Factors for Infection and Disease Control (pp. 93-108). Routledge.
  • Stanojevic, A., Cherubini, G., Woźniak, S., & Eleftheriou, E. (2022). Time-encoded multiplication-free spiking neural networks: application to data classification tasks. Neural Computing and Applications, 1-17.
  • Starr, J., Kain, M., & Bhatia, S. An Agent-Based Model for Localized COVID-19 Transmission Dynamics and Intervention Impacts.
  • Steffens, B., Corlay, Q., Suurmeyer, N., Noglows, J., Arnold, D., & Demyanov, V. (2022). Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects. Energies, 15(3), 902.
  • Stern, J. L., Valencia-Romero, A., & Grogan, P. T. (2022). Strategic robustness in bi-level system-of-systems design. Design Science, 8.
  • Student, J. (2022). Agent-Based Modelling. In Applied Data Science in Tourism (pp. 481-511). Springer, Cham.
  • Su, P., Chen, M., & Wang, Y. (2022). Agent-based model: A method worthy of promotion in Library and Information Science. Journal of Information Science, 01655515211061867.
  • Sulis, E., & Taveter, K. (2022). Agent-Based Business Process Simulation: A Primer with Applications and Examples. Springer Nature.
  • Sulis, E., & Taveter, K. (2022). Multi-Agent Systems and Business Process Management. In Agent-Based Business Process Simulation: A Primer with Applications and Examples (pp. 131-140). Cham: Springer International Publishing.
  • Sullivan, F. R., Duan, L., & Pektas, E. (2022, June). Design of an Evaluative Rubric for CT Integrated Curriculum in the Elementary Grades. In CTE-STEM 2022 conference.
  • Sun, L., & Fu, Z. (2022). Research on Universities’ Control of Online Discourse Power in the Period of COVID-19: A Case Study of Shanghai Universities. In INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND APPLICATIONS (pp. 95-103). Springer, Singapore.
  • Sun, M. C., Sakai, K., Chen, A. Y., & Hsu, Y. T. (2022). Location problems of vertical evacuaiton structures for dam-failure floods: Considering shelter-in-place and horizontal evacuation. International Journal of Disaster Risk Reduction, 103044.
  • Sung, R., & Park, J. (2022). Economic Sanctions and Consumer Behavior in Target States: An Agent-Based Model of Boycott Movements. In Conference of the Computational Social Science Society of the Americas (pp. 43-55). Springer, Cham.
  • Sunxin, W. A. N. G., Yanming, W. A. N. G., Jie, K. O. N. G., & Gaopan, S. H. E. N. (2022). A flexible scaling self-healing method for morphology of swarm robots. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 40(1), 206-214.
  • Swanson, H., Sherin, B., & Wilensky, U. (2022). Tuning Perceptions and Inferences to See a Graph in a New Way. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Sweety, S. A., Khan, M., Haque, A., & Salehin, M. (2022). An Agent Based Model of Mangrove Social-Ecological System for Livelihood Security Assessment. In Water Management: A View from Multidisciplinary Perspectives (pp. 319-349). Springer, Cham.
  • Syed, M., Cagely, M., Dogra, P., Hollmer, L., Butner, J. D., Cristini, V., & Koay, E. J. (2022). Immune‐checkpoint inhibitor therapy response evaluation using oncophysics‐based mathematical models. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, e1855.
  • Symons, S. L., Harvey, C. T., & Eyles, C. H. (2022). Innovating, integrating, and influencing: A science program for the 21st century. Where learning deeply matters: Reflections on the past, present, and future of teaching at McMaster University, 1(1).
  • Szangolies, L., Rohwäder, M. S., & Jeltsch, F. (2022). Single Large AND Several Small habitat patches: A community perspective on their importance for biodiversity. Basic and Applied Ecology.
  • Szczepanska, T., Angourakis, A., Graham, S., & Borit, M. (2022, April). Quantum Leaper: A Methodology Journey From a Model in NetLogo to a Game in Unity. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 191). Springer Nature.
  • Tan, W. C., & Sidhu, M. S. (2022). Review of RFID and IoT integration in supply chain management. Operations Research Perspectives, 100229.
  • Tan, X. J., Cheor, W. L., Yeo, K. S., & Leow, W. Z. (2022). Expert systems in oil palm precision agriculture: A decade systematic review. Journal of King Saud University-Computer and Information Sciences.
  • Taraktaş, B. (2022). Incorporating Computational Social Science in Political Science. In Opportunities and Challenges for Computational Social Science Methods (pp. 23-44). IGI Global.
  • Taranum, F., Sridevi, K., Hijab, M., Sayeedunissa, S. F., Kaleem, A., & Niraja, K. S. (2022). Role of an Optimal Multiagent Scheduling in Different Applications Using ML. In Multi-Agent Technologies and Machine Learning. IntechOpen.
  • Taylor, R., Forrester, J., Pedoth, L., & Zeitlyn, D. (2022). Structured output methods and environmental issues: perspectives on co-created bottom-up and ‘sideways’ science. Humanities and Social Sciences Communications, 9(1), 1-11.
  • Tedeschi, L. O. (2022). ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: The progression of data analytics and artificial intelligence in support of sustainable development in animal science. Journal of Animal Science.
  • Terán, O., Leger, P., & López, M. (2022). Modeling and simulating Chinese cross-border e-commerce: an agent-based simulation approach. Journal of Simulation, 1-18.
  • Termos, A., & Yorke-Smith, N. (2022). Urbanism and Geographic Crises: A Micro-Simulation Lens on Beirut. Urban Planning, 7(1), 87-100.
  • Thakur, C., & Gupta, S. (2022). Multi-Agent System Applications in Health Care: A Survey. Multi Agent Systems: Technologies and Applications towards Human-Centered, 139.
  • Then, A., Ewald, J., Söllner, N., Cooper, R. E., Küsel, K., Ibrahim, B., & Schuster, S. (2022). Agent-based modelling of iron cycling bacteria provides a framework for testing alternative environmental conditions and modes of action. Royal Society Open Science, 9(5), 211553.
  • Thomas, S. R., & Trinh, M. P. (2022, July). Effects of Transformational Leadership on Employability and Employee Retention. In Academy of Management Proceedings. Academy of Management Briarcliff Manor, NY 10510.
  • Thompson, J., & Cruz-Gambardella, C. (2022). Development of a Computational Policy Model for Comparing the Effect of Compensation Scheme Policies on Recovery After Workplace Injury. Journal of Occupational Rehabilitation, 32, 241-251.
  • Torrens, P. M. (2022). Agent models of customer journeys on retail high streets. Journal of Economic Interaction and Coordination, 1-42.
  • Toth, C. A., Pauli, B. P., McClure, C. J., Francis, C. D., Newman, P., Barber, J. R., & Fristrup, K. (2022). A stochastic simulation model for assessing the masking effects of road noise for wildlife, outdoor recreation, and bioacoustic monitoring. Oecologia, 1-12.
  • Tracy, M., Gordis, E., Strully, K., Marshall, B. D., & Cerdá, M. (2022). Applications of agent-based modeling in trauma research. Psychological Trauma: Theory, Research, Practice, and Policy.
  • Trejo, J. A. O., Razon, T. G. N., de los Angeles Cobian, C., Ojeda, A. L., & Aguayo, M. G. B. (2022). Fase de regreso a clases en ciudades inteligentes, simulación de modelo epidemiológico SIR en estudio de caso de la universidad de Guadalajara, en CUCEA. Ciencia Latina Revista Científica Multidisciplinar, 6(5), 1586-1602.
  • Trentesaux, D., & Karnouskos, S. (2022). Engineering ethical behaviors in autonomous industrial cyber-physical human systems. Cognition, Technology & Work, 24(1), 113-126.
  • Trivedi, A., & Pandey, M. (2022). Testing and evaluation of crowd management strategies at religious gatherings in India using agent-based modelling and simulation. International Journal of Advanced Intelligence Paradigms, 22(3-4), 379-415.
  • Truong, V. T., Baverel, P. G., Lythe, G. D., Vicini, P., Yates, J. W., & Dubois, V. F. (2022). Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models. CPT: pharmacometrics & systems pharmacology, 11(2), 133-148.
  • Turchaninov, I., van Dam, K. H., Bustos-Turu, G., & Acha, S. Transport electrification and fast-charging expansion: A case study in Alaska. In International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) (p. 55).
  • Twardawa, M. Zjawiska emergentne. Biologia, 1, 26.
  • Tzouras, P. G., Mitropoulos, L., Stavropoulou, E., Antoniou, E., Koliou, K., Karolemeas, C., ... & Kepaptsoglou, K. (2022). Agent-based models for simulating e-scooter sharing services: A review and a qualitative assessment. International Journal of Transportation Science and Technology.
  • Uchmański, J., Niewolski, M., & Janiszewski, J. (2022). Interspecific competition in perennial sedentary organisms: An individual‐based model. Population Ecology.
  • Uddin, M. N., Chi, H. L., Wei, H. H., Lee, M., & Ni, M. (2022). Influence of interior layouts on occupant energy-saving behaviour in buildings: An integrated approach using Agent-Based Modelling, System Dynamics and Building Information Modelling. Renewable and Sustainable Energy Reviews, 161, 112382.
  • Ullah, K. M., & Dwivedi, P. Ascertaining Land Allocation Decisions of Farmers about the Adoption of Carinata as a Potential Crop for Sustainable Aviation Fuel Production in the Southern United States. GCB Bioenergy.
  • Umlauft, M., Schranz, M., & Elmenreich, W. (2022, July). Simulation of Swarm Intelligence for Flexible Job-Shop Scheduling with SwarmFabSim: Case Studies with Artificial Hormones and an Ant Algorithm. In International Conference on Simulation and Modeling Methodologies, Technologies and Applications (pp. 133-155). Cham: Springer International Publishing.
  • Umlauft, M., Schranz, M., & Elmenreich, W. SwarmFabSim: A Simulation Framework for Bottom-up Optimization in Flexible Job-Shop Scheduling Using NetLogo.
  • Utrero, T. H. (2022). ¿ Influyen el número de orígenes y los umbrales de confianza en las creencias en la dinámica de difusión de rumores? Una propuesta teórica desde un modelo basado en agentes. Papers. Revista de Sociologia, 107(2), e2994-e2994.
  • Vakil, S., Reith, A., & Melo, N. A. (2022). Jamming power: Youth agency and community‐driven science in a critical technology learning program. Journal of Research in Science Teaching.
  • Valdez, A. C. Towards an Understanding of Opinion Formation on the Internet. group, 4, 8.
  • Vallés, A. D. (2022). El Efecto ee Impuestos y Subsidios en la Difusión de Fotovoltaicos en Puerto Rico: Análisis con un Modelo de Agentes (Doctoral dissertation, University of Puerto Rico, Rio Piedras (Puerto Rico)).
  • van der Borgh, M., Schäfers, T., Lindgreen, A., & Di Benedetto, C. A. (2022). Moving the needle: Publishing academic-practitioner research in Industrial Marketing Management. Industrial Marketing Management.
  • van der Zwet, K., Barros, A. I., van Engers, T. M., & Sloot, P. M. (2022). Promises and pitfalls of computational modelling for insurgency conflicts. The Journal of Defense Modeling and Simulation, 15485129211073612.
  • Van Dyke Parunak, H. (2022). Learning Actor Preferences by Evolution. In Conference of the Computational Social Science Society of the Americas (pp. 85-97). Springer, Cham.
  • van Haeringen, E., Liistro, G., & Gerritsen, C. (2022). An Agent-Based Model of Emotion Contagion and Group Identification: A Case Study in the Field of Football Supporters. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 410-422). Springer, Cham.
  • van Klingeren, F. (2022). Using player types to understand cooperative behaviour under economic and sociocultural heterogeneity in common-pool resources: Evidence from lab experiments and agent-based models. PloS one, 17(5), e0268616.
  • van Roekel, G., & Smit, M. (2022). Herd behaviour and the emergence of clusters. Spatial Economic Analysis, 1-21.
  • Vander Linden, M. (2022). Iza Romanowska, Colin D. Wren and Stefani A. Crabtree. Agent-Based Modelling for Archaeology: Simulating the Complexity of Society (Santa Fe: The Santa Fe Institute Press. 2021. xiii and 429 pp., numerous illustr., pbk, ISBN 978-1-947864-25-2). European Journal of Archaeology, 25(4), 547-549.
  • Vargas Guarnizo, M. P., & Bohórquez Arévalo, L. E. (2022). Design of a simulation model that represents the collective intelligence genome of (malone et al., 2010). Tecnura, 26(72), 59-77.
  • Vargas Guarnizo, M. P., & Bohórquez Arévalo, L. E. (2022). Diseño de modelo de simulación que representa el genoma de inteligencia colectiva de Malone, Laubacher y Dellarocas. Tecnura, 26(72), 59-77.
  • Vega-Sánchez, R., & Herrera, J. M. (2022). Agent-based modelling for the study of shipwreck site formation processes: A theoretical framework and conceptual model. F1000Research, 11(1525), 1525.
  • Veldboer, T. (2022). A pure coordination game for the multi-agent card game The Mind (Doctoral dissertation).
  • Verhagen, P. (2022). Modelling the Basics of Roman Demography. Simulating Roman Economies: Theories, Methods, and Computational Models, 271.
  • Verleger, M., Stansbury, R., Akbas, M., & Craiger, P. (2022, August). An Undergraduate Research Experience in Unmanned Aircraft Systems (UAS) Cybersecurity–Outcomes and Lessons Learned. In 2022 ASEE Annual Conference & Exposition.
  • Vermeer, W., Gurkan, C., Hjorth, A., Benbow, N., Mustanski, B. M., Kern, D., Brown, C. H., & Wilensky, U. (2022). Agent-based model projections for reducing HIV infection among MSM: Prevention and care pathways to end the HIV epidemic in Chicago, Illinois. PloS one, 17(10), e0274288. https://doi.org/10.1371/journal.pone.0274288
  • Vidanaarachchi, R., Thompson, J., Godic, B., & McClure, R. AgentsX. jl—An Extended Julia Framework for Exploring Urban and Social Systems. In International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) (p. 16).
  • Vieira, L. S., & Laubenbacher, R. C. (2022). Computational models in systems biology: standards, dissemination, and best practices. Current Opinion in Biotechnology, 75, 102702.
  • Vinh, P. C. (2022). Context-awareness and Nature of Computation and Communication. Mobile Networks and Applications, 1-3.
  • Volpe, R., Catrini, P., Piacentino, A., & Fichera, A. (2022). An agent-based model to support the preliminary design and operation of heating and power grids with cogeneration units and photovoltaic panels in densely populated areas. Energy, 261, 125317.
  • Vriens, E., & Buskens, V. (2022). Sharing Risk under Heterogeneity: Participation in Settings of Incomplete Information. Journal of Artificial Societies & Social Simulation, 25(2).
  • Wagh, A., Fuhrmann, T., Eloy, A. A. D. S., Wolf, J., Bumbacher, E., Blikstein, P., & Wilkerson, M. (2022, June). MoDa: Designing a Tool to Interweave Computational Modeling with Real-world Data Analysis for Science Learning in Middle School. In Interaction Design and Children (pp. 206-211).
  • Wahyudiono, S., Darmawan, A. A., & Burhan, M. S. (2022). Pemodelan Shift Kerja dalam Proyek Konstruksi menggunakan NetLogo dalam meminimalkan Penyebaran Covid-19. Jurnal Ilmiah Universitas Batanghari Jambi, 22(2), 1256-1263.
  • Wan, S., & Liu, Y. (2022). A security detection approach based on autonomy-oriented user sensor in social recommendation network. International Journal of Distributed Sensor Networks, 18(3), 15501329221082415.
  • Wan, S., Chen, Z., Lyu, C., Li, R., Yue, Y., & Liu, Y. (2022). Research on disaster information dissemination based on social sensor networks. International Journal of Distributed Sensor Networks, 18(3), 15501329221080666.
  • Wang, H., Qiu, L., Chen, Z., Li, F., Jiang, P., Zhang, A., & Nie, X. (2022). Is rationality or herd more conducive to promoting farmers to protect wetlands? A hybrid interactive simulation. Habitat International, 128, 102647.
  • Wang, J., & Kim, Y. J. (2022). Evolutionary Characteristics of Microstructural Hydration and Chloride Diffusion in UHPC. Materials & Design, 111528.
  • Wang, K. (2022). Feasibility analysis and Research on Intelligent trusted modeling of network architecture software. In Journal of Physics: Conference Series (Vol. 2173, No. 1, p. 012027). IOP Publishing.
  • Wang, K. D., Cock, J. M., Käser, T., & Bumbacher, E. (2022). A systematic review of empirical studies using log data from open‐ended learning environments to measure science and engineering practices. British Journal of Educational Technology.
  • Wang, L. C., Yang, M., Li, Y., & Hou, Y. Q. (2022). A model of lane-changing intention induced by deceleration frequency in an automatic driving environment. Physica A: Statistical Mechanics and its Applications, 127905.
  • Wang, M., & Peng, S. (2022, October). COVID-19 Visualization Platform Based on Population Density Propagation Model. In 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA) (pp. 406-411). IEEE.
  • Wang, R., Ye, Z., Lu, M., & Hsu, S. C. (2022). Understanding post-pandemic work-from-home behaviours and community level energy reduction via agent-based modelling. Applied Energy, 322, 119433.
  • Wang, Y. (2022, May). Robust Information Center Implementation of College English Training under the Network Computer Aided System Environment Considering QoS. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 676-680). IEEE.
  • Wang, Y., Yao, X., Song, L., & Zhang, S. (2022). The Behaviour and Influence Mechanism of the Subject of IUR Collaborative Innovation: A Simulation Study Based on NetLogo. Discrete Dynamics in Nature and Society, 2022.
  • Wang, Z., & Jia, G. (2022). Sensitivity Analysis of Tsunami Evacuation Risk with Respect to Epistemic Uncertainty. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8(3), 04022037.
  • Wang, Z., & Jia, G. (2022). Simulation-based and risk-informed assessment of the effectiveness of tsunami evacuation routes using agent-based modeling: a case study of seaside, Oregon. International Journal of Disaster Risk Science, 13(1), 66-86.
  • Watson, J. W., Boyd, R., Dutta, R., Vasdekis, G., Walker, N. D., Roy, S., ... & Sibly, R. M. (2022). Incorporating environmental variability in a spatially-explicit individual-based model of European sea bass. Ecological Modelling, 466, 109878.
  • Watts, K. (2022). Using in vitro, in silico, and in-Classroom Techniques to Address the Gender Data Gap in Health Care.
  • Wei-qiang, O. U., & Bin, Z. H. U. (2022). Research on Dynamic Allocation Mechanism of Mainstream and Newstream Innovation Resources Based on ABMS. Operations Research and Management Science, 31(6), 182.
  • Wei, C. H. E. N., En-hua, H. U., Hong-mei, S. H. A. N., & Long, Z. H. A. N. G. (2022). Study on the Influence of Union-Enterprise Relation Modes on the Performance of Enterprises of Different Scales: A Multi-agent-based Simulation Model. Operations Research and Management Science, 31(1), 232.
  • Wei, L., Yang, Y., Wu, J., Long, C., & Lin, Y. B. (2022). A Bidirectional Trust Model for Service Delegation in Social Internet of Things. Future Internet, 14(5), 135.
  • Wei, Y., Jang, N., Zhang, Z., Zeng, M., & Yang, Z. Research on combat simulation agent modelling methods combined with reinforcement learning. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-12.
  • Weisberg, M. (2022).Understanding the Emergence of Population Behavior in Individual-Based Models. Philosophy of Science.
  • Weisburd, D., Wolfowicz, M., Hasisi, B., Paolucci, M., & Andrighetto, G. (2022). What is the best approach for preventing recruitment to terrorism? Findings from ABM experiments in social and situational prevention. Criminology & Public Policy, 21(2), 461-485.
  • Werntz, S., & Oppenheimer, D. (2022). In the Dark: Agent-Based Modeling of Uninformed Individuals within Polarized Groups. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 44, No. 44).
  • Weyer, J., Philipp, M., & Adelt, F. Agent-Based Modelling of Infrastructure Systems. Metropolitan Research, 155.
  • Whitenack, L., & Mahabir, R. (2022, April). A tool for optimizing the efficiency of drive-thru services. In 2022 Systems and Information Engineering Design Symposium (SIEDS) (pp. 151-156). IEEE.
  • Whitney, E. (2022). Uses of Agent-Based Modeling and Simulation to Support Business Decision-Making for Healthcare Industry Leadership in the United States of America (Doctoral dissertation, Northcentral University).
  • Widiyanto, S., Adi, D., & Soans, R. V. (2022). Agent-Based Simulation Disaster Evacuation Awareness on Night Situation in Aceh. IPTEK The Journal of Engineering, 8(1), 36-43.
  • Wijermans, N., Schill, C., Lindahl, T., & Schlüter, M. (2022). Combining approaches: Looking behind the scenes of integrating multiple types of evidence from controlled behavioural experiments through agent-based modelling. International Journal of Social Research Methodology, 1-13.
  • Wilson, A. (2022). Positioning Computational Modelling in Roman Studies. Simulating Roman Economies: Theories, Methods, and Computational Models, 308.
  • Wing, A. K. (2022). Building a Framework for Apron Planning, Design, Optimization, Future Proofing and Expansion (Doctoral dissertation, University of South Alabama).
  • Winzar, H., Baumann, C., Soboleva, A., Park, S. H., & Pitt, D. (2022). Competitive Productivity (CP) as an emergent phenomenon: Methods for modelling micro, meso, and macro levels. International Journal of Hospitality Management, 105, 103252.
  • Witeck, G. R., Rocha, A. M. A., Silva, G. O., Silva, A., Durães, D., & Machado, J. (2022). A Bibliometric Review and Analysis of Traffic Lights Optimization. In International Conference on Computational Science and Its Applications (pp. 43-54). Springer, Cham.
  • Wojcieszak, M., Sobkowicz, P., Yu, X., & Bulat, B. (2022). What Information Drives Political Polarization? Comparing the Effects of In-group Praise, Out-group Derogation, and Evidence-based Communications on Polarization. The International Journal of Press/Politics, 27(2), 325-352.
  • Wood, N. Grassroots Citizen Participation with Government Agencies in Disaster Response Activities. The Cupola, 342.
  • Wozniak, M., & Dziecielski, M. (2022). Should I Turn or Should I Go? Simulation of Pedestrian Behaviour in an Urban Environment. Journal of Simulation, 1-15.
  • Wright, I. D., Reimherr, M., & Liechty, J. A Machine Learning Approach to Classification for Traders in Financial Markets. Stat, e465.
  • Wu, R., Han, Y., Guo, K., & Liu, K. (2022, July). Information dissemination model of recommendation platform based on network community. In 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC) (pp. 633-641). IEEE.
  • Wu, S., Lei, Y., & Jin, W. (2022). An Interdisciplinary Approach to Quantify the Human Disaster Risk Perception and Its Influence on the Population at Risk: A Case Study of Longchi Town, China. International Journal of Environmental Research and Public Health, 19(24), 16393.
  • Wu, S., Peel, A., Zhao, L., Horn, M., & Wilensky, U. (2022). A professional development that helps teachers integrate computational thinking into their STEM classrooms. Innovations in Science Teacher Education 7(2).
  • Wu, S., Swanson, H., Sherin, B., & Wilensky, U. (2022). Investigating Student Learning about Disease Spread and Prevention in the Context of Agent-Based Computational Modeling. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Wu, S., Swanson, H., Sherin, B., & Wilensky, U. (2022). Students’ Prior Knowledge of Disease Spread and Prevention. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Wu, Z., & Forget, G. (2022). PlanktonIndividuals. jl: A GPU supported individual-based phytoplankton life cycle model. Journal of Open Source Software, 7(73), 4207.
  • Wu, Z., & Huang, S. (2022, December). The Impact of Heterogeneous Reputation Evaluation Laws on Cooperation Based on Net Logo. In 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) (pp. 123-133). Atlantis Press.
  • Wurzer, G., Reismann, M., Marschnigg, C., Dorfmeister, A., Tauböck, S., Ledermüller, K., & Spörk, J. (2022). PASSt-A: Agent-based student analytics aimed at improved feasibility and study success. IFAC-PapersOnLine, 55(20), 361-366.
  • Xi, J., Chan, W. (2022). Reopening Universities without Testing During COVID-19: Evaluating a Possible Alternative Strategy in Low Risk Countries. Archives of Clinical and Biomedical Research 6 (2022): 971-981.
  • Xia, M., Lu, Z., Xu, L., Shi, Y., Ma, Q., Wu, Y., & Sheng, B. (2022). Impact of Regional Differences in Risk Attitude on the Power Law at the Urban Scale. Land, 11(10), 1791.
  • Xiang, L., & Diamond, S. (2022). Developing and Using Computer Models to Understand Epidemics Breadcrumb. The Science Teacher, 89(3).
  • Xiao, J. (2022). A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware (Doctoral dissertation, Technische Universität München).
  • Xiaobei, X. U., & Hongping, Y. U. A. N. An Agent-based Modeling Approach for Investigating the Diffusion of BIM Technology. Industrial Engineering Journal, 24(6), 57.
  • Xie, J., Tian, S., Liu, J., Cao, R., Yue, P., Cai, X., ... & Zhang, D. K. (2022). Dual role of the nasal microbiota in neurological diseases—An unignorable risk factor or a potential therapy carrier. Pharmacological Research, 106189.
  • Xin-gang, Z., Yi, Z., Hui, W., & Zhen, W. (2022). How can the cost and effectiveness of renewable portfolio standards be coordinated? Incentive mechanism design from the coevolution perspective. Renewable and Sustainable Energy Reviews, 158, 112096.
  • Xin, Z., Li, J., Wang, Z., & Li, J. (2023). A Hardware-in-the-Loop Simulation Platform for UAV Swarm Decision-Making. In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control (pp. 189-199). Springer, Singapore.
  • Xiong, M., Wang, Y., & Cheng, Z. (2021, December). Research on Modeling and Simulation of Information Cocoon Based on Opinion Dynamics. In 2021 The 9th International Conference on Information Technology: IoT and Smart City (pp. 161-167).
  • Xu, H., Wang, Y., & Yan, Y. (2022). Study on the Optimum Process Conditions for Preparation of C4 Olefins by Ethanol Coupling. Academic Journal of Science and Technology, 3(1), 50-57.
  • Xu, L., Ding, R., & Wang, L. (2022). How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles. Scientometrics, 1-27.
  • Xu, Z., Zhang, H., & Huang, Z. (2022). A Continuous Markov-Chain Model for the Simulation of COVID-19 Epidemic Dynamics. Biology, 11(2), 190.
  • Xue, X., Yu, X. N., Zhou, D. Y., Wang, X., Zhou, Z. B., & Wang, F. Y. (2022). Computational Experiments: Past, Present and Future. arXiv preprint arXiv:2202.13690.
  • Yadav, A., & Lachney, M. (2022). Teaching with, about, and through technology: Visions for the future of teacher education. Journal of Technology and Teacher Education, 30(2), 189-200.
  • Yadav, A., Caeli, E. N., Ocak, C., & Macann, V. (2022, July). Teacher Education and Computational Thinking: Measuring Pre-service Teacher Conceptions and Attitudes. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1 (pp. 547-553).
  • Yang, H., Wu, X., Zhao, S., Madani, H., Chen, J., & Chen, Y. (2022). An Agent-based Model Study on Subsidy Fraud in Technological Transition. In ICAART (1) (pp. 353-358).
  • Yang, J. (2022). Xi, Wai Kin (Victor) Chan. Reopening Universities without Testing During COVID-19: Evaluating a Possible Alternative Strategy in Low Risk Countries. Archives of Clinical and Biomedical Research, 6, 971-981.
  • Yang, K., Yang, H., Zhang, J., & Kang, R. (2022). Effects on Taxiing Conflicts at Intersections by Pilots’ Sensitive Speed Adjustment. Aerospace, 9(6), 288.
  • Yang, L. (2022). Double-edged Effects of Pricing on Diffusion of Green Products. Journal of Cleaner Production, 132109.
  • Yang, L., Iwami, M., Chen, Y., Wu, M., & van Dam, K. H. (2022). Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review. Progress in Planning, 100657.
  • Yang, R., Wang, A., & Bai, Y. Evolutionary Mechanism of Social Interaction and Green Travel Behavior of Urban Residents: Based on Scale-Free Network. Available at SSRN 4094750.
  • Yang, Y., Yin, J., Wang, D., Liu, Y., Lu, Y., Zhang, W., & Xu, S. (2022). ABM-based emergency evacuation modelling during urban pluvial floods: A “7.20” pluvial flood event study in Zhengzhou, Henan Province. Science China Earth Sciences, 1-10.
  • Yang, Z., Jinling, L., Haixiang, G., & Weiming, C. (2022). Research on Emotional Contagion and Intervention Strategy of Indoor Evacuation Based on Risk Perception. Journal of System Simulation, 34(12), 2691.
  • Yao, Z., Wu, X., & Li, N. (2022, May). Simulation of indirect-reciprocity-based lane-changing in Internet of Vehicles environment. In 2nd International Conference on Internet of Things and Smart City (IoTSC 2022) (Vol. 12249, pp. 17-23). SPIE.
  • Yazan, D. M., van Capelleveen, G., & Fraccascia, L. (2022). Decision-Support Tools for Smart Transition to Circular Economy. In Smart Industry–Better Management (Vol. 28, pp. 151-169). Emerald Publishing Limited.
  • Ye, X., Du, J., Han, Y., Newman, G., Retchless, D., Zou, L., ... & Cai, Z. (2022). Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda. Journal of Planning Literature, 08854122221137861.
  • Yegenoglu, A., Subramoney, A., Hater, T., Jimenez-Romero, C., Klijn, W., Martin, A. P., ... & Diaz-Pier, S. (2022). Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn. arXiv preprint arXiv:2202.13822.
  • Yi-Chen, H., Tak-Yu, C., & Chie, B. T. (2022). The Effect of Dishonest Sellers on E-commerce: An Agent-Based Modeling Approach. Advances in Management and Applied Economics, 12(4).
  • Yin, D., & Gong, B. (2022). Auto-Adaptive Trust Measurement Model Based on Multidimensional Decision-Making Attributes for Internet of Vehicles. Wireless Communications and Mobile Computing, 2022.
  • Yin, S., Xu, Y., Xu, M., de Jong, M. C., Huisman, M. R., Contina, A., ... & de Boer, W. F. (2022). Habitat loss exacerbates pathogen spread: An Agent-based model of avian influenza infection in migratory waterfowl. PLoS computational biology, 18(8), e1009577.
  • Yu, S. (2022). Agent-based modelling using survey data to simulate occupancy patterns and occupant interactions for workplace design. Building and Environment, 224, 109519.
  • Yu, Y., Yazan, D. M., Junjan, V., & Iacob, M. E. (2022). Circular economy in the construction industry: A review of decision support tools based on Information & Communication Technologies. Journal of Cleaner Production, 131335.
  • Zadbood, A., & Hoffenson, S. (2022). Social Network Word-of-Mouth Integrated into Agent-Based Design for Market Systems Modeling. Journal of Mechanical Design, 1-17.
  • Zakaria, N. (2022). Action network: a probabilistic graphical model for social simulation. SIMULATION, 98(4), 335-346.
  • Zargar, S. H., Sadeghi, J., & Brown, N. C. (2022). Agent-based modelling for early-stage optimization of spatial structures. International Journal of Architectural Computing, 14780771221143493.
  • Zauner, G., & Weidinger, W. (2022). Modelling and. The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics, 347.
  • Zejin, T., & Pyung-hoi, K. O. O. (2022). A Study Of Supply Chain Risk Decisions With Consideration Of Risk Preference Based On Abm Simulation. 한국경영과학회 학술대회논문집, 4151-4156.
  • Zellner, M. L., Milz, D., Lyons, L., Hoch, C. J., & Radinsky, J. (2022). Finding the Balance Between Simplicity and Realism in Participatory Modeling for Environmental Planning. Environmental Modelling & Software, 157, 105481.
  • Zhang, B., Zhang, H., & Yang, X. (2023). Research on Social Atomization Risk and Governance Decision Making in Emerging Community. In International Conference on Decision Science & Management (pp. 119-126). Springer, Singapore.
  • Zhang, C., Wu, X., Zhao, S., Madani, H., Chen, J., & Chen, Y. (2022). Simulation study on the low carbon transition process in Japan’s electricity market. Green Technologies and Sustainability, 100006.
  • Zhang, G., Li, H., He, R., & Lu, P. (2022). Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions. Complex & Intelligent Systems, 8(2), 1369-1387.
  • Zhang, H., Xiong, H., Wang, G., & Jiang, P. (2022). How institutional pressures improve environmental management performance in construction projects: an agent-based simulation approach. Environment, Development and Sustainability, 1-31.
  • Zhang, J., & Robinson, D. T. (2022). Investigating path dependence and spatial characteristics for retail success using location allocation and agent-based approaches. Computers, Environment and Urban Systems, 94, 101798.
  • Zhang, L., & Jiang, X. (2022, September). Comprehensive evaluation model based on data and analysis system. In International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022) (Vol. 12332, pp. 458-464). SPIE.
  • Zhang, Q., Wu, X., & Chen, Y. (2022). Is economic crisis an opportunity for realizing the low-carbon transition? A simulation study on the interaction between economic cycle and energy regulation policy. Energy Policy, 168, 113114.
  • Zhang, W., Yuan, J., Zhang, H. N., & Li, B. Y. Simulation research on blockchain bidding model. In Proc. of SPIE Vol (Vol. 12160, pp. 121600Q-1).
  • Zhang, Y., Gao, J., Bilgihan, A., & Lorenz, M. (2022). A holistic assessment of eWOM management effectiveness with agent-based modeling. International Journal of Contemporary Hospitality Management, (ahead-of-print).
  • Zhang, Y., Xu, L., Gong, Z., Wang, Y., & Wang, Z. (2022). Study Blockchain-Based Supply Chain Finance System of the Construction Industrialization. In International Symposium on Advancement of Construction Management and Real Estate (pp. 804-816). Springer, Singapore.
  • Zhao, B., Lyu, X., & Qi, N. (2022). Construction and Optimization of Transboundary Business Financial Credit Network in the Era of 5G Communication. Wireless Communications and Mobile Computing, 2022.
  • Zhao, L., Peel, A., Horn, M. S., & Wilensky, U. (2022). Student Perceptions of Computational Thinking Practices in a CT-integrated Environmental Science Unit. the National Association for Research in Science Teaching (NARST) 2022. Vancouver, British Columbia.
  • Zhao, L., Peel, A., Horn, M.S., Wilensky, U. (2022). Student perceptions of computational thinking practices in a CT-integrated environmental science unit. Paper accepted to the Annual Meeting of the American Educational Research Association (AERA) 2022. San Diego, CA.
  • Zhao, N., Chong, H. Y., & Li, Q. (2022). Agent-based modelling of helping behaviour diffusion in project teams as an evolutionary process. Journal of Simulation, 1-18.
  • Zhao, N., Lei, C., Liu, H., & Wu, C. (2022). Improving the Effectiveness of Organisational Collaborative Innovation in Megaprojects: An Agent-Based Modelling Approach. Sustainability, 14(15), 9070.
  • Zhong, J., Li, D., Huang, Z., Lu, C., & Cai, W. (2022). Data-driven Crowd Modeling Techniques: A Survey. ACM Transactions on Modeling and Computer Simulation (TOMACS), 32(1), 1-33.
  • Zhong, Q., Hilbert, M., & Frey, S. (2022). Breaking the Structural Reinforcement: An Agent-Based Model on Cultural Consumption and Social Relations. Social Science Computer Review, 08944393211056501.
  • Zhou, J., Kofinas, G. P., Kielland, K., Boone, R. B., Prugh, L., & Tape, K. D. (2022). Climate change, moose, and subsistence harvest: social-ecological assessment of Nuiqsut, Alaska. Ecology and Society, 27(3), 29.
  • Zhou, Q. (2022, October). Effectiveness of Different Control Measures on the Spread of COVID-19. In Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences (pp. 220-226).
  • Zhou, Y., Shi, S., & Wang, S. (2022). A Multi-Agent Model-Based Evolutionary Model of Port Service Value Network and Decision Preferences. Sustainability, 14(6), 3565.
  • Zhu, G., Xing, W., Popov, V., Li, Y., Xie, C., & Horwitz, P. (2022). Using Learning Analytics to Understand Students' Discourse and Behaviors in STEM Education. In Artificial Intelligence in STEM Education (pp. 225-240). CRC Press.
  • Zhuo, Q. (2022, October). Predicting the Future of Achieving Herd Immunity in New York City. In Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences (pp. 579-582).
  • Živojinović, T., & Zornić, N. (2022). Anticipating the impact of sharing economy drivers on consumer intention for using a sharing economy service. Journal of East European Management Studies, 27(2), 233-258.
  • Zu, C., Zeng, H., Zhou, X. (2022).Computational Simulation of Team Creativity: the Benefit of Member Flow. Frontiers in Psychology.
  • Zuccotti, C. V., Lorenz, J., Paolillo, R., Rodríguez Sánchez, A., & Serka, S. (2022). Exploring the dynamics of neighbourhood ethnic segregation with agent-based modelling: an empirical application to Bradford, UK. Journal of Ethnic and Migration Studies, 1-22.
  • Zusai, D., Sawa, R., Cheung, M. W., Lahkar, R., & Wu, J. (2022). Tributes to Bill Sandholm. Journal of Dynamics and Games.
  • Zvereva, O., Ershova, I., Goldstein, S., Shangina, E., & Tebaikina, N. (2022, April). Agent-based model implementing for investigation of economic agents’ behavior influence on autonomous community viability. In AIP Conference Proceedings (Vol. 2425, No. 1, p. 110015). AIP Publishing LLC.
  • Дубовський, А. А. (2022). АНАЛІЗ ПРИНЦИПІВ, МЕТОДІВ І ПРОГРАМНИХ ЗАСОБІВ ПРОГНОЗУВАННЯ ВАКАНСІЙ НА РИНКУ ПРАЦІ. Тези доповідей VІ Міжнародної науково-практичної конференції «Інформаційні технології в освіті, науці і техніці»(ІТОНТ-2022),(Черкаси, 23-25 червня 2022 р.)[Електронний ресурс]. Черкаси: ЧДТУ, 2022. 220 с., 107.
  • ЕРШОВ, Н. М. (2022). РАЗРАБОТКА И ИССЛЕДОВАНИЕ РАСПРЕДЕЛЕННЫХ АЛГОРИТМОВ УПРАВЛЕНИЯ СИСТЕМАМИ РОЕВОГО ИНТЕЛЛЕКТА. COMPUTATIONAL NANOTECHNOLOGY Учредители: ООО" Издательский дом" Юр-ВАК", 9(2), 21-34.
  • ИЛЬИНСКИЙ, А. И. Учредители: Богомолов Александр Иванович. ХРОНОЭКОНОМИКА Учредители: Богомолов Александр Иванович, (4), 62-65.
  • Кабанов, А. А. (2022). МОДЕЛЬНО-ОРИЕНТИРОВАННАЯ РАЗРАБОТКА ПРОИЗВОДСТВ РАКЕТНО-КОСМИЧЕСКИХ СИСТЕМ В КОНТЕКСТЕ ИЗДЕЛИЙ В АЭРОКОСМИЧЕСКИХ ВУЗАХ. Космические аппараты и технологии, 6(3 (41)), 195-205.
  • Карчевський, М. В. (2022). ОБЧИСЛЮВАЛЬНЕ КРИМІНОЛОГІЧНЕ АРГУМЕНТУВАННЯ: ПОНЯТТЯ, МОЖЛИВОСТІ ТА ПЕРСПЕКТИВИ ВИКОРИСТАННЯ. Вісник Луганського державного університету внутрішніх справ імені ЕО Дідоренка, 4(100), 112-126.
  • Макаров, В. Л., Бахтизин, А. Р., Бекларян, Г. Л., Акопов, А. С., & Стрелковский, Н. В. (2022). МОДЕЛИРОВАНИЕ МИГРАЦИОННЫХ И ДЕМОГРАФИЧЕСКИХ ПРОЦЕССОВ С ИСПОЛЬЗОВАНИЕМ FLAME GPU. Бизнес-информатика, 16(1), 7-21.
  • Писковая, Е. А., & Димов, А. В. (2022). ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ ПЕРЕВОЗОЧНОГО ПРОЦЕССА НА ЖЕЛЕЗНОДОРОЖНОМ ТРАНСПОРТЕ. In Повышение управленческого, экономического, социального и инновационно-технического потенциала предприятий, отраслей и народно-хозяйственных комплексов (pp. 212-215).
  • САМОЙЛОВА, К. В., & ЗАМЯТИНА, Е. Б. (2022). Архитектура программной системы для проектирования надежных бизнес-процессов. Труды Института системного программирования РАН, 34(2), 87-76.
  • Сарнацький, В. В., & Баклан, І. В. МЕТОДИ ТА ЗАСОБИ МОДЕЛЮВАННЯ РОЗПОВСЮДЖЕННЯ ІНФЕКЦІЙНИХ ЗАХВОРЮВАНЬ.
  • Сіницький, М. Є. ПРОГРАМНЕ ЗАБЕЗПЕЧЕННЯ ЕКОНОМІСТІВ У ЦИФРОВУ ЕПОХУ. Стратегія розвитку України: фінансово-економічний та гуманітарний аспекти: матеріали VIII Міжнародної науково-практичної конференції. Київ,«Інформаційно-аналітичне агентство», 2021. 309 c., 251.
  • اسدی, رحمان, & شاه محمدی درمنی. مدل‌سازی و شبیه‌سازی مراحل مختلف رشد و پاسخ به درمان سرطان سرویکس. مجله انفورماتیک سلامت و زیست پزشکی, 8(2), 140-152.‎
  • جعفری, جیریایی, مسگری, & محمد سعدی. مدلسازی عامل مبنای گسترش بیماری مالاریا. Journal of Geomatics Science and Technology, 11(1), 205-219.‎
  • حسینی, آذر, عادل, آذرفر, & عبادی. (2022). شناسایی و ارزیابی ریسک‌های زنجیره تأمین صنعت بیمه با رویکرد شبیه‌سازی عامل بنیان. پژوهش های پیشرفت: سیستم ها و راهبردها, 2(4), 11-44.‎
  • رحمان آرش. فشار همتایان در استعمال دخانیات و تاثیر آن بر رفاه اجتماعی؛ یک بررسی با مدل سازی و شبیه سازی مبتنی بر عامل.
  • رضائی, & وحیدنیا. (2022). راهکارهای بازدارنده برای جلوگیری از سیل به کمک سنجش از دور و رویکردهای تلفیقی منطق فازی و مدل سازی عامل مبنا. فصلنامه علمی-پژوهشی اطلاعات جغرافیایی «سپهر», 31(121), 111-125.
  • رمضانی, محمد, میرزاحسین, رصافی, & امیرعباس. (2022). مدل عامل-مبنای سوخت‌گیری وسایل نقلیه‌ی شخصی با رویکرد مدیریت تقاضا و مقایسه‌ی نتایج آن با رجحان بیان‌شده‌ی کاربران: مطالعه‌ی موردی کلان‌شهر تهران. نشریه مهندسی عمران امیرکبیر.‎
  • عباسی سیر, سلمان, هاشمی گهر, فیضی, & عمّار. (2022). مدل‌سازی عامل‌بنیان رفتار سهامداران در بازار اوراق بهادار تهران (مورد مطالعه: شرکت فولاد مبارکه اصفهان). پژوهش های نوین در تصمیم گیری, 7(1), 88-114.‎
  • مینایی, مژده, وحیدنیا, & محمد حسن. (2022). راهکارهای بازدارنده برای جلوگیری از سیل به کمک سنجش‌ازدور و مدل‌سازی عامل مبنا (مطالعه موردی: شهرستان شوش). مخاطرات محیط طبیعی.‎
  • مینایی, وحیدنیا, & محمد حسن. (2022). راهکارهای بازدارنده برای جلوگیری از سیل به کمک سنجش‌ازدور و مدل‌سازی عامل مبنا (مطالعه موردی: شهرستان شوش). مخاطرات محیط طبیعی, 1-1.‎
  • 박원범, & 이문걸. (2022, June). 대대급 무인기 이동표적 탐색방안. In 2022 년 한국산업경영시스템학회 춘계학술대회 (pp. 575-579).
  • 정혜영, 서보순, 손유진, 김미진, 김병만, & 손일수. (2022). 행위자기반모형 (ABM) 을 활용한 저출생 현상 탐색. 교육혁신연구, 32, 139-168.
  • 冯楠, 任彬彬, 黄梓宸, & 李敏强. (2022). 数字经济下工业互联网平台信息共享激励机制研究. 北京交通大学学报 (社会科学版), 21(02), 1.
  • 加藤大望, 矢田昇平, & 倉橋節也. (2022). Multi-agent system を用いた工場内 AGV 搬送システムの解析. In 人工知能学会全国大会論文集 第 36 回 (2022) (pp. 3O4GS502-3O4GS502). 一般社団法人 人工知能学会.
  • 席周慧, 孟德霖, & 赵继军. (2022). 钻石公主号邮轮上 COVID-19 传播动态的研究. 复杂系统与复杂性科学, 19(1), 67-73.
  • 张鑫. (2022). 信息激励对居民垃圾分类行为影响机理及政策仿真研究 (Master's thesis, 中国矿业大学).
  • 曾杨, 黎金玲, 郭海湘, & 陈卫明. (2022). 基于风险感知的室内疏散情绪传染与干预策略研究. 系统仿真学报, 34(12), 2691.
  • 李春发, 曹颖颖, 王聪, & 郝琳娜. (2022). 平台规制下直播电商三方策略演化博弈与仿真. 复杂系统与复杂性科学, 19(1), 34-44.
  • 毕崇武, 贠婕, 周静虹, & 叶光辉. (2022). 引文内容视角下的引文网络知识流动网络分析. 情报科学, 39(1), 79.
  • 毕崇武, 贠婕, 周静虹, & 叶光辉. (2022). 引文内容视角下的引文网络知识流动网络分析. 情报科学, 39(1), 79.
  • 犬飼佳吾. (2022). マルチエージェントのための行動科学: 実験経済学からのアプローチ. 行動経済学, 15, 1-3.
  • 王奇, 王刚桥, 陈永强, & 刘奕. 面向社会计算的集成建模方法与应用系统. 计算机科学, 49(4), 25-29.
  • 王荪馨, 王彦明, 孔杰, & 申高攀. (2022). 一种柔性缩放的群机器人形态自修复方法. 西北工业大学学报, 40(1).
  • 范春梅, 吴阳, & 李华强. (2022). 奖惩机制和游客参与下的低价游监管——基于三方演化博弈视角. 管理评论, 34(3), 290.
  • 薛霄, 于湘凝, 周德雨, 彭超, 王晓, 周长兵, & 王飞跃. (2022). 计算实验方法的溯源, 现状与展望. 自动化学报, 48, 1-26.
  • 袁博. (2022). 基于动态博弈的网络防御策略选取方法研究 (Master's thesis, 郑州轻工业大学).
  • 西田遼, 重中秀介, 加藤優作, & 大西正輝. (2022). 群集シミュレーションによる歩行空間設計と制御に関する研究動向. 人工知能学会論文誌, 37(2), J-LB1_1.

2021

  • Castillo Osorio, E. E., Seo, M. S., & Yoo, H. H. (2021). Analysis of suitable evacuation routes through multi-agent system simulation within buildings. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 39(5), 265-278.
  • Martin, A. A., & Barnas, A. F. (2022). Sitting ducks: Strategies to increase recruitment in common eiders (Somateria mollissima) facing polar bear (Ursus maritimus) predation.
  • Abbasi, K. M., Khan, T. A., & ul Haq, I. (2021). Modeling-framework for model-based software engineering of complex Internet of things systems. Mathematical Biosciences and Engineering, 18(6), 9312-9335.
  • Abbott, R., & Lim, J. (2021, July). PyLogo: A Python Reimplementation of (Much of) NetLogo. In SIMULTECH (pp. 199-206).
  • Abd Elhamid, M., Abdelaziz, T., & Bassioni, H. (2021). FACTORS AFFECTING THE THICKNESS OF REPLACEMENT LAYER ON MEDIUM CLAY. ASEAN Engineering Journal, 11(4), 232-245.
  • Abdollahian, M., Chang, Y. L., & Lee, Y. Y. (2021, September). A Complex Adaptive System Approach for Anticipating Technology Diffusion, Income Inequality and Economic Recovery. In International Conference on Computational Science and Its Applications (pp. 251-262). Springer, Cham.
  • Abrahamson, D. (2021). Grasp actually: An evolutionist argument for enactivist mathematics education. Human Development. https://doi.org/10.1159/000515680
  • Abrahamson, D., Worsley, M., Pardos, Z., & Ou, L. (2021). Learning analytics of embodied design: Enhancing synergy. International Journal of Child-Computer Interaction, 100409. https://doi.org/10.1016/j.ijcci.2021.100409
  • Abrami, G., Daré, W. S., Ducrot, R., Salliou, N., & Bommel, P. Participatory modelling. (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems, 189.
  • Abrica, N. L., & Ontiveros, J. A. A. A MODEL FOR REBELLION INFLUENCED BY OPINION AND APPLIED PUBLIC POLICY. Computational Sociology, 201.
  • Abuga, D., & Raghava, N. S. (2021). Real-time Smart Garbage Bin Mechanism for Solid Waste Management in Smart Cities. Sustainable Cities and Society, 103347.
  • Acheampong, R. A., & Asabere, S. B. (2021). Simulating the co-emergence of urban spatial structure and commute patterns in an African metropolis: A geospatial agent-based model. Habitat International, 110, 102343.
  • Agarwal, Ankit. (2021). Agent-based model of broadband adoption in unserved and underserved areas. Masters Theses. 7973.
  • Agha-Hoseinali-Shirazi, M., Bozorg-Haddad, O., Laituri, M., & DeAngelis, D. (2021). Application of Agent Base Modeling in Water Resources Management and Planning. In Essential Tools for Water Resources Analysis, Planning, and Management (pp. 177-216).
  • Agustin, A. D., Ferrer, J. C. M., Bolingot, H. J. M., Celestre, J. D. I., Oppus, C. M., & Monje, J. C. N. (2021, November). Agent-based Modeling of COVID-19 Infection Rate vis-à-vis the Philippine Government Community Quarantine and Face Covering Measures. In 2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) (pp. 1-8). IEEE.
  • Ahimbisibwe, V., Lippe, M., Auch, E., Groeneveld, J., Tumwebaze, S. B., & Berger, U. (2021). Understanding smallholder farmer decision making in forest land restoration using agent-based modeling. Socio-Environmental Systems Modelling, 3, 18036-18036.
  • Ahn, S., & Yun, S. J. (2021). A Study on Residents' Participation in Rural Tourism Project Using an Agent-Based Model-Based on the Theory of Planned Behavior. Journal of Korean Society of Rural Planning, 27(2), 77-89.
  • Ahrweiler, P. (2021). Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019. Springer Nature.
  • Ahumada-Tello, E., & Ramos, K. (2021). Complejidad Social y Educación Superior. Análisis Crítico Basado en Agentes. Revista Ciencias de la Complejidad, 2(Edición Especial), 51-59.
  • Akundi, A., & Smith, E. (2021). Quantitative Characterization of Complex Systems—An Information Theoretic Approach. Applied System Innovation, 4(4), 99.
  • Al-Gharaibeh, R. S., & Ali, M. Z. (2021). Knowledge Sharing Framework: a Game-Theoretic Approach. Journal of the Knowledge Economy, 1-35.
  • Al-Najjar, A. A. M. (2021). Optimizing MANETs Network Lifetime Using a Proactive Clustering Algorithm. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(2), 3280-3292.
  • Alajlan, A., Edris, A., Heckendorn, R. B., & Soule, T. (2021). Using Neural Networks and Genetic Algorithms for Predicting Human Movement in Crowds. In Advances in Artificial Intelligence and Applied Cognitive Computing (pp. 353-368). Springer, Cham.
  • Alam, M. D. (2021). Development of a Mass Evacuation Decision Support Tool.
  • Albarrán, J. C., & Ramırez, E. C. Digital Twin in Water Supply Systems to Industry 4.0: The Holonic Production Unit. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA LATIN AMERICA 2021, 42.
  • Albuquerque, E. P. D. (2021). The Creation and Diffusion of Knowledge-an Agent Based Modelling Approach. UCD Centre for Economic Research Working Paper Series; WP2021/13. University College Dublin. School of Economics. 1-22.
  • Alcon, A. G. (2021). Alcon Reinforces Strength of Industry-Leading Ophthalmology Portfolio with Largest Surgical Device Scientific Presence at ASCRS 2021 Folgen.
  • Alexander, S. O. (2021). Evaluation, Communication, and Integration of Climate Information across Scales to Foster Local Decision-Making and Support Community Resilience (Doctoral dissertation, The University of Wisconsin-Madison).
  • Alfaro, J. F., & Miller, S. A. (2021). Analysis of electrification strategies for rural renewable electrification in developing countries using agent-based models. Energy for Sustainable Development, 61, 89-103.
  • Alimboyong, C. R. (2021). Modeling virus spread on a network using NetLogo for optimum network management. Indonesian Journal of Electrical Engineering and Computer Science, 23(1), 370-377.
  • Alipour, M., Salim, H., Stewart, R. A., & Sahin, O. (2021). Residential solar photovoltaic adoption behaviour: End-to-end review of theories, methods and approaches. Renewable Energy.
  • Allioui, H., Sadgal, M., & Elfazziki, A. (2021). Optimized control for medical image segmentation: improved multi-agent systems agreements using Particle Swarm Optimization. Journal of Ambient Intelligence and Humanized Computing, 1-19.
  • Allison, A. E., Dickson, M. E., Fisher, K. T., & Thrush, S. F. Communicating drivers of environmental change through transdisciplinary human‐environment modelling. Earth's Future, e2020EF001918.
  • Alsharhan, A. M. (2021). Survey of Agent-Based Simulations for Modelling COVID-19 Pandemic. Advances in Science, Technology and Engineering Systems Journal, 6(2), 439-447.
  • Altun, K., ALTUNTAŞ, S., & DERELİ, T. An interaction-oriented multi-agent SIR model to assess the spread of SARS-CoV-2. Hacettepe Journal of Mathematics and Statistics, 1-12.
  • Álvarez-Pomar, L., & Rojas-Galeano, S. (2021). Impact of Personal Protection Habits on the Spread of Pandemics: Insights from an Agent-Based Model. The Scientific World Journal, 2021.
  • Alzoor, F. S., Ezzeldin, M., Mohamed, M., & El-Dakhakhni, W. (2021). Prioritizing Bridge Rehabilitation Plans through Systemic Risk-Guided Classifications. Journal of Bridge Engineering, 26(7), 04021038.
  • Ambrosius, F. H., Kramer, M. R., Spiegel, A., Bokkers, E. A., Bock, B. B., & Hofstede, G. J. (2022). Diffusion of organic farming among Dutch pig farmers: An agent-based model. Agricultural Systems, 197, 103336.
  • Ambrosius, F. H., Kramer, M. R., Spiegel, A., Bokkers, E. A., Bock, B. B., & Hofstede, G. J. UNDERSTANDING DIFFUSION OF ORGANIC FARMING AMONG DUTCH PIG FARMERS: AN AGENT-BASED MODEL. Transition through markets, 93.
  • Amparore, E. G., Beccuti, M., Castagno, P., Franceschinis, G., Pennisi, M., & Pernice, S. (2021, December). Multiformalism modeling and simulation of immune system mechanisms. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 3259-3266). IEEE.
  • An, G., & Cockrell, R. C. (2021). Agent-Based Modeling of Systemic Inflammation: A Pathway Toward Controlling Sepsis. In Sepsis (pp. 231-257). Humana, New York, NY.
  • An, L., Grimm, V., Sullivan, A., TurnerII, B. L., Malleson, N., Heppenstall, A., ... & Tang, W. (2021). Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457, 109685.
  • An, S., Broniec, W., Rugaber, S., Weigel, E., Hammock, J., & Goel, A. (2021). Recognizing Novice Learner’s Modeling Behaviors. In Procs. 19th International Conference on Intelligent Tutoring Systems.
  • An, X., Qi, L., Zhang, J., & Jiang, X. (2021). Research on dual innovation incentive mechanism in terms of organizations’ differential knowledge absorptive capacity. Plos one, 16(8), e0256751.
  • Andrade, J. P. B., Maia, J. E. B., & de Campos, G. A. L. (2021). Centralized Algorithms Based on Clustering with Self-tuning of Parameters for Cooperative Target Observation. Revista de Informática Teórica e Aplicada, 28(2), 39-49.
  • Andrae, S., & Pobuda, P. (2021). Welche Modellierungswerkzeuge stehen zur Verfügung?. In Agentenbasierte Modellierung (pp. 37-41). Springer Gabler, Wiesbaden.
  • Anebagilu, P. K., Dietrich, J., Prado-Stuardo, L., Morales, B., Winter, E., & Arumi, J. L. (2021). Application of the theory of planned behavior with agent-based modeling for sustainable management of vegetative filter strips. Journal of Environmental Management, 284, 112014.
  • Angel, M. C. M., Humberto, D. B., & Alfredo, T. M. (2021). Optimization of Vehicle Flow Times in a Single Crossing System Through the Development of a Multi-Agent Platform. Journal homepage: http://iieta. org/journals/isi, 26(4), 387-392.
  • Anokhin, A., Burov, S., Parygin, D., Rent, V., Sadovnikova, N., & Finogeev, A. (2021). Development of Scenarios for Modeling the Behavior of People in an Urban Environment. In Society 5.0: Cyberspace for Advanced Human-Centered Society (pp. 103-114). Springer, Cham.
  • Antczak, T., Skorupa, B., Szurlej, M., Weron, R., & Zabawa, J. (2021). Simulation modeling of epidemic risk in supermarkets: Investigating the impact of social distancing and checkout zone design (No. WORMS/21/05). Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
  • Anvari, S., Nambiar, S., Pang, J., & Maftoon, N. (2021). Computational Models and Simulations of Cancer Metastasis. Archives of Computational Methods in Engineering, 1-23.
  • Anzola, D. (2021). Capturing the representational and the experimental in the modelling of artificial societies. European Journal for Philosophy of Science, 11(3), 1-29.
  • Aqib, M., & Ukil, A. (2020, November). Modelling of Electric Vehicle Charging and Discharging Profile to Mimic Real life Scenario at Charging Stations. In 2020 IEEE REGION 10 CONFERENCE (TENCON) (pp. 501-505). IEEE.
  • Arasteh, M. A., & Farjami, Y. (2021). New Hydro-economic System Dynamics and Agent-based Modeling for Sustainable Urban Groundwater Management: A Case Study of Dehno, Yazd Province, Iran. Sustainable Cities and Society, 103078.
  • Arico, F., Annatelli, M., & Trapasso, G. (2021). Turning mustard gas chemistry into green chemistry: a new tool for pharmaceutical synthesis. In 6th Green & Sustainable Chemistry Conference. Elsevier.
  • Ariosa Hernández, R. (2021). N2P: Netlogo to Pandora, noves funcionalitats i mòdul de visualització en entorns ABM (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Ascenzi, I. (2021). Forest Credits to Foster Reforestation in the Brazilian Atlantic Forest (Master's thesis).
  • Asiddao, M. D., & Bongolan, V. P. (2021). Agent-Based Fire-Spreading Model in a Dense Urban Community. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 46, 35-40.
  • Aslan, U. Wu, S. P., Horn, M., & Wilensky, U. (2021). Connecting "the chemistry triplet" through co-designing computational models with teachers: A case study on calorimetry . Paper presented at the 2021 Annual Meeting of the American Education Research Association (AERA).
  • Astudillo, Y. A. P. A Hybrid Control Architecture for an Automated Storage and Retrieval System. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA LATIN AMERICA 2021, 30.
  • Av-Shalom, N. A. Y., Duncan, R. G., & Chinn, C. A. (2021). Students’ Conceptualizations of the Role of Evidence in Modeling. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Azadi, F., Mitrovic, N., & Stevanovic, A. (2021). Impact of Shared Lanes on Performance of the Combined Flexible Lane Assignment and Reservation-based Intersection Control. Transportation Research Record, 03611981211064274.
  • Azadi, M., Rahman, A., & ShahMohamadi, F. (2021). Modeling and simulation of ovarian cancer and tumor growth and spread in different stages of ovarian cancer according to the TNM system. Journal of Health and Biomedical Informatics, 8(1), 42-54.
  • Aziz, Z. A., Abdulqader, D. N., Sallow, A. B., & Omer, H. K. (2021). Python Parallel Processing and Multiprocessing: A Rivew. Academic Journal of Nawroz University, 10(3), 345-354.
  • Azizi, A., Mubayi, A., & Mubayi, A. (2021). Social Ecological Contexts and Alcohol Drinking Dynamics: An Application of the Survey Data-Driven Agent-Based Model for University Students. Journal of the Indian Institute of Science, 1-21.
  • Bagnoli, F., de Bonfioli Cavalcabo, G., Casu, B., & Guazzini, A. (2021). Community Formation as a Byproduct of a Recommendation System: A Simulation Model for Bubble Formation in Social Media. Future Internet, 13(11), 296.
  • Bampoh, D. K., Earl, J. E., & Zollner, P. A. (2021). Simulating the relative effects of movement and sociality on the distribution of animal-transported subsidies. Theoretical Ecology, 1-14.
  • Bandemer, L., & McCarthy, K. S. (2021). Supporting Comprehension in Computer-Based Science Simulations (No. 6188). EasyChair.
  • Barceló, J. G. A., & Garcés, R. A. A. (2021). Inclusión de la propensión al autoempleo en el proceso de emparejamiento del mercado laboral. Problemas del Desarrollo. Revista Latinoamericana de Economía, 52(207).
  • Barnes, B., Dunn, S., Pearson, C., & Wilkinson, S. (2021). Improving Human Behaviour in Macroscale City Evacuation Agent-Based Simulation. International Journal of Disaster Risk Reduction, 102289.
  • Barop, J. (2021). GENERALISING HOTELLING’S LAW: Economic and Philosophical Findings from Agent-Based Modelling. Rerum Causae, 12(1).
  • Barrón-Estrada, M. L., Zatarain-Cabada, R., Romero-Polo, J. A., & Monroy, J. N. (2021). Patrony: A mobile application for pattern recognition learning. Education and Information Technologies, 1-24.
  • Barton, A., Volna, E., Kotyrba, M., & Jarusek, R. (2021). Proposal of a Control Algorithm for Multiagent Cooperation Using Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems.
  • Bastani, M., & Jahan, A. (2021). Integration of Taguchi-Simulation Method for Improving Banking Services. Sustainable Operations and Computers.
  • Battaglia, O. R., Di Paola, B., & Fazio, C. (2021). Exploring the Coherence of Student Reasoning when Responding to Questionnaires on Thermally Activated Phenomena. Eurasia Journal of Mathematics, Science and Technology Education, 17(7), em1977.
  • Baulenas, E., Baiges, T., Cervera, T., & Pahl-Wostl, C. (2021). How do structural and agent-based factors influence the effectiveness of incentive policies? A spatially explicit agent-based model to optimize woodland-for-water PES policy design at the local level. Ecology and Society, 26(2).
  • Bazzanella, E., & Santos, F. (2021). Does a Q-Learning NetLogo Extension Simplify the Development of Agent-based Simulations?. Proceedings of the 15thWorkshop-School on Agents, Environments, and Applications (WESAAC), to appear.
  • Beccuti, M., Castagno, P., Franceschinis, G., Pennisi, M., & Pernice, S. (2021). A Petri Net Formalism to Study Systems at Different Scales Exploiting Agent-Based and Stochastic Simulations. In Performance Engineering and Stochastic Modeling (pp. 22-43). Springer, Cham.
  • Belavadi, P., Burbach, L., Ziefle, M., & Valdez, A. C. (2021, July). Finding a Structure: Evaluating Different Modelling Languages Regarding Their Suitability of Designing Agent-Based Models. In International Conference on Human-Computer Interaction (pp. 201-219). Springer, Cham.
  • Belda, A., Giancola, E., Williams, K., Dabirian, S., Jradi, M., Volpe, R., ... & Eicker, U. (2022). Reviewing Challenges and Limitations of Energy Modelling Software in the Assessment of PEDs Using Case Studies. In Sustainability in Energy and Buildings 2021 (pp. 465-477). Springer, Singapore.
  • Beltrán Pérez, G., & Rodríguez González, D. G. An agent-based approach for tourist planning.
  • Berger, C., & Mahdavi, A. (2021, July). Approaching the human dimension of building performance via agent-based modeling. In ECPPM 2021-eWork and eBusiness in Architecture, Engineering and Construction: Proceedings of the 13th European Conference on Product & Process Modelling (ECPPM 2021), 15-17 September 2021, Moscow, Russia (p. 171). CRC Press.
  • Bergholm, J. (2021). The Data Retention Saga Continued–from Tele2 Sverige to Privacy International and La Quadrature du Net. JFT, 2021(2), 111-139.
  • Bernardin, A., Martínez, A. J., & Perez-Acle, T. (2021). On the effectiveness of communication strategies as non-pharmaceutical interventions to tackle epidemics. PloS one, 16(10), e0257995.
  • Bethencourt, J. A. B., Zayas, R. H., & Escoda, M. Á. (2021). Simulación estocástica de un brote de enfermedad respiratoria aviar en Camagüey. Revista de Producción Animal, 33(2).
  • Bezzaoucha, F. S., Sahnoun, M. H., & Benslimane, S. M. (2021, February). Multi-agent modeling and simulation of wind turbine behavior with failure propagation consideration. In 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH) (pp. 97-102). IEEE.
  • Bezzout, H., & Faylali, H. E. (2022). EmPRM: A Novel Multi-agent Model for Modeling and Simulating of Electromagnetic Waves Using Netlogo Platform. In Advances on Smart and Soft Computing (pp. 517-526). Springer, Singapore.
  • Bhunia, G. S., & Shit, P. K. (2021). GeoComputation and Spatial Modelling for Decision-Making. In GeoComputation and Public Health (pp. 221-273). Springer, Cham.
  • Biggs, R., de Vos, A., Preiser, R., Clements, H., Maciejewski, K., & Schlüter, M. (Eds.). (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems. Routledge.
  • Bijak, J., Hinsch, M., Nurse, S., Prike, T., & Reinhardt, O. (2022). Bayesian Model-Based Approach: Impact on Science and Policy. In Towards Bayesian Model-Based Demography (pp. 155-174). Springer, Cham.
  • Bijandi, M., Karimi, M., van der Knaap, W., & Bansouleh, B. F. (2021). A novel approach for multi-stakeholder agricultural land reallocation using agent-based modeling: A case study in Iran. Landscape and Urban Planning, 215, 104231.
  • Bioco, J., Cánovas, F., Prata, P., & Fazendeiro, P. (2021). SDSim: A generalized user friendly web ABM system to simulate spatiotemporal distribution of species under environmental scenarios. Environmental Modelling & Software, 105234.
  • Bissembayeva, G. (2021). Simulation and Modeling of Microorganisms in Biofilm. (Thesis).
  • Blanchard, J. R., Santos, R. O., & Rehage, J. S. (2021). Sociability interacts with temporal environmental variation to spatially structure metapopulations: A fish dispersal simulation in an ephemeral landscape. Ecological Modelling, 443, 109458.
  • Bonakdar, S. B., & Roos, M. Dissimilarity Effects on House Prices: What Is the Value of Similar Neighbours?.
  • Bonghanoy, C. (2021). A Netlogo Simulation of Behavior-Based and Random Walk (BRW) Method to Solve Area-Coverage Problem.
  • Bonin, O. (2021). 15. Thom’s catastrophe theory and Turing’s morphogenesis for urban growth modelling. Handbook on Entropy, Complexity and Spatial Dynamics: A Rebirth of Theory?, 246.
  • Bosse, S. (2021). Large-scale agent-based simulation and crowd sensing with mobile agents. Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods.
  • Botetano, C., & Abrahamson, D. (2021). The Botetano arithmetic method: Introduction and early evidence. International Journal of Mathematical Education in Science and Technology, 1-19. https://doi.org/10.1080/0020739X.2020.1867916
  • Bourceret, A., Amblard, L., & Mathias, J. D. (2021). Governance in social-ecological agent-based models: a review. Ecology and Society, 26(2).
  • Bozuyla, M., & Tola, A. T. (2021). Designing a Novel Transportation System Using Microscopic Models and Multi-Agent Approach. Automatic Control and Computer Sciences, 55(2), 125-136.
  • Brady, C. (2021). Patches as an expressive medium for exploratory multi‐agent modelling. British Journal of Educational Technology, 52(3), 1024-1042.
  • Breitwieser, L., Hesam, A., de Montigny, J., Vavourakis, V., Iosif, A., Jennings, J., ... & Bauer, R. (2021). BioDynaMo: a modular platform for high-performance agent-based simulation. Bioinformatics.
  • Brinkmann, K., Kübler, D., Liehr, S., & Buerkert, A. (2021). Agent-based modelling of the social-ecological nature of poverty traps in southwestern Madagascar. Agricultural Systems, 190, 103125.
  • Brittin, J., Araz, O. M., Ramirez-Nafarrate, A., & Huang, T. T. K. (2021). An Agent-Based Simulation Model for Testing Novel Obesity Interventions in School Environment Design. IEEE Transactions on Engineering Management.
  • Brouwer, J. (2021). Safety implications of the introduction of Autonomous Vehicles on rural roads: An agent-based modeling approach.
  • Brown, C. (2021). Quantitative modelling and computer simulation. The Routledge Handbook of Landscape Ecology.
  • Brown, S., Ferreira, C., Houck, M., & Liner, B. (2021). Conceptual Ex‐Ante Simulation for Green Stormwater Infrastructure Adoption on Private Property Using Agent‐Based Modeling. Water Environment Research.
  • Buchmann, T., Wolf, P., & Fidaschek, S. (2021). Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach. Energies, 14(3), 656.
  • Buhat, C. A. H., Lutero, D. S., Olave, Y. H., Torres, M. C., & Rabajante, J. F. (2021). Community Transmission of Respiratory Infectious Diseases using Agent-based and Compartmental Models. Mindanao Journal of Science and Technology, 19(2).
  • Burg, V., Troitzsch, K. G., Akyol, D., Baier, U., Hellweg, S., & Thees, O. (2021). Farmer's willingness to adopt private and collective biogas facilities: An agent-based modeling approach. Resources, Conservation and Recycling, 167, 105400.
  • Burova, A. A., Burov, S. S., Parygin, D. S., Finogeev, A. G., & Smirnova, T. V. (2021). Administration panel of the multi-agent modeling platform with the ability to generate graphical reports. International Journal of Open Information Technologies, 9(12), 4-14.
  • Burrows, A. C., Borowczak, M., & Mugayitoglu, B. (2022). Computer Science beyond Coding: Partnering to Create Teacher Cybersecurity Microcredentials. Education Sciences, 12(1), 4.
  • Burrows, A. C., Borowczak, M., Myers, A., Schwortz, A. C., & McKim, C. (2021). Integrated STEM for Teacher Professional Learning and Development:“I Need Time for Practice”. Educ. Sci. 2021, 11, 21.
  • Burrows, A. C., Swarts, G. P., Hutchison, L., Katzmann, J. M., Thompson, R., Freeman, L., ... & Reynolds, T. (2021). Finding Spaces: Teacher Education Technology Competencies (TETCs). Education Sciences, 11(11), 733.
  • Cadavid, L., Díez-Echavarría, L. F., & Valencia-Arias, A. (2021). Does heterogeneity operationalization matter to model the diffusion phenomena?. IEEE Latin America Transactions, 100(XXX).
  • Calderón, A., & Silva, V. (2021). Exposure forecasting for seismic risk estimation: Application to Costa Rica. Earthquake Spectra, 8755293021989333.
  • Calderoni, F., Campedelli, G. M., Szekely, A., Paolucci, M., & Andrighetto, G. (2021). Recruitment into Organized Crime: An Agent-Based Approach Testing the Impact of Different Policies. Journal of Quantitative Criminology, 1-41.
  • Camara, D., Kotzinos, D., Rousseau, P.(2021) Weak signal detection and identification in large data sets: a review of methods and applications. IEEE TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING (preprint.)
  • Campennì, M., Cronk, L., & Aktipis, A. (2021). Need-Based Transfers Enhance Resilience to Shocks: An Agent-Based Model of a Maasai Risk-Pooling System. Human Ecology, 1-14.
  • Canals, A. (2021). To hoard or to share? Strategic management of knowledge and ICTs in complex economic systems. Intangible Capital, 17(2), 148-172.
  • Cao, Y., Li, F., Xi, X., van Bilsen, D. J. C., & Xu, L. (2021). Urban livability: Agent-based simulation, assessment, and interpretation for the case of Futian District, Shenzhen. Journal of Cleaner Production, 128662.
  • Caplan, B., Covitt, B., Love, G., Berkowitz, A. R., Gunckel, K. L., McClure, C., & Moore, J. C. (2021). Using computational thinking and modeling to build water and watershed literacy. Connected Science Learning, 3(2).
  • Cardoso, R. C., & Ferrando, A. (2021). A Review of Agent-Based Programming for Multi-Agent Systems. Computers, 10(2), 16.
  • Cardoso, R. C., Ferrando, A., Briola, D., Menghi, C., & Ahlbrecht, T. (2021). Agents and Robots for Reliable Engineered Autonomy: A Perspective from the Organisers of AREA 2020. Journal of Sensor and Actuator Networks, 10(2), 33.
  • Carrella, E. (2021). No Free Lunch when Estimating Simulation Parameters. Journal of Artificial Societies and Social Simulation, 24(2).
  • Carvajal León, B. F. (2022). Modelización basada en el individuo, de un reactor CSTR con recirculación de lodos activados (Bachelor's thesis, Quito: UCE).
  • Ceja, A., Kane, S., & Way, M. (2021). The PEACH Model: Physiology, Exoclimatology, and Astroecology for Characterizing Habitability. Bulletin of the American Astronomical Society, 53(3), 1217.
  • CENANİ, Ş. Emergence and complexity in agent-based modeling: Review of state-of-the-art research. Journal of Computational Design, 2(2), 1-24.
  • Chabbar, S., Benmir, M., Karkri, J. E., Bensaid, K., Aboulaich, R., & Nejjari, C. (2021). Modeling and simulation of the evolution of the corona virus pandemic in a context of Migration. Journal of Theoretical and Applied Information Technology, 4363-4374.
  • Chai, S. S., & Goh, K. L. (2021). Daily Rainfall Forecasting Using Meteorology Data with Long Short-Term Memory (LSTM) Network. Journal of Optimization in Industrial Engineering.
  • Chanda, S. S. (2021). Mandating Code Disclosure is Unnecessary--Strict Model Verification Does Not Require Accessing Original Computer Code. arXiv preprint arXiv:2105.05170.
  • Chathika, G., Rand, W., & Garibay, I. (2021). Inferring mechanisms of response prioritization on social media under information overload. Scientific Reports (Nature Publisher Group), 11(1).
  • Chaves, C. J. N., Leal, B. S. S., Rossatto, D. R., Berger, U., & Palma-Silva, C. (2021). Deforestation is the turning point for the spreading of a weedy epiphyte: an IBM approach. Scientific Reports, 11(1), 1-14.
  • Chen, B., Xu, B., & Gong, P. (2021). Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities. Big Earth Data, 1-32.
  • Chen, F., Liu, J., & Chen, J. (2021). Earthquake Disaster Rescue Model Based on Complex Adaptive System Theory. Complexity, 2021.
  • Chen, J., Gong, B., Wang, Y., & Zhang, Y. (2021). Construction of Internet of things trusted group based on multidimensional attribute trust model. International Journal of Distributed Sensor Networks, 17(1), 1550147721989888.
  • Chen, J., Shi, T., & Li, N. (2021). Pedestrian evacuation simulation in indoor emergency situations: Approaches, models and tools. Safety Science, 142, 105378.
  • Chen, S. H. (2021). Humanity in the Era of Autonomous Human–machine Teams. In Systems Engineering and Artificial Intelligence (pp. 309-331). Springer, Cham.
  • Cheng, C., Luo, Y., Yu, C. B., & Ding, W. P. (2021). Social bots and mass media manipulate public opinion through dual opinion climate. Chinese Physics B.
  • Cheng, L., Guo, H., & Lin, H. (2021). Evolutionary model of coal mine safety system based on multi-agent modeling. Process Safety and Environmental Protection.
  • Cheremisina, E. N., Tokareva, N. A., Kirpicheva, E. Y., Kreider, O. A., Milovidova, A. A., & Potemkina, S. V. (2021). THE CONCEPT OF TRAINING IT PROFESSIONALS IN THE CROSS-CUTTING DIGITAL TECHNOLOGIES.
  • Chesney, T. (2021). Designing an Agent Model. Agent-Based Modelling of Worker Exploitation, 117-122.
  • Chetcuti, J., Kunin, W. E., & Bullock, J. M. (2021). Matrix composition mediates effects of habitat fragmentation: a modelling study. Landscape Ecology, 1-16.
  • Chinesta Fortes, I. (2021). Wealth Distribution and Inheritance: A Agent-Based simulation analysis.
  • Chiu, M. M., & Reimann, P. (2021). Statistical and stochastic analysis of sequence data. In International Handbook of Computer-Supported Collaborative Learning (pp. 533-550). Springer, Cham.
  • Cho, S. Y., Lim, M. J., & Im, T. (2021). The Effects of Project Based Learning on Learners' Creativity and Problem Solving. Journal of Practical Engineering Education, 13(1), 213-219.
  • Choi, T., & Park, S. (2021). Theory building via agent-based modeling in public administration research: vindications and limitations. International Journal of Public Sector Management.
  • Chukundah, R. E. (2021). Reforesting the Atlantic Forest, through Forest Credits, PES or Carbon Credits (Bachelor's thesis).
  • Chun-bei, X. I. A. O., Jun, M. A., & Chen, Y. A. N. G. (2021). Simulation study on fire spread of traditional building community in Beijing Nanluoguxiang region. Fire Science and Technology, 40(6), 865.
  • Chunxiao, Z., & Junjie, G. (2021). Autonomy-oriented proximity mobile social network modeling in smart city for emergency rescue. International Journal of Distributed Sensor Networks, 17(12), 15501477211061252.
  • Cimino, M. G., Minici, D., Monaco, M., Petrocchi, S., & Vaglini, G. (2021). A hyper-heuristic methodology for coordinating swarms of robots in target search. Computers & Electrical Engineering, 95, 107420.
  • Civico, M. (2021). Language policy and planning: a discussion on the complexity of language matters and the role of computational methods. SN Social Sciences, 1(8), 1-22.
  • Coelho, C. G. C., & Ralha, C. G. (2021). MASE-EGTI: An agent-based simulator for environmental land change. Environmental Modelling & Software, 105252.
  • Collard, J. D., Stattner, E., & Gergos, P. (2021). The “ReadyPark” Collaborative Parking Search Strategy. Smart Cities, 4(3), 1130-1145.
  • Collard, P. (2021). The “flat peer learning” agent-based model. Journal of Computational Social Science, 1-27.
  • Condie, S. A., Anthony, K. R., Babcock, R. C., Baird, M. E., Beeden, R., Fletcher, C. S., ... & Westcott, D. A. (2021). Large-scale interventions may delay decline of the Great Barrier Reef. Royal Society Open Science, 8(4), 201296.
  • Conroy-Beam, D. (2021). Couple Simulation: A Novel Approach for Evaluating Models of Human Mate Choice. Personality and Social Psychology Review, 1088868320971258.
  • Crevier, L. P., Salkeld, J. H., Marley, J., & Parrott, L. (2021). Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities. Ecological Modelling, 446, 109505.
  • Crouse, K.N. (2021). B3GET: A New Computational Approach for Understanding and Exploring Ecology, Evolution, and Behavior (Publication No. 28962838)[Doctoral dissertation, University of Minnesota]. University of Minnesota ProQuest Dissertations & Theses. http://turing.library.northwestern.edu/login?url=https://www.proquest.com/dissertations-theses/b3get-new-computational-approach-understanding/docview/2641521630/se-2?accountid=12861
  • Cruz Ardila, J. C., Trujillo Perdomo, J. F., Zambrano Vidal, L. F., Panesso Patiño, V., Arévalo Soto, A., & Girón Restrepo, G. A. (2021). Boletín de Investigaciones agosto de 2021.
  • Cruz, D. A., & Kemp, M. (2021). Hybrid computational modeling methods for systems biology. Progress in Biomedical Engineering.
  • Cubeiro, M. T. (2021). Ignorancia y complejidad: la sociología de la enfermedad mental. Acciones e Investigaciones Sociales, (42).
  • Cunha, B., Brito, C., Araújo, G., Sousa, R., Soares, A., & Silva, F. A (2021). Smart traffic control in vehicle ad-hoc networks: a Systematic Literature Review. Universidade Federal do Piauí.
  • Currat, M., Quilodrán, C. S., & Excoffier, L. (2021). Simulations of Human Dispersal and Genetic Diversity. In Evolution of the Human Genome II (pp. 231-256). Springer, Tokyo.
  • Currie, T. E., Campenni, M., Flitton, A., Njagi, T., Ontiri, E., Perret, C., & Walker, L. (2021). The cultural evolution and ecology of institutions. Philosophical Transactions of the Royal Society B, 376(1828), 20200047.
  • Currie, T., Campenni, M., Flitton, A., Njagi, T., Ontiri, E., Perret, C., & Walker, L. (2021). Code supporting The Cultural Evolution & Ecology of Institutions.
  • da Silva, A. C. G., de Lima, C. L., da Silva, C. C., Moreno, G. M. M., Silva, E. L., Marques, G. S., ... & dos Santos, W. P. (2022). Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis, 333-357.
  • da Silva, A. J. P., & Arroio, A. (2021). Tempo e tecnologia no processo de visualização em Química: um estudo exploratório sobre as práticas de professores em formação inicial. Revista de Investigação Tecnológica em Educação em Ciências e Matemática, 1, 80-99.
  • Dabholkar, S., (2021). Designing computational models as Emergent Systems Microworlds for learning biomaking digitally. In Walker, J. and Strawhacker, A. (Symposium chairs), The Biomaker Ecosystem: Technologies, Spaces and Curriculum for K-12 Making with Biology. Presented at The 2021 Annual Meeting of American Education Research Association (AERA).
  • Dabholkar, S., Horn, M., & Wilensky, U. (2021). A technology-mediated co-design approach for integrating Computational Thinking in a science classroom. Paper presented in the 2021 Annual Meeting of the American Education Research Association (AERA).
  • Dabholkar, S., Peel, A., Hao, D., Kelter, J., Horn, M., & Wilensky, U. (2021). Analysis of Co-designed Biology Units Integrated with Computational Thinking Activities. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 665-668). Bochum, Germany: International Society of the Learning Sciences.
  • Dabholkar, S., Tran, S., Horn, M. S., & Wilensky, U. (2021). Students' Attitudinal Change After Participating in a CT integrated Biology Unit . In Dabholkar S. (Symposium organizer), Integrating Computational Thinking in Science Curricula: Professional Development and Student Assessment. Presented at the 2021 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Daems, D. (2021). Social Complexity and Complex Systems in Archaeology. Routledge.
  • Das, D. (2021). Agent Based Virus Model using NetLogo: Infection Propagation, Precaution, Recovery, Multi-site Mobility and (Un) Lockdown. arXiv preprint arXiv:2102.00844.
  • Datseris, G., Vahdati, A. R., & DuBois, T. C. (2021). Agents. jl: A performant and feature-full agent based modelling software of minimal code complexity. arXiv preprint arXiv:2101.10072.
  • David, N. G. (2021). Reframing Educational Leadership Research in the Twenty-First Century. In Concept and Design Developments in School Improvement Research (pp. 107-135). Springer, Cham.
  • Day, T. E., Dong, Y., & Gopakumar, B. (2021). How Many of Those Things Do We Really Need? Discrete Event Simulation. In Comprehensive Healthcare Simulation: Improving Healthcare Systems (pp. 187-195). Springer, Cham.
  • de Castro, M. G. A., & García-Peñalvo, F. J. ICT methodologies for teacher professional development in Erasmus+ projects related to eLearning. In 2021 XI International Conference on Virtual Campus (JICV) (pp. 1-6). IEEE.
  • de Jong, K., Panja, D., van Kreveld, M., & Karssenberg, D. (2021). An environmental modelling framework based on asynchronous many-tasks: scalability and usability. Environmental Modelling & Software, 104998.
  • de Kemp, E. A. (2021). Spatial Agents for Geological Surface Modelling. Geoscientific Model Development Discussions, 1-32.
  • de Oliveira Simoyama, F., Sarti, F. M., & Battisti, M. C. G. (2021). Effects of disclosing inspection scores of health facilities. Socio-Economic Planning Sciences, 101183.
  • de Oliveira, G. D., Porto, P. P. G., Alves, C. D. M. A., & Ralha, C. G. (2021). An Agent-Based Model for Simulating Irrigated Agriculture in the Samambaia Basin in Goiás. Revista de Informática Teórica e Aplicada, 28(2), 107-123.
  • de Quadros, C. E. P., Adamatti, D. F., & de Lima Bicho, A. (2021, October). BioTraffic: a bio-inspired behavioral model to vehicle traffic simulation. In 2021 20th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames) (pp. 29-38). IEEE.
  • de Souza Almeida, F. M., Gomes, A. P., & de Freitas, A. F. (2021). Social networks and efficiency in dairy farming: the case of the Program for the Development of Dairy Farming in Minas Gerais, Brazil. Livestock Science, 104401.
  • de Souza, V. M., Bloemhof, J., & Borsato, M. (2021). Assessing the eco-effectiveness of a solid waste management plan using agent-based modelling. Waste Management, 125, 235-248.
  • de Velazco, F. F., Lara, E. C., & Luna, S. R. (2021). Proposal of a Model from the Perspective of Parsons Functional-Structural Theory. Journal of Systemics, Cybernetics and Informatics, 19(8), 182-197.
  • de Vos, A., Maciejewski, K., Bodin, Ö., Norström, A., Schlüter, M., & Tengö, M. The practice and design of social-ecological systems research. (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems, 47.
  • de Wildt, T. E., Boijmans, A. R., Chappin, E. J., & Herder, P. M. (2021). An ex ante assessment of value conflicts and social acceptance of sustainable heating systems: An agent-based modelling approach. Energy Policy, 153, 112265.
  • DeAngelis, D. L., Franco, D., Hastings, A., Hilker, F. M., Lenhart, S., Lutscher, F., ... & Tyson, R. C. (2021). Towards Building a Sustainable Future: Positioning Ecological Modelling for Impact in Ecosystems Management. Bulletin of Mathematical Biology, 83(10), 1-28.
  • Delcea, C., Cotfas, L. A., Milne, R. J., Xie, N., & Mierzwiak, R. (2021). Grey clustering of the variations in the back-to-front airplane boarding method considering COVID-19 flying restrictions. Grey Systems: Theory and Application.
  • Deng, S., & Zhang, J. (2021). Modernization Versus Dependency Approaches to Sustainable Development--Based on the UN Report 2019. In E3S Web of Conferences (Vol. 275, p. 02029). EDP Sciences.
  • Derkach, T. M. (2021, March). The origin of misconceptions in inorganic chemistry and their correction by computer modelling. In Journal of Physics: Conference Series (Vol. 1840, No. 1, p. 012012). IOP Publishing.
  • Dhou, K., & Cruzen, C. (2021, June). An Innovative Employment of the NetLogo AIDS Model in Developing a New Chain Code for Compression. In International Conference on Computational Science (pp. 17-25). Springer, Cham.
  • Díaz Monsalvea, J., Enríquez Corredor, I., Pinto Moreno, Á. M., & Sánchez Santamaría, J. Evaluación de una emulación de un sistema ASRS acoplado a un sistema de compras.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. PROTOCOLO–Evaluar la competencia transversal trabajo en equipo en estudiantes de grado y máster mediante la entrevista por competencias. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Dignum, F. (2021). The Real Impact of Social Simulations During the COVID-19 Crisis. In Social Simulation for a Crisis (pp. 319-329). Springer, Cham.
  • Dimka, J., & Sattenspiel, L. (2021). “We didn't get much schooling because we were fishing all the time”: Potential impacts of irregular school attendance on the spread of epidemics. American Journal of Human Biology, e23578.
  • Dobrota, M., Zornić, N., & Marković, A. (2021). FDI Time Series Forecasts: Evidence from Emerging Markets. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies.
  • Doeweler, F. (2021). Causes of Recruitment Limitation at Abrupt Alpine Treelines (Doctoral dissertation, Auckland University of Technology).
  • Dogaroglu, B., Caliskanelli, S.P., Tanyel, S. (2021). Comparison of Intelligent Parking Guidance System and Conventional System with Regard to Capacity Utilisation. Sustainable Cities and Society, 103152.
  • Donaldson, S. (2021). Flocc: From Agent-Based Models to Interactive Simulations on the Web. Northeast Journal of Complex Systems (NEJCS), 3(1), 6.
  • Dong, S. (2021). A Class of Public Opinion Dissemination Model considering the Information Screening Mechanism. Security and Communication Networks, 2021.
  • Dong, Z., Liu, H., & Zheng, X. (2021). The influence of teacher-student proximity, teacher feedback, and near-seated peer groups on classroom engagement: An agent-based modeling approach. Plos one, 16(1), e0244935.
  • Döpper, T., Milakovic, D., Scheel, O., Große-Wöhrmann, B., Oexle, J., Slotosch, S., ... & Widmaier, L. (2021). Expanding HLRS Academic HPC Simulation Training Programs to More Target Groups. Graphics: Steven Behun, Heather Marvin, 12(3), 13.
  • Doussin, B., Adam, C., & Georges, D. (2021). Multi-scale simulation of COVID-19 epidemics. arXiv preprint arXiv:2112.01167.
  • Dovrat, D., Tripathy, T., & Bruckstein, A. M. (2021). On Tracking and Capture in Proportional-Control Bearing-Only Unicycle Pursuit. IEEE Control Systems Letters.
  • Ducke, B., & Suchowska, P. (2021). Exploratory Network Reconstruction with Sparse Archaeological Data and XTENT. Journal of Archaeological Method and Theory, 1-32.
  • Duijsings, R. F. Y. (2021). Bargaining Model: Enhancing the Wealth and Survival of the Poor by Finding a Better Long-term Strategy (Bachelor's thesis).
  • Eberbach, C., Hmelo‐Silver, C. E., Jordan, R., Taylor, J., & Hunter, R. (2021). Multidimensional trajectories for understanding ecosystems. Science Education.
  • Edrisi, A., Lahoorpoor, B., & Lovreglio, R. (2021). Simulating Metro Station Evacuation using Three Agent-based Exit Choice Models. Case Studies on Transport Policy.
  • Effati, S., & Tavakoli, E. (2021). The Effect of Social Distancing and Personal Protective Equipment on the Outbreak of SARS-COVID-2: An Agent-Based Modeling Approach. (pre-print).
  • EFSA Scientific Committee, More, S., Bampidis, V., Benford, D., Bragard, C., Halldorsson, T., ... & Rortais, A. (2021). A systems‐based approach to the environmental risk assessment of multiple stressors in honey bees. EFSA Journal, 19(5), e06607.
  • Eismann, K. (2021). Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter. Journal of Business Economics, 1-31.
  • Eitzel, M. V., Solera, J., Hove, E. M., Wilson, K. B., Ndlovu, A. M., Ndlovu, D., ... & Veski, A. (2021). Assessing the Potential of Participatory Modeling for Decolonial Restoration of an Agro-Pastoral System in Rural Zimbabwe. Citizen Science: Theory and Practice, 6(1).
  • El Fakir, A., Fairchild, R., Tkiouat, M., & Taamouti, A. (2021). A bargaining model for profit and loss sharing entrepreneurial financing: A game theoretic model using agent based simulation. International journal of finance and economics.
  • El Mouhib, M., Azghiou, K., & Tahani, A. (2021, April). Analysis of the Impact of Traffic Density on the Compromised CAV Rate: a Multi-Agent Modeling Approach. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-6). IEEE.
  • Ellison, A. M., & Gotelli, N. J. (2021). Ants (Hymenoptera: Formicidae) and humans: from inspiration and metaphor to 21 st-century symbiont. Myrmecological News, 31.
  • Elmenreich, W., Schnabl, A., & Schranz, M. (2021). An artificial hormone-based algorithm for production scheduling from the bottom-up.
  • Embodying, P. (2021). The Pack. Design Make Play for Equity, Inclusion, and Agency: The Evolving Landscape of Creative STEM Learning, 208.
  • Encinas, D., Jara, J., Bond, R., Rosatto, D., Maccallini, L., Gomez, M., ... & Morales, M. (2021). Técnicas de modelado y simulación para arquitecturas HPC y salud. In XXIII Workshop de Investigadores en Ciencias de la Computación (WICC 2021, Chilecito, La Rioja).
  • Enderle, P., King, N., & Margulieux, L. (2021). What’s in a Wave?. The Science Teacher, 88(4).
  • Ernst, A., & Simon, K. H. (2021). Soziale Simulation als Werkzeug der transformativen Forschung. GAIA-Ecological Perspectives for Science and Society, 30(2), 134-136.
  • Eshanthini, P., Nandhakumar, S., & Bandari, R. Ground Water Modelling of Poondi Micro-Watershed, Thiruvallur, Tamil Nadu. Advances in Construction Management: Select Proceedings of ACMM 2021, 233.
  • Ewert, U. C. (2021). Agentenbasierte Modellierung in der Mediävistik, oder: Wie der Netzwerkhandel der Hansekaufleute entstanden sein könnte. Mannheim Working Papers in Premodern Economic History, 2(2), 1-27.
  • Faber, R. (2021). Accessible Data Mining for Agent-Based Simulation Models. (Master’s thesis).
  • Fahad, M., Sontheimer, K., Jimenez-Romero, C., Chavez, R. I., Diaz, S., Klijn, W., & Morrison, A. Multi-scale brain co-simulation in the Human Brain Project: EBRAINS tools for in-transit simulation and analysis.
  • Fain, J. (2021). Should retail stores locate close to a rival?. Journal of Economic Interaction and Coordination, 1-34.
  • Falcone, R., & Sapienza, A. (2021). An agent-based model to assess citizens’ acceptance of COVID-19 restrictions. Journal of Simulation, 1-15.
  • Fan, Z., Ding, N., Zhu, X., & Zhu, Y. (2021). Suicide Bombing Attack Modelling and Simulation: A Case Study of Golden Water Bridge Attack. Journal of Safety Science and Resilience.
  • Farhadi, B., Rahmani, A. M., Asghari, P., & Hosseinzadeh, M. (2021). Friendship Selection and Management in Social Internet of Things: A systematic review. Computer Networks, 108568.
  • Farhadicheshmehmorvari, A. (2021). An Agent-Based Financial Network Modeling Based on Systematic Trust (Doctoral dissertation).
  • Faweya, O., Desai, P. S., & Higgs III, C. F. (2021). Towards an agent-based model to simulate osseointegration in powder-bed 3D printed implant-like structures. Journal of the Mechanical Behavior of Biomedical Materials, 104915.
  • Fazio, M., Pluchino, A., Inturri, G., Pira, M. L., Giuffrida, N., & Ignaccolo, M. (2021). Exploring the impact of mobility restrictions on the COVID-19 spreading through an agent-based approach. arXiv preprint arXiv:2102.08226.
  • Feinberg, A., Hooijschuur, E., Rogge, N., Ghorbani, A., & Herder, P. (2021). Sustaining collective action in urban community gardens. Journal of Artificial Societies and Social Simulation, 24(3).
  • Ferguson, M., Arangala, C., Yokley, K., & Rave, M. (2021). Agent Based Simulation of Dengue with Wolbachia Intervention. Minnesota Journal of Undergraduate Mathematics, 6(1).
  • Ferrare, F. D., Baum, D. M., de Almeida Júnior, J. R., Júnior, J. B. C., & Cugnasca, P. S. (2021, October). Scenarios for the Use of eVTOLs Using Multiagent SystemsWith Netlogo: Comparison Of Parameters And The Impact On UAM. In 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) (pp. 1-7). IEEE.
  • Ferrare, F. D., Baum, D. M., de Almeida Júnior, J. R., Junior, J. B. C., & Cugnasca, P. S. (2021). Urban Air Mobility (UAM): A Model Proposal based on Agents using Netlogo. In SIMULTECH (pp. 352-359).
  • Ferraro, K. M., Schmitz, O. J., & McCary, M. A. (2021). Effects of ungulate density and sociality on landscape heterogeneity: a mechanistic modeling approach. Ecography.
  • Fitzpatrick, B. G., Federico, P., Kanarek, A., & Lenhart, S. Control of a consumer‐resource agent‐based model using partial differential equation approximation. Optimal Control Applications and Methods.
  • Flache, A., & de Matos Fernandes, C. A. (2021). 24. Agent-based computational models1. Research Handbook on Analytical Sociology, 453.
  • Florindo, A. A., Teixeira, I. P., Barrozo, L. V., Sarti, F. M., Fisberg, R. M., Andrade, D. R., & Garcia, L. M. T. (2021). Study protocol: health survey of Sao Paulo: ISA-Physical Activity and Environment. BMC Public Health, 21(1), 1-10.
  • Folke, T., & Kennedy, W. G. (2021). Agent-Based Modelling: A Bridge Between Psychology and Macro-social Science. In M. MacLachlan & J. McVeigh (Eds.), Macropsychology: A Population Science for Sustainable Development Goals, 189.
  • Foramitti, J. (2021). AgentPy: A package for agent-based modeling in Python. Journal of Open Source Software, 6(62), 3065.
  • Fouladvand, J., Rojas, M. A., Hoppe, T., & Ghorbani, A. (2021). Simulating thermal energy community formation: Institutional enablers outplaying technological choice. Applied Energy, 117897.
  • Fox, W. P. (2021). Mathematical Modeling in the Age of a Pandemic.
  • Francos, R. M., & Bruckstein, A. M. (2021). Pincer-based vs. Same-direction Search Strategies After Smart Evaders by Swarms of Agents. arXiv preprint arXiv:2104.06940.
  • Fregoso, J. H. C. (2021). Breves consideraciones sobre la naturaleza compleja de la ciencia económica. Expresión Económica. Revista de análisis, (46), 9-19.
  • Friedman, D. A., Tschantz, A., Ramstead, M. J. D., Friston, K., & Constant, A. (2021). Active Inferants: An Active Inference Framework for Ant Colony Behavior. Front. Behav. Neurosci, 15, 647732.
  • Friesen, M. R., & McLeod, R. D. (2021). Towards Equitable Hiring Practices for Engineering Education Institutions: An Individual-Based Simulation Model. In Advances in Software Engineering, Education, and e-Learning (pp. 265-276). Springer, Cham.
  • Fu, Z., Dong, P., Li, S., Ju, Y., & Liu, H. (2021). How blockchain renovate the electric vehicle charging services in the urban area? A case study of Shanghai, China. Journal of Cleaner Production, 128172.
  • Fuchs, A., Pichler-Koban, C., Pitman, A., Elmenreich, W., & Jungmeier, M. (2021). Games and Gamification—New Instruments for Communicating Sustainability. The Sustainability Communication Reader: A Reflective Compendium, 221-243.
  • Fuchs, S., Rietsche, R., Aier, S., & Rivera, M. (2021). Is more always better? Simulating Feedback Exchange in Organizations.
  • Gajary, L. C. (2021). Pathways for Theory Development: A Logic and a Methodology for Public and Nonprofit Strategic Planning (Doctoral dissertation, The Ohio State University).
  • Galán, S. F. (2021). Comparative Evaluation of the Fast Marching Method and the Fast Evacuation Method for Heterogeneous Media. Applied Artificial Intelligence, 1-25.
  • Galán, S. F. (2021). Extending cellular evolutionary algorithms with message passing. Soft Computing, 1-12.
  • Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., ... & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, 101741.
  • Gallagher, C. A., Chudzinska, M., Larsen‐Gray, A., Pollock, C. J., Sells, S. N., White, P. J., & Berger, U. (2021). From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models. Biological Reviews.
  • GAO, DEHUA. "Agent-Based Dynamics Modelling in Routine." Cambridge Handbook of Routine Dynamics (2021): 159.
  • García-Díaz, C. (2021). Agent-Based Organizational Ecologies: A Generative Approach to Market Evolution. In Pathways Between Social Science and Computational Social Science (pp. 179-196). Springer, Cham.
  • García, R. M., de la Iglesia, D. H., de Paz, J. F., Leithardt, V. R., & Villarrubia, G. (2021, February). Urban Search and Rescue with Anti-pheromone Robot Swarm architecture. In 2021 Telecoms Conference (ConfTELE) (pp. 1-6). IEEE.
  • Garg, V. (2021). Cooperative Multi-robot Target Searching and Tracking Using Velocity Inspired Robotic Fruit Fly Algorithm. SN Computer Science, 2(6), 1-12.
  • Garrido, D., Jacob, J., Silva, D. C., & Rossetti, R. J. (2021). Pedestrian Simulation In SUMO Through Externally Modelled Agents. In ECMS (pp. 111-118).
  • Gasparini, F., Giltri, M., & Bandini, S. (2021). Safety perception and pedestrian dynamics: Experimental results towards affective agents modeling. AI Communications, (Preprint), 1-15.
  • Gay, P. E., Trumper, E., Lecoq, M., & Piou, C. (2021). Importance of human capital, field knowledge and experience to improve pest locust management. Pest Management Science.
  • Gegear, R. J., Heath, K. N., & Ryder, E. F. (2021). Modeling scale up of anthropogenic impacts from individual pollinator behavior to pollination systems. Conservation Biology.
  • Gerbrands, P., & Unger, B. (2021). Policy Reform Effects in the Tax Ecosystem. Combating Fiscal Fraud and Empowering Regulators: Bringing Tax Money Back Into the COFFERS, 272.
  • Gerdes, L., Scholz-Wäckerle, M., & Schröter, J. (2021). Computerspiele und ökonomische Modellformen Auf dem Weg zu transformationskritischen Medien. Zeitschrift für Medienwissenschaft, 13(2), 35-44.
  • Gervasi, V., & Guberti, V. (2021). African swine fever endemic persistence in wild boar populations: Key mechanisms explored through modelling. Transboundary and Emerging Diseases.
  • Ghaitaranpour, A., Mohebbi, M., & Koocheki, A. (2021). An innovative model for describing oil penetration into the doughnut crust during hot air frying. Food Research International, 110458.
  • Ghorbani, A., de Bruin, B., & Kreulen, K. (2021). Studying the Influence of Culture on the Effective Management of the COVID-19 Crisis. In Social Simulation for a Crisis (pp. 189-230). Springer, Cham.
  • Ghorbani, A., Ho, P., & Bravo, G. (2021). Institutional form versus function in a common property context: The credibility thesis tested through an agent-based model. Land Use Policy, 102, 105237.
  • Ghoreishi, M., Razavi, S., & Elshorbagy, A. (2021). Understanding Human Adaptation to Drought: Agent-Based Agricultural Water Demand Modeling in the Bow River Basin, Canada. Hydrological Sciences Journal.
  • Giabbanelli, P. J., & Jackson, P. J. (2021, June). How Do Teams of Novice Modelers Choose an Approach? An Iterated, Repeated Experiment in a First-Year Modeling Course. In International Conference on Computational Science (pp. 661-674). Springer, Cham.
  • Giabbanelli, P. J., Tison, B., & Keith, J. (2021). The application of modelling and simulation to public health: Assessing the quality of Agent-Based Models for obesity. Simulation Modelling Practice and Theory, 102268.
  • Gibson, M., Slade, R., Pereira, J. P., & Rogelj, J. (2021). Comparing Mechanisms of Food Choice in an Agent-Based Model of Milk Consumption and Substitution in the UK. Journal of Artificial Societies and Social Simulation, 24(3).
  • Giordano, N., Rosati, S., Valeri, F., Borchiellini, A., & Balestra, G. (2021). Simulation of the Impact on the Workload of the Enlargement of the Clinical Staff of a Specialistic Reference Center. Studies in health technology and informatics, 281, 605-609.
  • Glake, D., Panse, F., Ritter, N., Clemen, T., & Lenfers, U. (2021). Data Management in Multi-Agent Simulation Systems. BTW 2021.
  • Gokhale, V. A. (2021). 7 Positioning Resilience in. Reliability-Based Analysis and Design of Structures and Infrastructure, 89.
  • Goldstein, E., Erinjery, J. J., Martin, G., Kasturiratne, A., Ediriweera, D. S., de Silva, H. J., ... & Iwamura, T. (2021). Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes. PLOS Neglected Tropical Diseases, 15(1), e0009047.
  • Gómez González, L. (2021). Medidas para la prevención del COVID-19 en los procesos de la aviación comercial de pasajeros (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • González-Mon, B., Lindkvist, E., Bodin, Ö., Zepeda-Domínguez, J. A., & Schlüter, M. (2021). Fish provision in a changing environment: The buffering effect of regional trade networks. Plos one, 16(12), e0261514.
  • Grajdura, S. A., Borjigin, S. G., & Niemeier, D. A. (2020, November). Agent-based wildfire evacuation with spatial simulation: a case study. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (pp. 56-59).
  • Gravel-Miguel, C., Murray, J. K., Schoville, B. J., Wren, C. D., & Marean, C. W. (2021). Exploring variability in lithic armature discard in the archaeological record. Journal of Human Evolution, 155, 102981.
  • Griesemer, M., & Sindi, S. S. (2022). Rules of Engagement: A Guide to Developing Agent-Based Models. In Microbial Systems Biology (pp. 367-380). Humana, New York, NY.
  • Grillo, C. A., Holford, M., & Walter, N. G. (2021). From Flatland to Jupiter: Searching for Rules of Interaction Across Biological Scales. Integrative and Comparative Biology.
  • Grizioti, M., & Kynigos, C. (2021, June). Children as players, modders, and creators of simulation games: A design for making sense of complex real-world problems: Children as players, modders and creators of simulation games. In Interaction Design and Children (pp. 363-374).
  • Guevara-Rivera, E., Osorno-Hinojosa, R., Zaldivar-Carrillo, V., & Perez-Ortiz, H. (2021). Dynamic simulation methodology for implementing circular economy: A new case study. Journal of Industrial Engineering and Management, 14(4), 850-862.
  • Gül, N., Hasgül, Z., & Aytöre, C. Agent-Based Simulation Modeling For Covid-19 Vaccination Policies: Single-Dose And DoubleDose Applications.
  • Gumzej, R. (2021). Use Case: E-Marketplace Regulation. In Intelligent Logistics Systems for Smart Cities and Communities (pp. 149-162). Springer, Cham.
  • Gunaratne, C., & Garibay, I. (2021). NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces. SoftwareX, 16, 100801.
  • Gunaratne, C., & Garibay, I. (2021). NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces. SoftwareX, 16, 100801.
  • Guo, L., Li, Y., & Sheng, D. (2021). Modeling and Simulating Online Panic in an Epidemic Complexity System: An Agent-Based Approach. Complexity, 2021.
  • Gürsoy, F., & Badur, B. (2021). An Agent-Based Modelling Approach to Brain Drain. arXiv preprint arXiv:2103.03234.
  • Habib, L., Pacaux-Lemoine, M. P., Berdal, Q., & Trentesaux, D. (2021). From Human-Human to Human-Machine Cooperation in Manufacturing 4.0. Processes, 9(11), 1910.
  • Haddad, T. A., Hedjazi, D., & Aouag, S. (2021). An IoT-Based Adaptive Traffic Light Control Algorithm for Isolated Intersection. In Advances in Computing Systems and Applications: Proceedings of the 4th Conference on Computing Systems and Applications (pp. 107-117). Springer International Publishing.
  • Haghpanah, F., Ghobadi, K., & Schafer, B. W. (2021). Multi-hazard hospital evacuation planning during disease outbreaks using agent-based modeling. International Journal of Disaster Risk Reduction, 102632.
  • Haghpanah, F., Schafer, B. W., & Castro, S. (2021). Application of Bug Navigation Algorithms for Large-Scale Agent-Based Evacuation Modeling to Support Decision Making. Fire Safety Journal, 103322.
  • Hakim, G., & Braun, R. (2021, December). Wireless Sensor Network Routing for Energy Efficiency. In International Conference On Systems Engineering (pp. 329-343). Springer, Cham.
  • Hammock, J., & Goel, A. (2021). Recognizing Novice Learner’s Modeling Behaviors. In Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (p. 189). Springer Nature.
  • Han, X. (2020, December). Influence of exits and evacuees on evacuation efficiency. In IOP Conference Series: Earth and Environmental Science (Vol. 608, No. 1, p. 012031). IOP Publishing.
  • Hansen, H. H., Korevaar, G., & Lukszo, Z. (2021). The effect of group decisions in heat transitions: An agent-based approach. Energy Policy, 156, 112306.
  • Haque, G. (2021). Expanding an Agent Based Model to Simulate SARS-CoV-2 Spread in Places of Worship.
  • Harel, D., & Marron, A. (2021, October). Introducing Dynamical Systems andChaos Early in Computer Science andSoftware Engineering Education Can Help Advance Theory and Practice ofSoftware Development and Computing. In International Symposium on Leveraging Applications of Formal Methods (pp. 322-334). Springer, Cham.
  • Harwick, C. (2021). Helipad: A Framework for Agent-Based Modeling in Python. Available at SSRN.
  • Hasanpour, S., & Rassafi, A. A. (2020). Pedestrian Movement Simulation in Evacuation Process from a Dynamic Environment using Agent-Based Modeling. Quarterly Journal of Transportation Engineering, 12(2), 357-376.
  • Hassannezhad, M., Gogarty, M., O’Connor, C. H., Cox, J., Meier, P. S., & Purshouse, R. C. (2021). A Cybernetic Participatory Approach for Whole-Systems Modelling and Analysis, with Application to Inclusive Economies.
  • Hassanpour, S., & Rassafi, A. A. (2021). Agent-Based Simulation for Pedestrian Evacuation Behaviour Using the Affordance Concept. KSCE Journal of Civil Engineering, 1-13.
  • Hassanpour, S., Rassafi, A. A., Gonzalez, V., & Liu, J. (2021). A hierarchical agent-based approach to simulate a dynamic decision-making process of evacuees using reinforcement learning. Journal of Choice Modelling, 100288.
  • Hbaieb, A., Ayed, S., & Chaari, L. (2021). A survey of trust management in the Internet of Vehicles. Computer Networks, 108558.
  • He, C., Jia, G., McCabe, B., Chen, Y., Zhang, P., & Sun, J. (2021). Psychological decision-making process of construction worker safety behavior: an agent-based simulation approach. International journal of occupational safety and ergonomics, (just-accepted), 1-27.
  • He, Z., Huang, D., & Fang, J. (2021). Social Stability Risk Diffusion of Large Complex Engineering Projects Based on an Improved SIR Model: A Simulation Research on Complex Networks. Complexity, 2021.
  • Hendriks, F., Distel, B., Engelke, K. M., Westmattelmann, D., & Wintterlin, F. (2021). Methodological and Practical Challenges of Interdisciplinary Trust Research. In Trust and Communication (pp. 29-57). Springer, Cham.
  • Hentati, A. I., & Fourati, L. C. A Convoy of Ground Mobile Vehicles Protection using Cooperative UAVs-based System. In 2021 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.
  • Hernández, P., Pena, C., Ramos, A., & Gómez-Cadenas, J. J. (2021). A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times. Plos one, 16(2), e0244107.
  • Herrera, D. Y. M., Barrientos, A. H., & Escobar, O. Z. (2021). A review of agent-based modeling for simulation of agricultural systems. DYNA, 88(217), 103-110.
  • Hervey, S. D., Rutledge, L. Y., Patterson, B. R., Romanski, M. C., Vucetich, J. A., Belant, J. L. & Brzeski, K. E. (2021). A first genetic assessment of the newly introduced Isle Royale gray wolves (Canis lupus). Conservation Genetics, 1-14.
  • Hižak, J. (2021). Iterated Prisoner's Dilemma among mobile agents performing 2D random walk. Croatian Operational Research Review, 12(2), 161-174.
  • Hjorth, A. (2021, August). NaturalLanguageProcesing4All: -A Constructionist NLP tool for Scaffolding Students’ Exploration of Text. In Proceedings of the 17th ACM Conference on International Computing Education Research (pp. 347-354).
  • Hjorth, A., Hansen, I. B., & Sherson, J. (2021). OptimizerSpace: A CSCL Tool for Search and Optimization. In Proceedings of the 14th International Conference on Computer-Supported Collaborative Learning-CSCL 2021. International Society of the Learning Sciences.
  • Hoffenson, S., & Fay, B. (2021). Teaching Market-Driven Engineering Design with an Agent-Based Simulation Tool. Advances in Engineering Education, 9(2), n2.
  • Høholt, M., Graungaard, D., Bouvin, N. O., Petersen, M. G., & Eriksson, E. (2021). Towards a model of progression in computational empowerment in education. International Journal of Child-Computer Interaction, 100302.
  • Hölzchen, E., Hertler, C., Mateos, A., Rodríguez, J., Berndt, J. O., & Timm, I. J. (2021). Discovering the opposite shore: How did hominins cross sea straits?. PloS one, 16(6), e0252885.
  • Hong, J., & Chun, J. (2021). Analysis of Designer Brands Aiming for the Value of Slow Fashion-Focused on John Alexander Skelton and Geoffrey B. Small. Journal of the Korean Society of Clothing and Textiles, 45(1), 136-154.
  • Hosseini, S., & Zandvakili, A. (2021). The SEIRS-C Model of Information diffusion Based on Rumor spreading with Fuzzy Logic in Social Networks. International Journal of Computer Mathematics, (just-accepted), 1-30.
  • Huang, J., & Nouri, B. (2021). An Agent-Based Model to Evaluate the Effect of Socioeconomic Status and Demographic Factors on COVID-19 Prevalence and Mortality.
  • Huang, S., Potter, A., Eyers, D., & Li, Q. (2021). The influence of online review adoption on the profitability of capacitated supply chains. Omega, 102501.
  • Huang, W., Yuan, B., Wang, S., & Zhang, X. (2020, December). Research on Simulation of Network Attack and Defense situation based on Evolutionary Game. In 2020 The 9th International Conference on Networks, Communication and Computing (pp. 96-103).
  • Huang, Y., Karabiyik, T., Madamanchi, A., & Magana, A. J. (2021). An Agent-Based Modeling Approach for Informing the U.S. Plastic Waste Management Process. SIMUL 2021 : The Thirteenth International Conference on Advances in System Simulation, 65–71.
  • Hudec, O., Gazda, V., Zoričák, M., & Horváth, D. (2021). 20. Industrial districts as the outcome of self-organisation in time and space. Handbook on Entropy, Complexity and Spatial Dynamics: A Rebirth of Theory?, 342.
  • Hunter, E., & Kelleher, J. D. (2021). Adapting an Agent-Based Model of Infectious Disease Spread in an Irish County to COVID-19. Systems, 9(2), 41.
  • Husarek, D., Paulus, S., Metzger, M., Salapic, V., & Niessen, S. (2021). Modeling Charging Infrastructure Requirements to Achieve a Holistic E-Mobility Integration in Regional Energy Systems.
  • Husnain M, & Shafi, N. (2021) An Extension to Wolf Sheep Predation (Docked Hybrid) Agent Based Model in NetLogo. Journal of Software Engineering and Intelligent Systems 6(1).
  • Hwang, Y., & Heo, G. (2021). Development of a Radiological Emergency Evacuation Model Using Agent-Based Modeling. Nuclear Engineering and Technology.
  • Ibbotson, P., Jimenez-Romero, C., & Page, K. M. (2021). Dying to cooperate: the role of environmental harshness in human collaboration. Behavioral Ecology.
  • Ibrahim, M., Hashmi, U. S., Nabeel, M., Imran, A., & Ekin, S. (2021). Embracing Complexity: Agent-based Modeling for HetNets Design and Optimization via Concurrent Reinforcement Learning Algorithms. IEEE Transactions on Network and Service Management.
  • Imirzian, N., & Hughes, D. P. (2021). An agent-based model shows zombie ants exhibit search behavior. Journal of Theoretical Biology, 110789.
  • Innes-Gold, A., Pavlowich, T., Heinichen, M., McManus, M. C., McNamee, J., Collie, J., & Humphries, A. (2021). Exploring social-ecological trade-offs in fisheries using a coupled food web and human behavior model. Ecology and Society, 26(2).
  • Inturri, G., Giuffrida, N., Ignaccolo, M., Le Pira, M., Pluchino, A., Rapisarda, A., & D'Angelo, R. (2021). Taxi vs. demand responsive shared transport systems: an agent-based simulation approach. Transport Policy.
  • Ionescu, Ș., Nica, I., & Chiriță, N. (2021). Cybernetics approach using agent-based modeling in the process of evacuating educational institutions in case of disasters. Sustainability, 13(18), 10277.
  • Jacobson, M. & Wilensky, U. (2021). Complex systems and the learning sciences: Educational, theoretical, and methodological implications. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (3rd Edition). Cambridge, UK: Cambridge University Press.
  • Jager, W. (2021). Using Agent Based Modelling to Explore Behavioural Dynamics Affecting our Climate. Current Opinion in Psychology.
  • Jahanbani, M., Vahidnia, M. H., & Aspanani, M. Planning to explore lime minerals using spectral angle mapper (SAM) processing and agent-based modeling (ABM). Journal of Geomatics Science and Technology, 0-0.
  • Jani, A. (2021). An agent-based model of repeated decision making under risk: modeling the role of alternate reference points and risk behavior on long-run outcomes. Journal of Business Economics, 1-27.
  • Jansens, R., Kingston, M., Morrison, B., Dubey, M., & Guerin, S. (2021). SIN (language).
  • Janssen, S. D., Viaene, K. P., Van Sprang, P., & De Schamphelaere, K. A. (2021). Integrating Bioavailability of Metals in Fish Population Models. Environmental Toxicology and Chemistry.
  • Jaramillo, D., Anderson, A., & Edington, C. (2021). Epidemiology model of Covid-19.
  • Jensen, M., Dignum, F., Vanhée, L., Păstrăv, C., & Verhagen, H. (2021). Agile Social Simulations for Resilience. In Social Simulation for a Crisis (pp. 379-408). Springer, Cham.
  • Jensen, M., Lorig, F., Vanhée, L., & Dignum, F. (2021). Deployment and Effects of an App for Tracking and Tracing Contacts During the COVID-19 Crisis. In Social Simulation for a Crisis (pp. 167-188). Springer, Cham.
  • Jensen, M., Vanhée, L., & Kammler, C. (2021). Social Simulations for Crises: From Theories to Implementation. In Social Simulation for a Crisis (pp. 39-84). Springer, Cham.
  • Jiang, H., Chen, C., Zhao, S., & Wu, Y. (2020). Evolution of a technology standard alliance based on an echo model developed through complex adaptive system theory. Complexity, 2020.
  • Jiang, L. (2021). Racial and Ethnic Disparities in Breastfeeding Practices and the Impact of Interventions in a Low-Income Population in Los Angeles County (Doctoral dissertation, UCLA).
  • Jiménez, A. F., Cárdenas, P. F., & Jiménez, F. (2022). Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales. Computers and Electronics in Agriculture, 192, 106635.
  • Jin, K. S., Lee, S. M., & Kim, Y. C. (2021). Adaptive and optimized agent placement scheme for parallel agent‐based simulation. ETRI Journal.
  • Jing, T., Meng, Q. H., & Ishida, H. (2021). Recent Progress and Trend of Robot Odor S.
  • Jiping, C., Lijie, C., Gang, D., & Bo, R. (2021). Simulation on Aviation Maintenance Support System Based on Goal-driven. Journal of System Simulation, 33(9), 2157.
  • Jo, C., Kim, D. H., & Lee, J. W. (2021). Sustainability of religious communities. Plos one, 16(5), e0250718.
  • Jungck, J. (2021). BIOLOGICAL MODELS FOR FINITE MATHEMATICS. PRIMUS, 1-43.
  • Kanters, H., Brughmans, T., & Romanowska, I. (2021). Sensitivity analysis in archaeological simulation: An application to the MERCURY model. Journal of Archaeological Science: Reports, 38, 102974.
  • Karchevskyi, M., & Karchevskaya, H. (2021, March). Agent-Based Modeling as a Method of Crime Research. In International Conference on Economics, Law and Education Research (ELER 2021) (pp. 117-121). Atlantis Press.
  • Kart, Ö., Genç, O. Ç., & Başçiftçi, F. (2021). Speed Compatible Green Wave Coridor with Internet of Objects (No. 6618). EasyChair.
  • Karyawati, A. E. (2021). Scenario modelling as planning evidence to improve access to emergency obstetric care in eastern Indonesia. In The Modal Future: A Theory of Future-Directed Thought and Talk (pp. 278-290). Cambridge: Cambridge University Press.
  • Kaspersen, M. H., Graungaard, D., Bouvin, N. O., Petersen, M. G., & Eriksson, E. (2021). Towards a model of progression in computational empowerment in education. International Journal of Child - Computer Interaction, 29, 100302.
  • Katan, J., & Perez, L. (2021). ABWiSE v1. 0: Toward and Agent-Based Approach to Simulating Wildfire Spread. Natural Hazards and Earth System Sciences Discussions, 1-27.
  • Katerndahl, D., Burge, S. K., & del Pilar Montanez Villacampa, M. (2021). Modeling Daily Partner Violence and Substance Use Based upon Couple’s Reporting. Journal of Interpersonal Violence, 08862605211050108.
  • Kato, D., Yada, S., & Kurahashi, S. (2021). Analysis of Factory Automated Guided Vehicles Systems Using Contract Net Protocol. In Agents and Multi-Agent Systems: Technologies and Applications 2021 (pp. 511-519). Springer, Singapore.
  • Kato, J. S., & Sbicca, A. (2021). Bounded Rationality, Group Formation and the Emergence of Trust: An Agent-Based Economic Model. Computational Economics, 1-29.
  • Kaufman, M., & Yuthas, K. (2021) Learning Analytics and Technology Through TeachingLearning Analytics through Teaching. Journal of Emerging Technologies in Accounting.
  • Kaur, S. (2021). A Framework to Study the Impact of Interventions on Social Isolation During Pandemics Using Multi-Agent Simulation (Doctoral dissertation, University of Windsor (Canada)).
  • Kautz, F., & Mallick, R. B. (2021). Simulation-Based Stochastic Method to Model Microcrack Coalescence in Asphalt Pavements: Concept Paper. Journal of Transportation Engineering, Part B: Pavements, 147(4), 06021003.
  • Ke, L., Sadler, T. D., Zangori, L., & Friedrichsen, P. J. (2021). Developing and Using Multiple Models to Promote Scientific Literacy in the Context of Socio-Scientific Issues. Science & Education, 1-19.
  • Kelter, J. Z., Emery, J. D., & Wilensky, U. (2021). A Multi-level Diffusion Unit: Connecting Submicro- and Macro-levels with Computational, Graphical, and Mathematical Representations. 2021 ASEE Virtual Annual Conference.
  • Kelter, J., Bugler, A., & Wilensky, U. (2021). Agent-Based Models of Quadratic Voting. In Z. Yang, & E. von Briesen (Eds.), Proceedings of the 2020 Conference of The Computational Social Science Society of the Americas (pp. 131-142). (Springer Proceedings in Complexity). Springer Science and Business Media B.V. https://doi.org/10.1007/978-3-030-83418-0_8.
  • Kelter, J., Peel, A., Bain, C., Anton, G., Dabholkar, S., Horn, M. S., & Wilensky, U. (2021). Constructionist co‐design: A dual approach to curriculum and professional development. British Journal of Educational Technology, 1043–1059. https://doi.org/10.1111/bjet.13084.
  • Khaddage, F., & Lattemann, C. (2021, July). Artificial Intelligence and Cloud-based Technologies to Empower Learning “Active Experiments via NetLogo”. In EdMedia+ Innovate Learning (pp. 15-21). Association for the Advancement of Computing in Education (AACE).
  • Khalil, H., & Wainer, G. CD2: An Automation Tool for Cell-DEVS CO2 Diffusion Models. Proc. SimAud2021.
  • Khan, I., & Cañamero, L. (2021, July). Adaptation-By-Proxy: Contagion Effect of Social Buffering in an Artificial Society. In ALIFE 2021: The 2021 Conference on Artificial Life. MIT Press.
  • Kianpour, M. (2021). Heterogeneous Preferences and Patterns of Contribution in Cybersecurity as a Public Good.
  • Kilkis, S., Prakasha, P. S., Naeem, N., & Nagel, B. (2021). A Python Modelling and Simulation Toolkit for Rapid Development of System of Systems Inverse Design (SoSID) Case Studies. In AIAA Aviation 2021 Forum (p. 3000).
  • Kilty, T. J., & Burrows, A. C. (2021). Secondary Science Preservice Teachers: Technology Integration in Methods and Residency. Journal of Science Teacher Education, 1-23.
  • Kim, S., Feng, B., Smith, K., Masoud, S., Zheng, Z., Szabo, C., & Loper, M. (2021). Toward Better Management of Potentially Hostile Crowds. In Proceedings of the 2021 Winter Simulation Conference (WSC).
  • Kim, W. (2021). Does START-UP NY Promote Firm Formation?. Nakhara: Journal of Environmental Design and Planning, 20, 105-105.
  • Kizhakkedath, A., & Tai, K. (2021). Vulnerability Analysis of Critical Infrastructure Network. International Journal of Critical Infrastructure Protection, 100472.
  • Klein, D., & Marx, J. Die epistemische Qualität demokratischer Entscheidungsverfahren. Interaktionseffekte zwischen eigennützigen, individuellen Überzeugungen und der epistemischen Qualität kollektiver Entscheidungen. Demokratie und Wahrheit.
  • Klopfer, E. (2021). The Complex Evolution of Technologies and Pedagogies for Learning about Complex Systems.
  • Knowe, M., & Gresalfi, M. (2021). Bridging the Divide: Exploring Affordances for Interdisciplinary Learning. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Kooshknow, A. R. M. M., Herber, R., & Ruzzenenti, F. (2021). Is electricity storage systems in the Netherlands indispensable or doable? Testing electricity storage business models with exploratory agent-based modeling. arXiv preprint arXiv:2112.11035.
  • Koppelaar, R., Marvuglia, A., & Rugani, B. (2021). Water Runoff and Catchment Improvement by Nature-Based Solution (NBS) Promotion in Private Household Gardens: An Agent-Based Model. In Rethinking Sustainability Towards a Regenerative Economy (pp. 91-114). Springer, Cham.
  • Koralewski, T. E., Wang, H. H., Grant, W. E., Brewer, M. J., Elliott, N. C., & Westbrook, J. K. (2021). Modeling the dispersal of wind-borne pests: Sensitivity of infestation forecasts to uncertainty in parameterization of long-distance airborne dispersal. Agricultural and Forest Meteorology, 301, 108357.
  • Kořínek, M., Tázlar, O., & Štekerová, K. (2021). Digital Twin Models: BIM Meets NetLogo.
  • Koshy-Chenthittayil, S., Archambault, L., Senthilkumar, D., Laubenbacher, R., Mendes, P., & Dongari-Bagtzoglou, A. (2021). Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review. Microorganisms, 9(2), 417.
  • Kostiou, V., Hall, M. W., Jones, P. H., & Hall, B. A. (2021). Simulations reveal that different responses to cell crowding determine the expansion of p53 and Notch mutant clones in squamous epithelia. Journal of the Royal Society Interface, 18(183), 20210607.
  • Kowarsch, D., & Yang, Z. (2021, July). The Impact of Housing Programs on Unsheltered Homeless Population: An Agent-Based Approach. In International Conference on Applied Human Factors and Ergonomics (pp. 84-92). Springer, Cham.
  • Král, B. J. Model komplexního systému: simulace šíření kůrovce.
  • Kurniawan, A. C., & Arvitrida, N. I. (2021). An agent-based simulation for a trade-off between frequency and depth in retail price promotion strategy. Management & Marketing, 16(1), 1-12.
  • Kussmaul, C., & Pirmann, T. (2021). Guided Inquiry Learning with Technology: Investigations to Support Social Constructivism. In CSEDU (1) (pp. 483-490).
  • Kwon, H. (2021). Refining Behavioural Theories and Rules in Agent-Based Models to Enhance Dynamic Simulation of Urban Change (Doctoral dissertation, University of Cambridge).
  • LaBarbera, K., & Scullen, J. C. (2021). Using individual capture data to reveal large-scale patterns of social association in birds. Journal of Ornithology, 1-17.
  • Lade, S. J., Anderies, J. M., Currie, P., & Rocha, J. C. Dynamical systems modelling. (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems, 359.
  • Laguna-Sánchez, G. A., & López-Sauceda, J. (2021). Modelo heurístico, para la dinámica de propagación de una enfermedad infecciosa. Contactos, Revista de Educación en Ciencias e Ingeniería, (119), 45-55.
  • Lal, C., & Marijan, D. (2021). Blockchain Testing: Challenges, Techniques, and Research Directions. arXiv preprint arXiv:2103.10074.
  • Lane, J. E., McCaffree, K., & Shults, F. L. (2021). Is radicalization reinforced by social media censorship?. arXiv preprint arXiv:2103.12842.
  • Lange, K. P., Korevaar, G., Oskam, I. F., Nikolic, I., & Herder, P. M. (2021). Agent-based Modelling and Simulation for Circular Business Model Experimentation. Resources, Conservation & Recycling Advances, 200055.
  • Langellier, B. A., Stankov, I., Hammond, R. A., Bilal, U., Auchincloss, A. H., Barrientos, T., ... & Roux, A. V. D. Potential impacts of policies to reduce purchasing of ultra-processed foods in Mexico at different stages of the social transition: an agent-based modeling approach. Public Health Nutrition, 1-24.
  • Larrain, N., & Groene, O. (2021). Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection. PloS one, 16(7), e0254334.
  • Latif, R., Ahmed, M. U., Tahir, S., Latif, S., Iqbal, W., & Ahmad, A. (2021). A novel trust management model for edge computing. Complex & Intelligent Systems, 1-17.
  • Le, N. T. T., Nguyen, P. A. H. C., & Hanachi, C. (2021, September). Agent-Based Modeling and Simulation of Citizens Sheltering During a Tsunami: Application to Da Nang City in Vietnam. In International Conference on Computational Collective Intelligence (pp. 199-211). Springer, Cham.
  • Lemanski, N., Silk, M., Fefferman, N., & Udiani, O. (2021). How territoriality reduces disease transmission among social insect colonies. Behavioral Ecology and Sociobiology, 75(12), 1-13.
  • Leoni, S. (2021). An Agent-Based Model for Tertiary Educational Choices in Italy. Research in Higher Education, 1-28.
  • Lermanda Sandoval, M. O. (2021). Modelo de sistema de detección de intrusos en red basado en especies indicadoras artificiales. (Thesis).
  • Levy, B., Windoloski, K., & Ludlam, J. (2021). Matrix and agent-based modeling of threats to a diamond-backed terrapin population. Mathematical Biosciences, 108672.
  • Levy, M., Peel, A., Dabholkar, S., Zhao, L., Juhl, S., Levites, L., Mills, J., Wu, S., Horn, M.S., & Wilensky, U.(2021). Co-Designing to Understanding Equity-Focus in Computational Thinking (CT) Integrated Science Curricula. Paper presented to the 2021 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Levy, M., Wu, S. P. W., Dabholkar, S., Horn, M. S., & Wilensky, U. (2021). Teachers' Sensemaking of CT Integration and Pedagogical Approaches. In Dabholkar S. (Symposium organizer), Integrating Computational Thinking in Science Curricula: Professional Development and Student Assessment. Presented at the 2021 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Lezia, A., Miano, A., & Hasty, J. (2021). Synthetic Gene Circuits: Design, Implement, and Apply. Proceedings of the IEEE.
  • Li, D., Li, C., & Gu, R. (2021). Evolutionary Game Analysis of Promoting Industrial Internet Platforms to Empower Manufacturing SMEs through Value Cocreation Cooperation. Discrete Dynamics in Nature and Society, 2021.
  • Li, H., Li, C., & Wang, Z. (2021). An agent-based model for exploring the impacts of reciprocal trust on knowledge transfer within an organization. Journal of Business & Industrial Marketing.
  • Li, K., Liu, Y., Wan, H., & Huang, Y. (2021). A discrete-event simulation model for the Bitcoin blockchain network with strategic miners and mining pool managers. Computers & Operations Research, 105365.
  • Li, L., Wang, J., Zhong, X., Lin, J., Wu, N., Zhang, Z., ... & Zhao, Y. (2022). Combined multi-objective optimization and agent-based modeling for a 100% renewable island energy system considering power-to-gas technology and extreme weather conditions. Applied Energy, 308, 118376.
  • Li, W., Cao, S., Hu, K., Cao, J., & Buyya, R. (2021). Blockchain-Enhanced Fair Task Scheduling for Cloud-Fog-Edge Coordination Environments: Model and Algorithm. Security and Communication Networks, 2021.
  • Li, Z., Pradena Miquel, M., & Pinacho-Davidson, P. (2022). Safety-Centric and Smart Outdoor Workplace: A New Research Direction and Its Technical Challenges. In Smart Trends in Computing and Communications (pp. 61-74). Springer, Singapore.
  • Liermann, V., & Dittmar, H. (2021). BSDS—Balance Sheet Dynamics Simulator (Application ABM). In The Digital Journey of Banking and Insurance, Volume I (pp. 137-159). Palgrave Macmillan, Cham.
  • Lin, S. Y., Hlynka, A. W., Xu, L., Lu, H., Sediek, O. A., El-Tawil, S., ... & Aguirre, B. (2021). Simple Run-Time Infrastructure (SRTI): An accessible distributed computing platform for interdisciplinary simulation. Journal of Computational Science, 101455.
  • Liu, C., Bhullar, M. S., Kaur, T., Kumar, J., Reddy, S. R. S., Singh, M., & Kaundun, S. S. (2021). Modelling the Effect and Variability of Integrated Weed Management of Phalaris minor in Rice-Wheat Cropping Systems in Northern India. Agronomy, 11(11), 2331.
  • Liu, H., Wu, M., Liu, X., Gao, J., Luo, X., & Wu, Y. (2021). Simulation of Policy Tools’ Effects on Farmers’ Adoption of Conservation Tillage Technology: An Empirical Analysis in China. Land, 10(10), 1075.
  • Liu, J., & Stacey, P. (2021). Modelling the effects of lockdown and social distancing in the management of the Global Coronavirus Crisis-Why the UK tier system failed.
  • Liu, W., & Agusdinata, D. B. (2021). Dynamics of local impacts in low-carbon transition: Agent-based modeling of lithium mining-community-aquifer interactions in Salar de Atacama, Chile. The Extractive Industries and Society, 100927.
  • Liu, Y., Gao, H., Cai, J., Lu, Y., & Fan, Z. (2021). Urbanization path, housing price and land finance: International experience and China’s facts. Land Use Policy, 105866.
  • Liu, Y., Yang, D., Timmermans, H. J., & de Vries, B. (2021). Simulating the effects of redesigned street-scale built environments on access/egress pedestrian flows to metro stations. Computational Urban Science, 1(1), 1-14.
  • Lloyd, S. J., & Chalabi, Z. (2021). Climate change, hunger and rural health through the lens of farming styles: An agent-based model to assess the potential role of peasant farming. Plos one, 16(2), e0246788.
  • López-Ortiz, E. J., Sancho-Caparrini, F., Martínez-del-Amor, M. Á., Soria-Morillo, L. M., & Álvarez-García, J. A. (2021). Hybrid agent-based methodology for testing response protocols. Knowledge-Based Systems, 107005.
  • Lorenz, F., & Jeyapragasan, G. (2020). The impact of climate change on tri-trophic interactions and crop production. The iScientist, 5(1), 4-12.
  • Lorenz, J. (2021). Epistemology of agent-based modeling. Handbook of Computational Social Science, Volume 1: Theory, Case Studies and Ethics, 13.
  • Lorig, F., Jensen, M., Kammler, C., Davidsson, P., & Verhagen, H. (2021). Comparative Validation of Simulation Models for the COVID-19 Crisis. In Social Simulation for a Crisis (pp. 331-352). Springer, Cham.
  • Love, C., Gresalfi, M., & Knowe, M. (2021). The Tragedy of Lost Ideas: Examining Epistemic Injustice in Pair Programming. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Lovellette, E., & Hexmoor, H. (2021). Lane and Speed Allocation Mechanism for Autonomous Vehicle Agents on a Multi-Lane Highway. Internet of Things, 100356.
  • Lu, P., & Chen, D. (2021). The life cycle model of Chinese empire dynamics (221 BC–1912 AD). The Journal of Mathematical Sociology, 1-37.
  • Lu, P., & Chena, D. (2021). The Life Cycle Model and Empire Dynamics of China.
  • Lu, P., Wen, F., Li, Y., & Chen, D. (2021). Multi-agent modeling of crowd dynamics under mass shooting cases. Chaos, Solitons & Fractals, 153, 111513.
  • Lu, P., Yang, H., Li, H., Li, M., & Zhang, Z. (2021). Swarm intelligence, social force and multi-agent modeling of heroic altruism behaviors under collective risks. Knowledge-Based Systems, 106725.
  • Lu, P., Yang, H., Li, M., & Zhang, Z. (2021). The sandpile model and empire dynamics. Chaos, Solitons & Fractals, 143, 110615.
  • Lu, P., Zhang, Z., & Li, M. (2021). Big data-drive agent-based modeling of online polarized opinions. Complex & Intelligent Systems, 1-18.
  • Lu, S., Wang, W., Cheng, Y., Yang, C., Jiao, Y., Xu, M., ... & Xu, J. (2021). Food-trade-associated COVID-19 outbreak from a contaminated wholesale food supermarket in Beijing. Journal of Biosafety and Biosecurity.
  • Lucherini, E., Sun, M., Winecoff, A., & Narayanan, A. (2021). T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems. arXiv preprint arXiv:2107.08959.
  • Lwin, T., Koike, T., & Dang, J. (2021). A RATIONALIZED SEISMIC DESIGN METHOD FOR BUILDINGS IN EARTHQUAKE-PRONE DEVELOPING COUNTRIES. ASEAN Engineering Journal, 11(4), 266-279.
  • Mabey, C. S., Armstrong, A. G., Mattson, C. A., Salmon, J. L., Hatch, N. W., & Dahlin, E. C. (2021). A computational simulation-based framework for estimating potential product impact during product design. Design Science, 7.
  • Maclay, G. J., & Ahmad, M. (2021). An agent based force vector model of social influence that predicts strong polarization in a connected world. Plos one, 16(11), e0259625.
  • Madamba, T. (2021). Simulating The Effects Of Cross-Contamination Of Escherichia Coli O157: H7 On Fresh-Cut Lettuce During Post-Harvest Processing From An Agent Based Perspective (Doctoral dissertation).
  • Madeira, L. M., Furtado, B. A., & Dill, A. R. (2021). VIDA: A simulation model of domestic VIolence in times of social DistAncing. arXiv preprint arXiv:2101.04057.
  • Mahfooz Ul Haque, H., Saleem, K., & Salman Khan, A. Modeling belief‐desire‐intention reasoning agents for situation‐aware formalisms. Concurrency and Computation: Practice and Experience, e6417.
  • Mahmud, S., Asadi, A., LaCrue, A. R., Akter, T., Hernandez, S., & Pinkley, S. N. (2021). A hybrid agent-based simulation and optimization approach for statewide truck parking capacity expansion. Procedia Computer Science, 184, 33-41.
  • Mahon, C. L., & Pelech, S. (2021). Guidance for analytical methods to cumulative effects assessment for terrestrial species. Environmental Reviews, 29(999), 1-24.
  • Maier-Speredelozzi, V., & Still, B. (2021). Robust Project-Based Organizations for the Design of Complex Engineered Systems. Project Management Journal, 87569728211014369.
  • Maiwald, J., & Schuette, T. (2021). Decentralised Electricity Markets and Proactive Customer Behaviour. Energies 2021, 14, 781.
  • Majid, M. H. A., Arshad, M. R., & Mokhtar, R. M. (2022). Swarm Robotics Behaviors and Tasks: A Technical Review. Control Engineering in Robotics and Industrial Automation, 99-167.
  • Maldonado Castañeda, C. E., Acevedo-Supelano, A. L., Bustacara, M., González-Martínez, C. J., Trujillo Perdomo, J. F., Millán-Hernández, E. M., ... & Silva González, S. L. (2021). Modelamiento basado en agentes (MBA) en estudios de salud pública.
  • Mamboleo, A. A., Doscher, C., & Paterson, A. (2021). A computational modelling approach to human-elephant interactions in the Bunda District, Tanzania. Ecological Modelling, 443, 109449.
  • MANSOORİ, H., GHORBANİ, M., & KOHANSAL, M. R. (2021). Simulation the Effects of climate change and market prices on farm’s structure by using an agent based model. Journal of Agricultural Sciences.
  • Marín-Lora, C., Chover, M., & Sotoca, J. M. (2021, October). A Multi-agent Specification for the Tetris Game. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 169-178). Springer, Cham.
  • Marks, R. (2021). Thirty-Five Years of Computational Economics.
  • Marsay, K. S., Greaves, S., Mahabaleshwar, H., Ho, C. M., Roehl, H., Monk, P. N., ... & Partridge, L. J. (2021). Tetraspanin Cd9b and Cxcl12a/Cxcr4b have a synergistic effect on the control of collective cell migration. Plos one, 16(11), e0260372.
  • Martin, K. & Wilensky U. (2021). How do ants know what to do? Paper presented at the 2021 Annual Meeting of the American Education Research Association (AERA).
  • Martin, K., Horn, M., & Wilensky, U. (2021). Constructivist Dialogue Mapping: A Comparison of Museum Experience. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 1081-1082). Bochum, Germany: International Society of the Learning Sciences.
  • Martinez, I., Bruse, J. L., Florez-Tapia, A. M., Viles, E., & Olaizola, I. G. (2021). ArchABM: an agent-based simulator of human interaction with the built environment. $ CO_2 $ and viral load analysis for indoor air quality. arXiv preprint arXiv:2111.01484.
  • Maruthasalam, A. P. P., Roy, D., & Venkateshan, P. (2021). Modelling driver's reactive strategies in e-hailing platforms: an agent-based simulation model and an approximate analytical model. International Journal of Production Research, 1-19.
  • Marvuglia, A., Bayram, A., Baustert, P., Gutiérrez, T. N., & Igos, E. (2021). Agent-based modelling to simulate farmers’ sustainable decisions: Farmers’ interaction and resulting green consciousness evolution. Journal of Cleaner Production, 129847.
  • Marvuglia, A., Koppelaar, R., & Rugani, B. (2020). The effect of green roofs on the reduction of mortality due to heatwaves: Results from the application of a spatial microsimulation model to four European cities. Ecological Modelling, 438, 109351.
  • Maryasin, O. Y. (2021). Bee-Inspired Algorithm for Groups of Cyber-Physical Robotic Cleaners with Swarm Intelligence. Cyber-Physical Systems: Modelling and Intelligent Control, 167-177.
  • Maszczyk, P., Tałanda, J., Babkiewicz, E., Leniowski, K., & Urban, P. (2021). Daphnia depth selection in gradients of light intensity from different artificial sources: An evolutionary trap?. Limnology and Oceanography.
  • Matsunami, N., Okuhara, S., & Ito, T. (2021). Reward Design for Multi-Agent Reinforcement Learning with a Penalty Based on the Payment Mechanism. Transactions of the Japanese Society for Artificial Intelligence, 36(5), AG21-H_1.
  • Maturo, A., Petrucci, A., Forzano, C., Giuzio, G. F., Buonomano, A., & Athienitis, A. (2021). Design and environmental sustainability assessment of energy-independent communities: The case study of a livestock farm in the North of Italy. Energy Reports.
  • Maw, Y. Y., & Tun, M. T. (2021). SENSITIVITY ANALYSIS OF ANGLE, LENGTH AND BRIM HEIGHT OF THE DIFFUSER FOR THE SMALL DIFFUSER AUGMENTED WIND TURBIN. ASEAN Engineering Journal, 11(4), 280-291.
  • McKelvey, A. (2021). Learning and Integrating CALL Practices to Support English Language Learners: A Case Study of K-12 Classroom Teachers and Their Professional Development (Doctoral dissertation, University of Wyoming).
  • McVeigh, J., & MacLachlan, M. Psychological Governance and COVID-19: A Case Study in Macropsychology. In M. MacLachlan & J. McVeigh (Eds.), Macropsychology: A Population Science for Sustainable Development Goals, 303.
  • Mestari, M. (2021). Evolutionary Heuristic for Avoiding Traffic Jams in Road Network Using A* Search Algorithm. In Innovations in Smart Cities Applications Volume 4: The Proceedings of the 5th International Conference on Smart City Applications (p. 423). Springer Nature.
  • Metcalf, S. J., Reilly, J. M., Jeon, S., Wang, A., Pyers, A., Brennan, K., & Dede, C. (2021). Assessing computational thinking through the lenses of functionality and computational fluency. Computer Science Education, 1-25.
  • Meza, A., Ari, I., Al-Sada, M. S., & Koç, M. (2021). Future LNG competition and trade using an agent-based predictive model. Energy Strategy Reviews, 38, 100734.
  • Miller Neilan, R., Majetic, G., Gil-Silva, M., Adke, A. P., Carrasquillo, Y., & Kolber, B. J. (2021). Agent-based modeling of the central amygdala and pain using cell-type specific physiological parameters. PLOS Computational Biology, 17(6), e1009097.
  • Miller, B. W., & Frid, L. (2021). A new approach for representing agent-environment feedbacks: coupled agent-based and state-and-transition simulation models. Landscape Ecology, 1-16.
  • Miller, L. (2021). Consumer Preferences and Associated Price Premiums for Agricultural Traits in Maine Markets.
  • Milne, R. J., Cotfas, L. A., & Delcea, C. (2021). Minimizing health risks as a function of the number of airplane boarding groups. Transportmetrica B: Transport Dynamics, 1-22.
  • Minh, C. C., & Van Noi, N. (2021). Optimising truck arrival management and number of service gates at container terminals. Maritime Business Review.
  • Miri, F., & Pazzi, R. (2021). A Comprehensive Survey on the Convergence of Vehicular Social Networks and Fog Computing. arXiv preprint arXiv:2112.00143.
  • Mittal, A., Gibson, N. O., Krejci, C. C., & Marusak, A. A. (2021). Crowd-shipping for urban food rescue logistics. International Journal of Physical Distribution & Logistics Management.
  • Miyazawa, A., & Didier, A. (2021). Ana Cavalcanti Pedro Ribeiro.
  • Mohammadi, V., Rahmani, A. M., Darwesh, A., & Sahafi, A. (2021). Trust-based friend selection algorithm for navigability in social Internet of Things. Knowledge-Based Systems, 107479.
  • Mohammadiun, S., Hu, G., Gharahbagh, A. A., Li, J., Hewage, K., & Sadiq, R. (2021). Intelligent Computational Techniques in Marine Oil Spill Management: A Critical Review. Journal of Hazardous Materials, 126425.
  • Mohammed, S. J., & Hasson, S. T. (2021, April). A developed Clustering Approach to Model the Data Dissemination Types in a Highway. In 2021 1st Babylon International Conference on Information Technology and Science (BICITS) (pp. 332-336). IEEE.
  • Molin, L. D., Kanwal, J., & Stone, C. (2021, June). Resource availability and the evolution of cooperation in a 3D agent-based simulation. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 93-101).
  • Möller, R., Furnari, A., Battiato, S., Härmä, A., & Farinella, G. M. (2021). A Survey on Human-aware Robot Navigation. arXiv preprint arXiv:2106.11650.
  • Montagud, A., de León, M. P., & Valencia, A. (2021). Systems Biology at the giga-scale: large multi-scale models of complex, heterogeneous multicellular systems. Current Opinion in Systems Biology, 100385.
  • More, S., Bampidis, V., Benford, D., Bragard, C., Halldorsson, T., Hernández-Jerez, A., Bennekou, S. H., Koutsoumanis, K., Machera, K., Naegeli, H., Nielsen, S. S., Schlatter, J., Schrenk, D., Silano, V., Turck, D., Younes, M., Arnold, G., Dorne, J. L. Maggiore, A., ... Rortais, A. (2021). A systems-based approach to the environmental risk assessment of multiple stressors in honey bees.
  • Moškon, M., Komac, R., Zimic, N., & Mraz, M. (2021). Distributed biological computation: from oscillators, logic gates and switches to a multicellular processor and neural computing applications. Neural Computing and Applications, 1-16.
  • Mostafizi, A., Koll, C., & Wang, H. (2021). A Decentralized and Coordinated Routing Algorithm for Connected and Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems.
  • Mota, F. P., Steffens, C. R., Adamatti, D. F., Botelho, S. S. D. C., & Rosa, V. (2021). A persuasive multi-agent simulator to improve electrical energy consumption. Journal of Simulation, 1-15.
  • Moya, I., Bermejo, E., Chica, M., & Cordón, Ó. (2021). Coral reefs optimization algorithms for agent-based model calibration. Engineering Applications of Artificial Intelligence, 100, 104170.
  • Moya, I., Chica, M., Sáez-Lozano, J. L., & Cordón, Ó. (2021). Simulating the influence of terror management strategies on the voter ideological distance using agent-based modeling. Telematics and Informatics, 101656.
  • Mponela, P. (2021). Options for sustainable agricultural intensification in maize mixed farming systems.
  • Mudrak, G., & Semwal, S. K. (2021, November). Using Agent Based Modeling to Frame Autonomous Vehicle Navigation as Complex Systems. In Proceedings of the Future Technologies Conference (pp. 154-172). Springer, Cham.
  • Mudrak, G., & Semwal, S. K. (2021). Autonomous Vehicle Decision Making and Urban Infrastructure Optimization. In Intelligent Computing (pp. 1190-1202). Springer, Cham.
  • Mulyono, N. B., Pambudi, N. F., Ahmad, L. B., & Adhiutama, A. (2021). Determining response time factors of emergency medical services during the COVID-19 pandemic. International Journal of Emergency Services.
  • Mumford, E. (2021). Marginalized Indigenous Knowledge and Contemporary Swedish Colonialism: The Case of Reindeer Husbandry in Gällivare Forest Sámi Community.
  • Munthali, K. G. (2021). Analysing Road Traffic Situation in Lilongwe: An Agent Based Modelling (ABM) Approach. Advanced Journal of Graduate Research, 10(1), 3-15.
  • Murukutla, S. A., Koushik, S. B., Chinthala, S. P. R., Bobbillapati, A., & Kandaswamy, S. (2021, October). A Simple Agent Based Modeling Tool for Plastic and Debris Tracking in Oceans. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 139-150). Springer, Cham.
  • Musaeus, L. H., & Musaeus, P. (2021, June). Computing and Gestures in High School Biology Education. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 533-539).
  • Mysterud, A., Viljugrein, H., Rolandsen, C. M., & Belsare, A. V. (2021). Harvest strategies for the elimination of low prevalence wildlife diseases. Royal Society Open Science, 8(3), 210124.
  • Nadimi, N., & Eshlaghi, A. T. (2021). Hybrid of System Dynamics-Agent Based Analysis of Mobile Operators Revenue The Case: Digital Service Entry of MCCI Company. Journal of Industrial Management Studies, 19(60), 51-84.
  • Nagaraj, R. K., & D’Souza, M. (2021). A verifiable multi-agent framework for dependable and adaptable avionics. Sādhanā, 46(1), 1-27.
  • Nagasawa, R., Mas, E., Moya, L., & Koshimura, S. (2021). Model-based analysis of multi-UAV path planning for surveying postdisaster building damage. Scientific Reports, 11(1), 1-14.
  • Nakanishi, H., Han, W., Muminovic, M., & Qu, T. (2021). An Agent-Based Bushfire Visualisation to Support Urban Planning: A Case Study of the South Coast, NSW 2019–2020. In Urban Informatics and Future Cities (pp. 371-386). Springer, Cham.
  • Narayanan, B. L., Yosri, A., Ezzeldin, M., El-Dakhakhni, W., & Dickson-Anderson, S. (2021). A complex network theoretic approach for interdependence investigation: An application to radionuclide behavior in the subsurface. Computers & Geosciences, 104913.
  • Naugle, A., Verzi, S., Lakkaraju, K., Swiler, L., Warrender, C., Bernard, M., & Romero, V. (2021). Feedback density and causal complexity of simulation model structure. Journal of Simulation, 1-11.
  • Navasartian, K. (2021). Simulating the Spread and Containment of COVID-19: An Agent-Based Modelling Approach (Doctoral dissertation, Wien).
  • Negahban, A., & Giabbanelli, P. J. (2021). Hybrid Agent-Based Simulation of Adoption Behavior and Social Interactions: Alternatives, Opportunities, and Pitfalls. IEEE Transactions on Computational Social Systems.
  • Nerrise, F. (2021, May). Predictive Agent-Based Modeling of Natural Disasters Using Machine Learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 18, pp. 15976-15977).
  • Neumayr, R. R. (2021). Agent-Based Semiology: Optimizing Office Occupation Patterns with Agent-Based Simulations. In Formal Methods in Architecture (pp. 49-59). Springer, Cham.
  • Newton, A. C., Evans, P. M., Watson, S. C., Ridding, L. E., Brand, S., McCracken, M., ... & Bullock, J. M. (2021). Ecological restoration of agricultural land can improve its contribution to economic development. PloS one, 16(3), e0247850.
  • Nganro, S., Trisutomo, S., Barkey, R., Ali, M., Imura, H., Onishi, A., ... & Mahamud, M. A. (2021). Prediction of Future Land Use and Land Cover (LULC) in Makassar City. TATALOKA, 23(2), 183-189.
  • Nguyen, G. T. H., Nong, D. H., & See, L. (2021). Simulating the Spatial Distribution of Pollutant Loads from Pig Farming using an Agent-based Modeling Approach. Environmental Science and Pollution Research(preprint). Vietnam National University of Agriculture.
  • Nguyen, J., Powers, S. T., Urquhart, N., Farrenkopf, T., & Guckert, M. (2021). An Overview of Agent-based Traffic Simulators. arXiv preprint arXiv:2102.07505.
  • Ni, L., Bausch, G., & Benjamin, R. (2021). Computer science teacher professional development and professional learning communities: a review of the research literature. Computer Science Education, 1-32.
  • Nöldeke, B., Winter, E., Laumonier, Y., & Simamora, T. (2021). Simulating Agroforestry Adoption in Rural Indonesia: The Potential of Trees on Farms for Livelihoods and Environment. Land, 10(4), 385.
  • Nsor, M., & Kapale, K. (2020). Visualization and Simulation of Traffic Flow. Bulletin of the American Physical Society, 65.
  • Ntankouo Njila, R. C., Mostafavi, M. A., & Brodeur, J. (2021). A Decentralized Semantic Reasoning Approach for the Detection and Representation of Continuous Spatial Dynamic Phenomena in Wireless Sensor Networks. ISPRS International Journal of Geo-Information, 10(3), 182.
  • Nunes, A. M., Zwick, M., & Wakeland, W. (2021). Sensitivity analysis of an agent-based simulation model using reconstructability analysis. International Journal of General Systems, 50(3), 319-338.
  • Nur, N. (2021). Developing Temporal Machine Learning Approaches to Support Modeling, Explaining, and Sensemaking of Academic Success and Risk of Undergraduate Students (Doctoral dissertation, The University of North Carolina at Charlotte).
  • Ofori, R. O., & Rouleau, M. D. (2021). Modeling the impacts of floating seaweeds on fisheries sustainability in Ghana. Marine Policy, 127, 104427.
  • Ogunsakin, R., Marin, C. A., & Mehandjiev, N. (2021). Towards engineering manufacturing systems for mass personalisation: a stigmergic approach. International Journal of Computer Integrated Manufacturing, 1-29.
  • Oh, J., & Kim, S. (2021). Distributed Trust Management for Fog Based IoT Environment. Journal of the Korea Institute of Information Security & Cryptology, 31(4), 731-751.
  • Oh, W. S., Yu, D. J., & Muneepeerakul, R. (2021). Efficiency-fairness trade-offs in evacuation management of urban floods: The effects of the shelter capacity and zone prioritization. PloS one, 16(6), e0253395.
  • Oliveira, N., & Secchi, D. (2021). Theory Building, Case Dependence, and Researchers’ Bounded Rationality: An Illustration From Studies of Innovation Diffusion. Sociological Methods & Research, 0049124120986201.
  • Olzer, R. (2021). The Costs (And Benefits) of Standing Out: Alternative Reproductive Behavior and Novel Trait Evolution in the Pacific Field Cricket (Doctoral dissertation, University of Minnesota).
  • Onggo, B. S. & Utomo, D. S. (2021). Dairy supply chain in West Java: Modelling using agent-based simulation and reporting using the stress guidelines. In M. Fakhimi, D. Robertson, & T. Boness (Eds.), Proceedings of the Operational Research Society Simulation Workshop 2021 (SW21). https://doi.org/10.36819/SW21.032
  • Orjuela-Garzon, W., Quintero, S., Giraldo, D. P., Lotero, L., & Nieto-Londoño, C. (2021). A Theoretical Framework for Analysing Technology Transfer Processes Using Agent-Based Modelling: A Case Study on Massive Technology Adoption (AMTEC) Program on Rice Production. Sustainability 2021, 13, 11143.
  • Ornstein, J. T., & Hammond, R. A. (2021). Agent-Based Modeling in the Social Sciences. New Horizons in Modeling and Simulation for Social Epidemiology and Public Health, 22.
  • Orozco-Rivera, J., Ceballos, Y., & Castillo-Grisales, J. A. (2022). Análisis del alto flujo vehicular para una vía de acceso a Medellín usando simulación basada en agentes. Revista UIS Ingenierías, 21(1), 73-82.
  • Otto Syk, M. (2021). Geopolitics of Finance; Modelling the role of states in the international financial system.
  • Ouseph, S. V. V. (2021). A modern paradigm for cloud computing adoption that brings into account the deployment organization's main concerns.
  • Overwater, A., & Yorke-Smith, N. (2021). Agent-based simulation of short-term peer-to-peer rentals: Evidence from the Amsterdam housing market. Environment and Planning B: Urban Analytics and City Science, 23998083211000747.
  • Ozarisoy, B., & Altan, H. (2021). A novel methodological framework for the optimisation of post-war social housing developments in the South-eastern Mediterranean climate: Policy design and life-cycle cost impact analysis of retrofitting strategies. Solar Energy, 225, 517-560.
  • Ozawa, S., Chen, H. H., Rao, G. G., Eguale, T., & Stringer, A. (2021). Value of pneumococcal vaccination in controlling the development of antimicrobial resistance (AMR): Case study using DREAMR in Ethiopia. Vaccine.
  • Özgür, K. A. R. T., Genç, O. Ç., & BASCİFTCİ, F. (2021). Speed Compatible Green Wave Corridor with The Internet of Things. Avrupa Bilim ve Teknoloji Dergisi, (28), 411-416.
  • Ozik, J., Wozniak, J. M., Collier, N., Macal, C. M., & Binois, M. (2021). A Population Data-Driven Workflow for COVID-19 Modeling and Learning.
  • Pahl, C. C., & Ruedas, L. A. (2021). Carnosaurs as Apex Scavengers: Agent-based simulations reveal possible vulture analogues in late Jurassic Dinosaurs. Ecological Modelling, 458, 109706.
  • Palatnik, A., & Abrahamson, D. (2021). Escape from Plato’s cave: An enactivist argument for learning 3d geometry by constructing tangible models. Submitted to CERME12, TWG4.
  • Panjaitan, J. R. H., & Gozan, M. (2021). TECHNO-ECONOMIC EVALUATION OF NITROCELLULOSE PRODUCTION FROM PALM OIL EMPTY FRUIT BUNCHES. ASEAN Engineering Journal, 11(4), 246-254.
  • Paoletti, J., Bisbey, T. M., Zajac, S., Waller, M. J., & Salas, E. (2021). Looking to the Middle of the Qualitative-Quantitative Spectrum for Integrated Mixed Methods. Small Group Research, 1046496421992433.
  • Papageorgiou, A., Chaitanya Munjulury, R., Gårdhagen, R., Amadori, K., & Jouannet, C. (2021). Development of Analysis Capabilities for the Preliminary Sizing and Evaluation of Unmanned Airborne Early Warning Aircraft. In AIAA AVIATION 2021 FORUM (p. 2452).
  • Papageorgiou, A., Ölvander, J., Amadori, K., & Jouannet, C. (2021). Development of Analysis and Simulation Models for Evaluating Airborne Radar Surveillance System of Systems. In AIAA Scitech 2021 Forum (p. 0303).
  • Pardos, Z. A., Rosenbaum, L. F., & Abrahamson, D. (2021). Characterizing learner behavior from touchscreen data. International Journal of Child-Computer Interaction, 100357. https://doi.org/10.1016/j.ijcci.2021.100357
  • Păstrăv, C., Jensen, M., Mellema, R., & Vanhée, L. (2021). Social Simulations for Crises: From Models to Usable Implementations. In Social Simulation for a Crisis (pp. 85-117). Springer, Cham.
  • Patarakin, E., Vachkova, S., & Burov, V. (2021). Agent-based modeling of teacher interaction within a repository of digital objects. In Education and City: Education and Quality of Living in the City (pp. 5013-5013).
  • Patel, J., Katan, J., Perez, L., & Sengupta, R (2021). Transferring decision boundaries onto a geographic space: Agent rules extracted from movement data using classification trees. Transactions in GIS.
  • Pathak, A., Mohan, V. M., & Banerjee, A. (2021). Optimal lockdown strategies: All about time.
  • Paul, R., Behjat, L., & Brennan, R. (2021). USING INDIVIDUAL-BASED MODELING TO BETTER UNDERSTAND THE HIDDEN CURRICULUM OF ENGINEERING. Proceedings of the Canadian Engineering Education Association (CEEA).
  • Paunova, M. (2021). How do they Integrate?: Social Exchange and Reciprocal Integration Among Migrants and Locals. In 5th European Conference on Social Network.
  • Pavlic, T. P., Hanson, J., Valentini, G., Walker, S. I., & Pratt, S. C. (2021). Quorum sensing without deliberation: biological inspiration for externalizing computation to physical spaces in multi-robot systems. Swarm Intelligence, 1-33.
  • Pavlović, B., Ivezić, D., & Živković, M. (2021). State and perspective of individual household heating in Serbia: A survey-based study. Energy and Buildings, 111128.
  • Peel, A., Dabholkar, S., Wu, S., Horn, M.S., Wilensky, U. (2021). An Evolving Definition of Computational Thinking in Science and Mathematics Classrooms. Proceedings of the 5th APSCE International Computational Thinking and STEM in Education Conference 2021, (pp. 119-122).
  • Peel, A., Kelter, J., Horn, M., & Wilensky, U. (2021). Designing professional learning experiences to support teachers' computational thinking learning and confidence. Presented at the 2021 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Peel, A., Kelter, J., Wilensky, U., & Horn, M. (2021). Supporting the Integration of Computational Thinking and Science Through Professional Development and Co-design. Presented to the Annual Meeting of the Association of Science Teacher Education (ASTE) 2021. Salt Lake City, UT.
  • Peel, A., Kelter, J., Zhao, L., Horn, M.S., Wilensky, U. (2022). A Design-Based Research Methodology Utilizing Conjecture Mapping to Frame Embedded Co-design Cycles. Paper accepted to the 2022 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Pereira, A., Laureano, R. M., Neto, F., & Macedo, J. (2021, June). Computer simulation of diabetic retinopathy screening adherence: Agent based model with fuzzy logic. In 2021 16th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE.
  • Perez, A. Z., Bone, C., & Stenhouse, G. (2021). Simulating multi-scale movement decision-making and learning in a large carnivore using agent-based modelling. Ecological Modelling, 452, 109568.
  • Perry, G. L. (2021). How far might plant-eating dinosaurs have moved seeds? Biol. Lett. 17 20200689 http://doi.org/10.1098/rsbl.2020.0689.
  • Perry, G. L., Brazier, R. E., & Wilmshurst, J. M. (2021). The role of paleoecology in understanding landscape-level ecosystem dynamics. The Routledge Handbook of Landscape Ecology.
  • Persson, A. (2021). Urban or else? A combined method analysis of the discourse of the Swedish state during the years 2018-2020 on the choice situation of rural youths to move or remain.
  • Pestle, W. J., Hubbell, C., & Hubbe, M. (2021). (DIGSS) Determination of Intervals using Georeferenced Survey Simulation: An R package for subsurface survey. PloS one, 16(9), e0257386.
  • Pfoser, A. Z., & Wenk, C. Towards Large-Scale Agent-Based Geospatial Simulation.
  • Pham, L. M., Parlavantzas, N., Le, H. H., & Bui, Q. H. (2021). Towards a Framework for High-Performance Simulation of Livestock Disease Outbreak: A Case Study of Spread of African Swine Fever in Vietnam. Animals, 11(9), 2743.
  • Phetheet, J., Hill, M. C., Barron, R. W., Gray, B. J., Wu, H., Amanor-Boadu, V., ... & Rossi, M. W. (2021). Relating agriculture, energy, and water decisions to farm incomes and climate projections using two freeware programs, FEWCalc and DSSAT. Agricultural Systems, 193, 103222.
  • Pian, Y., Peng, J., Xu, L., Wu, P., & Li, J. (2021, December). Analysis and simulation optimization of passenger flow in urban rail transit station. In Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021) (Vol. 12058, pp. 262-271). SPIE.
  • Pichon co-directeur, F. Une nouvelle politique d’exécution de simulations stochastiques fondée sur des principes de partitionnement, de sélection et de clonage (Doctoral dissertation, Université d’Artois).
  • Pidiha, N. (2021). Evolution of AEC Project Networks: an Agent-Based Modeling Approach (Doctoral dissertation, Michigan State University).
  • Pietzsch B.W., Peter F.J. and Berger U. (2021) The Effect of Sanitation Felling on the Spread of the European Spruce Bark Beetle=97An Individual-Based Modeling Approach. Front. For. Glob. Change 4:704930. https://doi.org/10.3389/ffgc.2021.704930
  • Pillai, M. S., Chaudhary, G., Khari, M., & Crespo, R. G. (2021). Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning. Soft Computing, 1-12.
  • Pilny, A. (2021). Computational Methods for Studying Group Communication. In The Emerald Handbook of Group and Team Communication Research. Emerald Publishing Limited.
  • Pineda Ramos, J. F. (2021). Enseñanza y Aprendizaje de los Números Complejos a través de la Historia y la Geometría Dinámica.
  • Pingkuo, L., & Huan, P. (2021). What drives the green and low-carbon energy transition in China?: An empirical analysis based on a novel framework. Energy, 122450.
  • Plantec, E., Aquilanti, L., & Belorgey, R. (2021). Vers l'adaptation de comportements par le biais de l'évolution culturelle pour des essaims de robots autonomes (Doctoral dissertation, LORIA, UMR 7503, Université de Lorraine, CNRS, Vandoeuvre-lès-Nancy).
  • Plazas Escudero, D., Cárdenas-Rodríguez, J. S., & Restrepo Sierra, M. (2021). Agent-Based Model for Studying Diabetes under the Influence of Relationships. Cuadernos de Ingeniería Matemática, 1(01), 1-9.
  • Pommerening, A. (2021). Doctoral Studies and All That. In Staying on Top in Academia (pp. 33-50). Springer, Cham.
  • Ponziani, F. A., Tinaburri, A., Eudes, V. S. G., & Zip, C. Some Effect Of Interpersonal Distance Constraints In Modeling Wayout Finding From An Exhibition Hall.
  • Pot, V., Portell, X., Otten, W., Garnier, P., Monga, O., & Baveye, P. C. Accounting for soil architecture and microbial dynamics in microscale models: Current practices in soil science and the path ahead. European Journal of Soil Science.
  • Pratama, R. A. R. J., & Rusdan, M. (2021). Effectiveness of Rastra Bulog Rice Distribution Using Agent Based Modeling and Simulation Tools. Almana: Jurnal Manajemen dan Bisnis, 5(1), 84-92.
  • Pray, I. W., Pizzitutti, F., Bonnet, G., Gonzalez-Gustavson, E., Wakeland, W., Pan, W. K., ... & Cysticercosis Working Group in Peru. (2021). Validation of a spatial agent-based model for Taenia solium transmission (“CystiAgent”) against a large prospective trial of control strategies in northern Peru. PLoS neglected tropical diseases, 15(10), e0009885.
  • Prinz, A. (2021). Teaching Language Engineering Using MPS. In Domain-Specific Languages in Practice (pp. 315-336). Springer, Cham.
  • QIU, L. P., & YANG, L. H. (2021). The research of co-evolution mechanisms between cross-border e-commerce and manufacturing cluster: An Agent-based model. In E3S Web of Conferences (Vol. 235, p. 03048).
  • Qureshi, A., & Ahmad, K. (2021, November). Agents and Secure Contracts in Cyber-Physical Systems: A Simulation. In Proceedings of the Future Technologies Conference (pp. 533-551). Springer, Cham.
  • Racine, E. E., & Bryson, J. J. (2021). Epidemic modeling as a means to reimagine health education and policy post-COVID. Health Education.
  • Radchuk, V., Kramer-Schadt, S., Berger, U., Scherer, C., Backmann, P., & Grimm, V. (2021). Individual-based models. Demographic Methods Across the Tree of Life, 213.
  • Raees, M., Khan, T. A., Mustafa Abbasi, K., Ahmed, A., Fazilat, S., & Ahmed, I. (2021). Context-Aware Services Using MANETs for Long-Distance Vehicular Systems: A Cognitive Agent-Based Model. Scientific Programming, 2021.
  • Rahimi, M., Navimipour, N. J., Hosseinzadeh, M., Moattar, M. H., & Darwesh, A. (2021). Toward the efficient service selection approaches in cloud computing. Kybernetes.
  • Railsback, S. F., & Arcata, C. A. (2021). InSALMO 7 Model Description.
  • Raimbault, J. (2021). A multiscale model of urban morphogenesis. arXiv preprint arXiv:2103.17241.
  • Raimbault, J. (2021). Simulating urban dynamics and international governance of transportation infrastructure projects. arXiv preprint arXiv:2108.13915.
  • Raimbault, J. (2021). Strong coupling between scales in a multi-scalar model of urban dynamics. arXiv preprint arXiv:2101.12725.
  • Rajak, B., Mallick, S., & Gaurav, K. (2021). Role of Information Communication and Technology at Kumbha Mela–2019 (Prayagraj). Ilkogretim Online, 20(5).
  • Rakotonarivo, S., Bell, A., Abernethy, K., Minderman, J., Duthie, A., Redpath, S., ... & Bunnefeld, N. (2021). The role of incentive-based instruments and social equity in conservation conflict interventions. Ecology and Society, 26(2).
  • Ramadhan, R., Salman, F., Mori, A., & Abdoellah, O. S. (2021). Shifting Cultivation, Palm Oil Plantation and Indirect Deforestation: A Study on Dusun Tonggong, Parindu, West Kalimantan, Indonesia. Journal of Sustainable Forestry, 1-20.
  • Rambu Ngana, F., & Eka Karyawati, A. A. I. N. (2021). Scenario modelling as planning evidence to improve access to emergency obstetric care in eastern Indonesia. Plos one, 16(6), e0251869.
  • Ramkumar, S., & Oh, W. S. (2021). AIforGoodSimulator-Modeling Covid-19 Spread and Potential Interventions in Refugee Camps v1. 0.0. CoMSES Computational Model Library.
  • Ramkumar, S., Mueller, M., Pyka, A., & Squazzoni, F. (2021). Diffusion of eco-innovation through inter-firm network targeting: An agent-based model. Journal of Cleaner Production, 130298.
  • Ratnadass, A., Avelino, J., Fernandes, P., Letourmy, P., Babin, R., Deberdt, P., ... & Van Den Berg, J. (2021). Synergies and tradeoffs in natural regulation of crop pests and diseases under plant species diversification. Crop Protection, 105658.
  • Reed, S. K. Complex Systems (pp. 209-220). Oxford University Press.
  • Regnath, F., Berger, C., & Mahdavi, A. (2022, May). The impact of occupants' energy awareness and thermal preferences on buildings' performance. In CLIMA 2022 conference.
  • Reinhardt, O., Warnke, T., & Uhrmacher, A. M. (2022). Agent-Based Modelling and Simulation with Domain-Specific Languages. In Towards Bayesian Model-Based Demography (pp. 113-134). Springer, Cham.
  • Ren, B., Wang, L., Wang, X., & Chen, J. (2021). Simulating Energy-Saving and Consuming Behaviours in the Design and Manufacturing Process with Adjacent Networks. In Recent Advances in Sustainable Energy and Intelligent Systems (pp. 441-451). Springer, Singapore.
  • Resende, L. P. A. O papel da construção de nicho na evolução e ecologia da socialidade em aranhas.
  • Retzlaff, C. O., Ziefle, M., & Valdez, A. C. (2021). The History of Agent-Based Modeling in the Social Sciences. In International Conference on Human-Computer Interaction (pp. 304-319). Springer, Cham.
  • Revuelta, E. C., Chávez, M. J., Vera, J. A. B., Rodríguez, Y. F., & Sánchez, M. C. (2021). Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms. Measurement, 108898.
  • Reyes-Mercado, P. FinTech Strategy: Linking Entrepreneurship, Finance, and Technology. Springer Nature.
  • Roanes-Lozano, E., Solano-Macías, C., & Roanes-Macías, E. A simplified introduction to virus propagation using Maple's Turtle Graphics package.
  • Robinson, J. T. (2021). Development of an Agent-Based Model to Recapitulate Murine Patellar Tendon Healing as a Function of Age (Doctoral dissertation, Tulane University School of Science and Engineering).
  • Rocha, É. G. D., Brigatti, E., Niebuhr, B. B., Ribeiro, M. C., & Vieira, M. V. (2021). Dispersal movement through fragmented landscapes: the role of stepping stones and perceptual range. Landscape Ecology, 1-19.
  • Rodriguez-Lopez, J. M., Schickhoff, M., Sengupta, S., & Scheffran, J. (2021). Technological and social networks of a pastoralist artificial society: agent-based modeling of mobility patterns. Journal of Computational Social Science, 1-27.
  • Rollins, M. L., & Griffen, B. D. (2021). Optimal Memory in Food-caching Organisms.
  • Romanowska, I., Wren, C., & Crabtree, S. (2021). Agent-Based Modeling for Archaeology: Simulating the Complexity of Societies. Santa Fe, NM: SFI Press.
  • Romero-Mujalli, D., Rochow, M., Kahl, S., Paraskevopoulou, S., Folkertsma, R., Jeltsch, F., & Tiedemann, R. Adaptive and non-adaptive plasticity in changing environments: implications for sexual species with different life history strategies. Authorea Preprints.
  • Rothrock, L., Abraham, A., Graf, A., Rodopman, M., & Nold, D. (2021). Aiding decision makers to reopening of places of worship. Human Factors and Ergonomics in Manufacturing & Service Industries.
  • Roxburgh, N., Stringer, L. C., Evans, A., GC, R. K., Malleson, N., & Heppenstall, A. (2021). Nepal Stressor Interaction Model (Nepal SIM).
  • Saba, J., Hel-Or, H., & Levy, S. T. (2021). Much. Matter. in. Motion: learning by modeling systems in chemistry and physics with a universal programing platform. Interactive Learning Environments, 1-20.
  • Saldanha, J., Adamatti, D. F., & Dimuro, G. (2021). Social identity theory applied to the game of self-regulation of social exchanges based on multiagent systems. Journal of Simulation, 1-14.
  • Saleem, K., & Haque, H. M. U. (2020). Modelling Situation-Aware Formalism Using BDI Reasoning Agents. In Context-Aware Systems and Applications, and Nature of Computation and Communication (pp. 169-181). Springer, Cham.
  • Sambuaga, R. D., & Lee, H. S. (2021). Optimized Evacuation Plan and Decision Support System Development with Agent-Based Modelling and GIS Analysis for Tsunami Events in Pandeglang, Banten, Indonesia. Journal of Coastal Research, 114(sp1), 509-513.
  • Samon, S., & Levy, S. T. (2021). The Role of Physical and Computer-Based Experiences in Learning Science Using a Complex Systems Approach. Science & Education, 1-37.
  • Sandoval-Félix, J., Castañón-Puga, M., & Gaxiola-Pacheco, C. G. (2021). Analyzing Urban Public Policies of the City of Ensenada in Mexico Using an Attractive Land Footprint Agent-Based Model. Sustainability, 13(2), 714.
  • Sandoval, S. M., & Alvarado-Monroy, A. (2021). La modelización como vehículo para el desarrollo del razonamiento covariacional en educación secundaria. Quadrante, 30(2), 147-178.
  • Sari, R. F., Ilmananda, A. S., & Romano, D. M. (2021). Social trust-based blockchain-enabled social media news verification system. Journal of Universal Computer Science, 27(9), 979-998.
  • Saunders, D. (2021). How to Put the Cart Behind the Horse in the Cultural Evolution of Gender. Philosophy of the Social Sciences, 00483931211049770.
  • Saxe, J. G. (2021). Claudio Cioffi-Revilla. Handbook of Computational Social Science, Volume 1: Theory, Case Studies and Ethics, 2.
  • Schloesser, D. S., Hollenbeck, D., & Kello, C. T. (2021). Individual and collective foraging in autonomous search agents with human intervention. Scientific Reports, 11(1), 1-13.
  • Schlüter, M., Lindkvist, E., Wijermans, N., & Polhill, G. Agent-based modelling.(2021). The Routledge Handbook of Research Methods for Social-Ecological Systems, 383.
  • Schmolke, A., Bartell, S. M., Roy, C., Desmarteau, D., Moore, A., Cox, M. J., ... & Brain, R. Applying a Hybrid Modeling Approach to Evaluate Potential Pesticide Effects and Mitigation Effectiveness for an Endangered Fish in Simulated Oxbow Habitats. Environmental Toxicology and Chemistry.
  • Schön, S., Marcus, C., Amadori, K., & Jouannet, C. (2021). Integration of Multi-Fidelity Models with Agent-Based Simulation for System of Systems. In AIAA AVIATION 2021 FORUM (p. 2996).
  • Schoville, B. J., Brown, K. S., & Wilkins, J. (2021). A Lithic Provisioning Model as a Proxy for Landscape Mobility in the Southern and Middle Kalahari. Journal of Archaeological Method and Theory, 1-26.
  • Schulz, J., & Mayerhoffer, D. M. (2021). Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms. Journal of Business Economics, 1-29.
  • Scorrano, M., & Danielis, R. (2021). Simulating electric vehicle uptake in Italy in the small-to-medium car segment: A system dynamics/agent-based model parametrized with discrete choice data. Research in Transportation Business & Management, 100736.
  • Secchi, D. (2021). An Unusual Diffusion Model. In Computational Organizational Cognition: A Study on Thinking and Action in Organizations. Emerald Publishing Limited.
  • Secchi, D. (2021). The Operational Boundaries of Docility. In Computational Organizational Cognition: A Study on Thinking and Action in Organizations. Emerald Publishing Limited.
  • Sedigh, A. H. A., Purvis, M. K., Savarimuthu, B. T. R., Frantz, C. K., & Purvis, M. A. (2021). Impact of different belief facets on agents' decision - A refined cognitive architecture to model the interaction between organisations' institutional characteristics and agents' behavior. In A. Aler Tubella, S. Cranefield, C. Frantz, F. Meneguzzi, & W. Vasconcelos (Eds.), Coordination, organizations, institutions, norms, and ethics for governance of multi-agent systems XIII: International Workshops COIN 2017 and COINE 2020, Sao Paulo, Brazil, May 8–9, 2017 and Virtual Event, May 9, 2020, Revised Selected Papers, p. 133. Springer Nature.
  • Şendurur, P., & Sendurur, E. (2022). Students as Gamers: Design, Code, and Play. In Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning (pp. 868-887). IGI Global.
  • Sengupta, S., Scheffran, J., & Kovalevsky, D. (2021, April). A Single-Agent Urban Coastal Adaptation Model: Adaptive decision-making within the VIABLE modeling framework. In EGU General Assembly Conference Abstracts (pp. EGU21-12752).
  • Sengupta, S., Scheffran, J., & Kovalevsky, D. (2021). Agent Adaptation in an Urban Coastal Scenario: Applying the VIABLE Framework. 12. Deutsche Klimatagung, Online-Tagung, 15. bis 18. März 2021.
  • Shaaban, M., & Scheffran, J. (2021). A Dynamic-Agent-Based Sustainability Assessment of Energy Systems. In Energy Systems Evaluation (Volume 1) (pp. 161-181). Springer, Cham.
  • Shafqat, F., Khan, M. N. A., & Shafqat, S. (2021). SmartHealth: IoT-Enabled Context-Aware 5G Ambient Cloud Platform. In IoT in Healthcare and Ambient Assisted Living (pp. 43-67). Springer, Singapore.
  • Shaharuddin, R. A., & Misro, M. Y. (2021, November). Traffic simulation using agent based modelling. In AIP Conference Proceedings (Vol. 2423, No. 1, p. 020035). AIP Publishing LLC.
  • Shaheen, J. (2021). Are Agent-based Models Universal Approximators. Academia Letters, 2.
  • Shanaa, M., & Abdallah, S. (2020, November). Agent-based simulation for COVID-19 outbreak within a semi-closed environment. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) (pp. 231-236). IEEE.
  • Shandera, S., Matsick, J. L., Hunter, D. R., & Leblond, L. (2021). RASE: Modeling cumulative disadvantage due to marginalized group status in academia. PloS one, 16(12), e0260567.
  • Shapiro, B., & Crooks, A. (2021, July). Kinetic Action and Radicalization: A Case Study of Pakistan. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 321-330). Springer, Cham.
  • Sherard, M. K., & Petrosino, A. J. (2021). Language, Modeling and Power: A Methodology for Analyzing Discourse in Interaction. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Shi, Z., Li, Y., & Jaberi-Douraki, M. (2021). Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes. PLOS Computational Biology, 17(9), e1009413.
  • Shiang, C. W., & Hussain, N. (2021). Modelling of Crowd Evacuation with Communication Strategy using Social Force Model. Journal of Optimization in Industrial Engineering.
  • Shiflet, A. B., Shiflet, G. W., & Pendarvis, M. P. (2020). Biology for the Global Citizen, A New Non-Majors Biology Text with Laboratories Using Computer Simulations.
  • Shiwei, Y. U. A. N., Xin, L. I., & Erhu, D. U. (2021). Progress and Prospect of Agent-Based Modeling for Water Resources Management. Advances in Earth Science, 36(9), 899-910.
  • Shojaati, N., & Osgood, N. D. (2021). Dynamic Computational Models and Simulations of the Opioid Crisis: A Comprehensive Survey. ACM Transactions on Computing for Healthcare (HEALTH), 3(1), 1-25.
  • Shoole, A. A., & Wadi, M. (2021). Multiagent systems application for the smart grid protection. Renewable and Sustainable Energy Reviews, 149, 111352.
  • Shou, W., Wang, J., & Wu, P. (2021). The application of simulation in lean production research: a critical review and future directions. Engineering, Construction and Architectural Management.
  • Siar, S. A., Keramati, M., & Motadel, M. (2021). Agent-Based Simulation of Consumer Behavior in Impulse Buying. Journal of Industrial Management Studies, 19(62), 99-138.
  • Siddiqui, J., Mehjabeen, M., & Stapleton, P. (2021). Emergence of corporate political activities in the guise of social responsibility: dispatches from a developing economy. Accounting, Auditing & Accountability Journal.
  • Siddiqui, S. Y., Ahmad, I., Khan, M. A., Khan, B. S., Ali, M. N., Naseer, I., ... & Usama, H. M. (2021). AIoT Enabled Traffic Congestion Control System Using Deep Neural Network.
  • Silva, E. M., Moura, G., & Da Silva, S. (2021). Monetary Policy Experiments in an Agent-Based Macroeconomic Model. Open Access Library Journal, 8(5), 1-14.
  • Silva, M. I. G., Silva, R. A. G., López, H. A. J., & Ontiveros, A. A. (2021). A mechanism of Individualistic Indirect Reciprocity with internal and external dynamics. arXiv preprint arXiv:2105.14144.
  • Silva, T. (2021). Complexity theory and the historical study of religion: navigating the transdisciplinary space between the Humanities and the Natural Sciences. História da Historiografia: International Journal of Theory and History of Historiography, 14(36), 167-196.
  • Silva, T. F. D., Araújo, M. S., Ferro Junior, R. J. C., Costa, L. F. D., Andrade, J. P. B., & Campos, G. A. L. D. (2021, November). Intelligent Agents for Observation and Containment of Malicious Targets Organizations. In Brazilian Conference on Intelligent Systems (pp. 48-63). Springer, Cham.
  • Silveira, N. J. C., Ferraz, D., de Mello, D. S., Polloni-Silva, E., do Nascimento Rebelatto, D. A., & Moralles, H. F. (2021). Determinants of Absorptive Capacity: a systematic literature review. Revista Gestão da Produção Operações e Sistemas, 16(2), 122.
  • Singh, P., Kaur, A., Batth, R. S., Aujla, G. S., & Masud, M. (2021). Service vs Protection: A Bayesian Learning Approach for Trust Provisioning in Edge of Things Environment. IEEE Internet of Things Journal.
  • Singh, S., France, A. M., Chen, Y. H., Farnham, P. G., Oster, A. M., & Gopalappa, C. (2021). Progression and transmission of HIV (PATH 4.0)-A new agent-based evolving network simulation for modeling HIV transmission clusters. Mathematical Biosciences and Engineering, 18(3), 2150-2181.
  • Smarzhevskiy, I. A. (2021). The Simulation Model of Collective Management Decision-Making Implemented in Netlogo. In Industry Competitiveness: Digitalization, Management, and Integration: Volume 2 (pp. 349-357). Springer International Publishing.
  • Smith, A. P. (2021). neworder: a dynamic microsimulation framework for Python. Journal of Open Source Software, 6(63), 3351.
  • Sobkowicz, P., & Sobkowicz, A. (2021). Agent Based Model of Anti-Vaccination Movements: Simulations and Comparison with Empirical Data. Vaccines, 9(8), 809.
  • Sobkowicz, P.(2021) The Role of Chance in Individual Sports: an Agent-Based Approach for Fencing Tournaments.
  • Sonawane, C., Yirga, G., & Carter, N. H. Public health and economic benefits of spotted hyenas Crocuta crocuta in a peri‐urban system. Journal of Applied Ecology.
  • Sood, S. K., Sood, V., & Mahajan, I. (2021). An intelligent healthcare system for predicting and preventing dengue virus infection. Computing, 1-39.
  • Souidi, M. E. H., Maarouk, T. M., & Ledmi, A. (2021). Multi-agent Ludo Game Collaborative Path Planning based on Markov Decision Process. In Inventive Systems and Control (pp. 37-51). Springer, Singapore.
  • Spanoudakis, N. I. (2021). Engineering Multi-agent Systems with Statecharts. SN Computer Science, 2(4), 1-21.
  • Spurný, J., Kopeček, I., Ošlejšek, R., Plhák, J., & Caputo, F. (2021). The prisoner’s dilemma in the workplace: how cooperative behavior of managers influence organizational performance and stress. Kybernetes.
  • Squire, K. D. (2021). From virtual to participatory learning with technology during COVID-19. E-Learning and Digital Media, 20427530211022926.
  • Staffini, A., Svensson, A. K., Chung, U. I., & Svensson, T. (2021). An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study. JMIR Medical Informatics, 9(4), e24192.
  • Steinbacher, M., Raddant, M., Karimi, F., Cuena, E., Alfarano, S., Iori, G., & Lux, T. (2021). Advances in the Agent-based Modeling of Economic and Social Behavior. SN Business & Economics.
  • Steinberg, S., & Gresalfi, M. (2021). Agency and Expressivity in Programming Play. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Strawhacker, A., Kafai, Y., T. Giang, M., Fields, D., & Tofel-Grehl, C. (2021, June). Designing the Virtual SPIKEY-20 Epidemic: Engaging Youth in Seeking Information and Using Personal Protection. In Interaction Design and Children (pp. 558-562).
  • Su, M., Cho, J. Y., Chi, M. T., Boucher, N., & Vanbibber, B. Designing Simulation Module to Diagnose Misconceptions in Learning Natural Selection.
  • Su, Y., Jiang, X., & Lin, Z. (2021). Simulation and Relationship Strength: Characteristics of Knowledge Flows Among Subjects in a Regional Innovation System. Science, Technology and Society, 09717218211020476.
  • Subrahmanyam, V. S. C., Raman, A. V., Krishna, S. S., Sitharthan, I., Basha, S. S., Prabavathy, B., & Deborah, S. A. (2021). Smart Warehouse Management System. In Recent Trends in Renewable Energy Sources and Power Conversion (pp. 99-114). Springer, Singapore.
  • Sulis, E., & Tambuscio, M. (2020). Simulation of misinformation spreading processes in social networks: An application with NetLogo. In G. Webb, Z. Zhang, V. S. Tseng, G. Williams, M. Vlachos, & L. Cao (Eds.), International Conference on Data Science and Advanced Analytics (DSAA) (pp. 614-618). IEEE. https://doi.org/10.1109/DSAA49011.2020.00086
  • Sulis, E., & Terna, P. (2021). An Agent-based Decision Support for a Vaccination Campaign. Journal of Medical Systems, 45(11), 1-7.
  • Sun, T., Bu, F., Liu, X., & Fu, Y. (2020, December). Modeling and Simulation of Group Drug-related Incident Evolution. In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (pp. 1247-1252). IEEE.
  • Swanson, H., Sherin, B., & Wilensky, U. (2021). Refining student thinking through computational modeling. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Swanson, H., Sherin, B., Wilensky, U. (2021). Characterizing student theory building in computational modeling activities. Proceedings of the 15th International Conference of the Learning Sciences. Bochum, Germany: International Society of the Learning Sciences.
  • Taghavi, A., Khaleghparast, S., & Eshghi, K. (2021). Optimal Agent Framework: A Novel, Cost-Effective Model Articulation to Fill the Integration Gap between Agent-Based Modeling and Decision-Making. Complexity, 2021.
  • Tałanda, J. (2021). The effect of artificial light at night on interactions between planktivorous fish and their cladoceran prey.
  • Tancredi, S., Abdu, R., Abrahamson, D., & Balasubramaniam, R. (2021). Modeling nonlinear dynamics of fluency development in an embodied-design mathematics learning environment with Recurrence Quantification Analysis. International Journal of Child-Computer Interaction, 100297. https://doi.org/10.1016/j.ijcci.2021.100297
  • Tejera Linares, M. D. C. (2021). Aprendiendo de la enfermedad COVID-19.
  • ten Broeke, G., van Voorn, G., Ligtenberg, A., & Molenaar, J. (2021). The Use of Surrogate Models to Analyse Agent-Based Models. Journal of Artificial Societies and Social Simulation, 24(2).
  • Termos, A., Picascia, S., & Yorke-Smith, N. (2021). Agent-Based Simulation of West Asian Urban Dynamics: Impact of Refugees. Journal of Artificial Societies and Social Simulation, 24(1), 1-2.
  • Terna, P. Matematica e simulazione per la morfologia e la dinamica spaziale. Volume 10-Numero 4-Luglio 2020, 177.
  • Ternes, P., Ward, J. A., Heppenstall, A., Kumar, V., Kieu, L. M., & Malleson, N. (2021). Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters. Open Research Europe, 1(131), 131.
  • Thasnimol, C. M., & Rajathy, R. (2021). An ideal solution for the deployment of photo voltaic generators using an agent-based nash differential evolution (NashDE) algorithm. International Journal of Emerging Electric Power Systems.
  • Thneibat, M., Thneibat, M., Al-Shattarat, B., & Al-kroom, H. (2021). Development of an agent-based model to understand the diffusion of value management in construction projects as a sustainability tool. Alexandria Engineering Journal.
  • Thomas, S. R. (2021). Effects of Transformational Leadership and Employability on Employee Retention: An Agent-Based Model (Doctoral dissertation, Arizona State University).
  • Thompson, J., Zhao, H., Seneviratne, S., Byrne, R., Vidanaarachichi, R., & McClure, R. (2021). Improving speed of models for improved real-world decision-making. SocArXiv sqy8c, Center for Open Science, 10.31219/osf.io/sqy8c.
  • Tian, D., Zhang, M., Zhao, A., Wang, B., Shi, J., & Feng, J. (2021). Agent-based modeling and simulation of edible fungi growers' adoption behavior towards fungal chaff recycling technology. Agricultural Systems, 103138.
  • Tiram, E., & Sinuany-Stern, Z. (2021). Overview of Simulation in Higher Education: Methods and Applications. In Handbook of Operations Research and Management Science in Higher Education (pp. 81-115). Springer, Cham.
  • Townsend, D. (2021). Validation and Inference of Agent Based Models. arXiv preprint arXiv:2107.03619.
  • Trentesaux, D. (2021). A Multi-agent Model for the Multi-plant Multi-product Physical Internet Supply Chain Network. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA 2020, 435.
  • Trentesaux, D., & Chauvin, C. (2021). A Benchmarking Platform for Human-Machine Cooperation in Cyber-Physical Manufacturing Systems. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA 2020, 313.
  • Trentesaux, D., & Karnouskos, S. (2021). Engineering ethical behaviors in autonomous industrial cyber-physical human systems. Cognition, Technology & Work, 1-14.
  • Troitzsch, K. G. (2021). Formal Design Methods and the Relation Between Simulation Models and Theory: A Philosophy of Science Point of View. In Pathways Between Social Science and Computational Social Science (pp. 21-45). Springer, Cham.
  • Troitzsch, K. G. (2021). Validating Simulation Models: The Case of Opinion Dynamics. In Pathways Between Social Science and Computational Social Science (pp. 123-155). Springer, Cham.
  • Truong, V. T., Baverel, P. G., Lythe, G. D., Vicini, P., Yates, J. W., & Dubois, V. F. (2021). Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models. CPT: Pharmacometrics & Systems Pharmacology.
  • Tsompanas, M. A., Fyrigos, I. A., Ntinas, V., Adamatzky, A., & Sirakoulis, G. C. (2021). Cellular automata implementation of Oregonator simulating light-sensitive Belousov–Zhabotinsky medium. Nonlinear Dynamics, 1-13.
  • Tucker-Raymond, E., Cassidy, M., & Puttick, G. (2021). Science teachers can teach computational thinking through distributed expertise. Computers & Education, 104284.
  • Tucker, G. E., Hutton, E. W., Piper, M. D., Campforts, B., Gan, T., Barnhart, K. R., ... & Syvitski, J. (2021). CSDMS: A community platform for numerical modeling of Earth-surface processes. Geoscientific Model Development Discussions, 1-40.
  • Tulang, A. B. (2021). Online Learning Communities Amid the COVID-19 Pandemic: An Agent-Based Model. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 6294-6302.
  • Tullis, J. G., & Fraundorf, S. H. (2021). Selecting effectively contributes to the mnemonic benefits of self-generated cues. Memory & Cognition, 1-17.
  • Uhde, A., & Hassenzahl, M. (2021). Simulating Social Acceptability With Agent-based Modeling. arXiv preprint arXiv:2105.06730.
  • Uzzo, S. M., Cramer, C. B., Sayama, H., & Faux, R. (2021). NetSci High: Bringing Agency to Diverse Teens Through the Science of Connected Systems. Northeast Journal of Complex Systems (NEJCS), 3(2), 2.
  • Valente, J. A., Caceffo, R., Bonacin, R., dos Reis, J. C., Gonçalves, D. A., & Baranauskas, M. C. C. (2021). Embodied‐based environment for kindergarten children: Revisiting constructionist ideas. British Journal of Educational Technology.
  • Van Buskirk, A. N., Rosenberry, C. S., Wallingford, B. D., Domoto, E. J., McDill, M. E., Drohan, P. J., & Diefenbach, D. R. (2021). Modeling how to achieve localized areas of reduced white-tailed deer density. Ecological Modelling, 109393.
  • Vanhée, L. (2021). Engineering Social Simulations for Crises. In Social Simulation for a Crisis (pp. 353-378). Springer, Cham.
  • Vasishta, A. (2021). Understanding Ideal Social Networking Strategies Based on Relational Mobility and Environmental Stability.
  • Vasishta, Angela, (2021). Understanding Ideal Social Networking Strategies Based on Relational Mobility and Environmental Stability Undergraduate Honors Theses. Paper 1731.
  • Vasylieva, O., Butvin, B., & Shtyfurak, Y. (2021). Methodological Approach to Agent-Based Modeling of Social Networks (No. 6291). EasyChair.
  • Vázquez, G. C., Cristóbal, R. V., Romero, E. A., & Alonso, J. U. (2021). Dinámicas de asistencia al supermercado “El Farolito” en condiciones de pandemia. Política y Cultura, (55), 151-175.
  • Vázquez, G. C., Cristóbal, R. V., Romero, E. A., & Alonso, J. U. (2021). Dinámicas de asistencia al supermercado “El Farolito” en condiciones de pandemia. Política y Cultura, (55), 151-175.
  • Veldt, N., Benson, A. R., & Kleinberg, J. (2021). Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components. arXiv preprint arXiv:2110.14859.
  • Velghe, F., De Wilde, F., Snellinx, S., Farahbakhsh, S., Belderbos, E., Peral, C., ... & Dietrich, T. (2021). Volatile Fatty Acid platform–a cornerstone for the circular bioeconomy. FEMS Microbiology Letters.
  • Veloso, P., & Krishnamurti, R. (2021). Mapping generative models for architectural design. The Routledge Companion to Artificial Intelligence in Architecture, 29.
  • Veloso, P., & Krishnamurti, R. (2021). Self-learning Agents for Spatial Synthesis. In Formal Methods in Architecture (pp. 265-276). Springer, Cham.
  • Vermeer, W. H., Smith, J. D., Wilensky, U., & Brown, C. H. (2021). High-Fidelity Agent-Based Modeling to Support Prevention Decision-Making: an Open Science Approach. Prevention Science, 1-12.
  • Vodopivec, N., Adam, C., & Chanteau, J. P. (2021). Modeling opinion leader's role in the diffusion of innovation. arXiv preprint arXiv:2101.11260.
  • Walzberg, J., Burton, R., Zhao, F., Frost, K., Muller, S., Carpenter, A., & Heath, G. (2022). An investigation of hard-disk drive circularity accounting for socio-technical dynamics and data uncertainty. Resources, Conservation and Recycling, 178, 106102.
  • Wang, J. (2021). Understanding Carsharing-Facilitating Neighborhood Preferences. (Thesis).
  • Wang, J., Yin, J., Khan, R. U., Wang, S., & Zheng, T. (2021). A Study of Inbound Logistics Mode Based on JIT Production in Cruise Ship Construction. Sustainability 2021, 13, 1588.
  • Wang, W. (2021). Spatial Analysis of COVID-19 Risk Based on Different Lockdown Strategies-a Case Study for Storrs Campus Community, University of Connecticut. Authorea Preprints.
  • Wang, Y. Y., & Bu, F. L. (2021, August). Emotional Interaction Computing of Actors in the Mass Incidents. In International Conference on Intelligent Computing (pp. 18-30). Springer, Cham.
  • Wang, Y., Ge, J., & Comber, A. (2021). Simulation model of pedestrian flow based on multi-agent system and Bayesian Nash equilibrium. AGILE: GIScience Series, 2, 1-7.
  • Wang, Y., Zhang, Q., Li, Q., Wang, J., Sannigrahi, S., Bilsborrow, R., ... & Song, C. (2021). Role of social networks in building household livelihood resilience under payments for ecosystem services programs in a poor rural community in China. Journal of Rural Studies.
  • Wang, Z. (2021). SIMULATION-BASED TSUNAMI EVACUATION RISK ASSESSMENT AND RISK-INFORMED MITIGATION (Doctoral dissertation, Colorado State University).
  • Wang, Z. Y., Shi, P. J., Zhang, X. B., Wang, Y. S., & Xie, X. Y. (2021). Simulation of Lanzhou urban land expansion based on multi-agent model. Ying Yong Sheng tai xue bao= The Journal of Applied Ecology, 32(6), 2169-2179.
  • Wang, Z., & Chen, A. (2021). On ISRC Rumor Spreading Model for Scale-Free Networks with Self-Purification Mechanism. Complexity, 2021.
  • Wang, Z., & Jia, G. (2021). Simulation-Based and Risk-Informed Assessment of the Effectiveness of Tsunami Evacuation Routes Using Agent-Based Modeling: A Case Study of Seaside, Oregon. International Journal of Disaster Risk Science, 1-21.
  • Wang, Z., & Jia, G. (2021). Tsunami evacuation risk assessment and probabilistic sensitivity analysis using augmented sample-based approach. International Journal of Disaster Risk Reduction, 102462.
  • Wasesa, M., Ramadhan, F. I., Nita, A., Belgiawan, P. F., & Mayangsari, L. (2021). Impact of overbooking reservation mechanism on container terminal’s operational performance and greenhouse gas emissions. The Asian Journal of Shipping and Logistics.
  • Watzek, J., Hauber, M. E., Jack, K. M., Murrell, J. R., Tecot, S. R., & Brosnan, S. F. (2021). MODELLING Collective Decision-Making: Insights Into COLLECTIVE anti-predator Behaviors From AN Agent-Based APPROACH. Behavioural Processes, 104530.
  • Webster, R. (2021). Dynamic Causal Inference Using a Hardware Implementation of Spiking Neurons (Doctoral dissertation, University of Otago).
  • Wens, M. L., van Loon, A. F., Veldkamp, T. I., & Aerts, J. C. (2021). Education, financial aid and awareness can reduce smallholder farmers’ vulnerability to drought under climate change. Natural Hazards and Earth System Sciences Discussions, 1-36.
  • White, A. L., & Gaff, H. D. (2021). Application and Modeling of a Tick-Killing Robot, TickBot. In The Mathematics of Patterns, Symmetries, and Beauties in Nature (pp. 31-57). Springer, Cham.
  • Widiyanto, S., Adi, D., Nurdin, N., & Fadila, F. (2021). Agent-Based Simulation for Evaluating the Effect of Different Walking and Driving Speed on Disaster Evacuation in Aceh. IPTEK The Journal of Engineering, 7(2), 50-58.
  • Will, M., Groeneveld, J., Frank, K., & Müller, B. (2021). Informal risk-sharing between smallholders may be threatened by formal insurance: Lessons from a stylized agent-based model. PloS one, 16(3), e0248757.
  • Williams, Timothy. Agent-Based Modeling of Resilience in Smallholder Agriculture: Toward Robust Models and Equitable Outcomes. Diss. 2021.
  • Wilsdorf, P., Wolpers, A., Hilton, J., Haack, F., & Uhrmacher, A. M. (2021). Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns. arXiv preprint arXiv:2109.06776.
  • Wojcieszak, M., Sobkowicz, P., Yu, X., & Bulat, B. (2021). What Information Drives Political Polarization? Comparing the Effects of In-group Praise, Out-group Derogation, and Evidence-based Communications on Polarization. The International Journal of Press/Politics, 19401612211004418.
  • Wooditch, A. (2021). The Benefits of Patrol Officers Using Unallocated Time for Everyday Crime Prevention. Journal of Quantitative Criminology, 1-25.
  • Wu, A. S., Mathias, H. D., Giordano, J. P., & Pherwani, A. (2021). Collective control as a decentralized task allocation testbed.
  • Wu, H., & Zhou, Y. (2021). Optimal degree of openness in open innovation: A perspective from knowledge acquisition & knowledge leakage. Technology in Society, 67, 101756.
  • Wu, R., Wang, Z., & Shi, Q. (2021). Increment of Heterogeneous Knowledge in Enterprise Innovation Ecosystem: An Agent-Based Simulation Framework. Complexity, 2021.
  • Wu, S. P. W., Anton, G., Bain, C., Peel, A. N., Horn, M. S., & Wilensky, U. (2021). Tools and resources for integrating computational thinking into your science classes. Presented at the NSTA National Conference 2021, Chicago, IL.
  • Wu, S. P. W., Peel, A., Bain, C., Horn, M. S. & Wilensky, U. (2021). Different Paths, Same Direction: How Teachers Learn Computational Thinking in STEM Practices through Professional Development. In Looi, C.K., Wadhwa, B., Dagiené, V., Seow, P., Kee, Y.H., & Wu, L.K. (Eds.) Proceedings of the 5th APSCE International Computational Thinking and Stem in Education Conference (CTE) (pp.52-58).
  • Wu, S., Horn, M., & Wilensky, U. (Apr 2021). Positioning teachers as co-designers to integrate CT practices in STEM. Related Paper-Set on Integrating Computational Thinking in Science Curricula: Teacher Professional Development and Student Assessment. NARST Annual Conference (NARST 2021). Online Presentation.
  • Wu, S., Jones, B. (*), Swanson, H., Horn, M., Wilensky, U. (2021). A Tale of Two PDs: Exploring Teachers' Experiences in Co-designing Computational Activities. Proceedings of the 15th International Conference of the Learning Sciences. Bochum, Germany: International Society of the Learning Sciences.
  • Wu, S., Wang, X., & Su, J. (2021). Statistical analysis of the community lockdown for COVID-19 pandemic. Applied Intelligence, 1-18.
  • Xiang, F., Chen, K., Su, J., Liu, H., & Zhang, W. (2021). Penetration Planning and Design Method of Unmanned Aerial Vehicle Inspired by Biological Swarm Intelligence Algorithm. Wireless Communications and Mobile Computing, 2021.
  • Xiang, L., Shen, G. Q., Li, D., Tan, Y., & Jin, X. (2021). A Multi-Agent Platform to Explore Strategies for Age-Friendly Community Projects in Urban China. The Gerontologist.
  • Xiong, M., Wang, Y., & Cheng, Z. (2021, December). Research on Modeling and Simulation of Information Cocoon Based on Opinion Dynamics. In 2021 The 9th International Conference on Information Technology: IoT and Smart City (pp. 161-167).
  • Xu, L., Mak, S., & Brintrup, A. (2021). Will bots take over the supply chain? Revisiting Agent-based supply chain automation. International Journal of Production Economics, 108279.
  • Xue, X., Chen, F., Zhou, D., Wang, X., Lu, M., & Wang, F. Y. (2021). Computational Experiments for Complex Social Systems--Part I: The Customization of Computational Model. IEEE Transactions on Computational Social Systems.
  • Yadav, A., & Berthelsen, U. D. (Eds.). (2021). Computational Thinking in Education: A Pedagogical Perspective. Routledge.
  • Yang, J. S. (2021). Dynamics of Firm’s Investment in Education and Training: An Agent-based Approach. Computational Economics, 1-35.
  • Yao, H., Jiang, Y., & Yang, R. (2021). Reconstruction Method of Landscape Planning Mode based on VR Technology and Wireless Communication Technology.
  • Yletyinen, J., Perry, G. L. W., Stahlmann-Brown, P., Pech, R., & Tylianakis, J. M. (2021). Multiple social network influences can generate unexpected environmental outcomes. Scientific Reports, 11(1), 1-14.
  • Yoon, S. A. (2021). Complex Systems Research in K12 Science Education: A Focus on What Works for Whom and under Which Conditions.
  • Yu, J. (2021). Evolving autonomous agents with simulated brains using L2L and Netlogo (No. FZJ-2022-00853). Jülich Supercomputing Center.
  • Yu, X., Nian, F., Yao, Y., & Luo, L. (2021). Phase Transition in Group Emotion. IEEE Transactions on Computational Social Systems.
  • Yu, Y., Yazan, D. M., Bhochhibhoya, S., & Volker, L. (2021). Towards Circular Economy Through Industrial Symbiosis in the Dutch Construction Industry: A Case of Recycled Concrete Aggregates. Journal of Cleaner Production, 126083.
  • Yuan, F. (2021). Smart city next-gen social networks system based on software reconstruction model and cognitive computing. Social Network Analysis and Mining, 11(1), 1-14.
  • Zagaria, C., Schulp, C. J., Zavalloni, M., Viaggi, D., & Verburg, P. H. (2021). Modelling transformational adaptation to climate change among crop farming systems in Romagna, Italy. Agricultural Systems, 188, 103024.
  • Zander, S. (2021). Wirkungsgefüge für einen systemischen Zugang zum mathematischen Modellieren nutzen. In Neue Materialien für einen realitätsbezogenen Mathematikunterricht 8 (pp. 119-132). Springer Spektrum, Wiesbaden.
  • Zanker, M., Bureš, V., & Tučník, P. (2021). Environment, Business, and Health Care Prevail: A Comprehensive, Systematic Review of System Dynamics Application Domains. Systems, 9(2), 28.
  • Zhang, G., Li, H., He, R., & Lu, P. (2021). Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions. Complex & Intelligent Systems, 1-19.
  • Zhang, J. (2021). Agent-Based Optimizing Match Between Passenger Demand and Service Supply for Urban Rail Transit Network With NetLogo. IEEE Access, 9, 32064-32080.
  • Zhang, J. (2021). Is competition sufficient to drive observed retail location and revenue patterns? An agent-based case study (Master's thesis, University of Waterloo).
  • Zhang, J., & Robinson, D. T. (2021). Replication of an agent-based model using the Replication Standard. Environmental Modelling & Software, 105016.
  • Zhang, T., Dong, P., Zeng, Y., & Ju, Y. (2022). Analyzing the diffusion of competitive smart wearable devices: An agent-based multi-dimensional relative agreement model. Journal of Business Research, 139, 90-105.
  • Zheng, C. (2021). Evolutionary Game Analysis of Knowledge Sharing in Low-Carbon Innovation Network. Complexity, 2021.
  • Zheng, G., Gong, B., & Zhang, Y. (2021). Dynamic Network Security Mechanism Based on Trust Management in Wireless Sensor Networks. Wireless Communications and Mobile Computing, 2021.
  • Zhong C. (2021). Adaptive Active Immune Policy for Sensor Nodes in the Internet of Things. Advances in Artificial Intelligence and Security Cham 2021. Springer International Publishing Pages: 514-525.
  • Zhou, Q. (2021). Detecting the public’s information behaviour preferences in multiple emergency events. Journal of Information Science, 01655515211027789.
  • Zhou, S., Fu, H., Tao, S., Han, Y., & Mao, M. (2021). Bridging the top-down and bottom-up approaches to smart urbanization? A reflection on Beijing’s Shuangjing International Sustainable Development Community Pilot. International Journal of Urban Sciences, 1-23.
  • Zhu, L., Ding, C., Zhu, T., & Wang, Y. (2021). A review on the forward osmosis applications and fouling control strategies for wastewater treatment. Frontiers of Chemical Science and Engineering, 1-20.
  • Zhu, Z., Yi, W., Ruifeng, F., Liya, L., & Meng, Q. Modeling on Anti-UAV System-of-systems Combat OODA Loop Based on NetLogo. Journal of System Simulation, 33(8), 1791.
  • Zia, K., Farooq, U., & Al Ajmi, S. (2021). Finding the Impact of Market Visibility and Monopoly on Wealth Distribution and Poverty Using Computational Economics. Computational Economics, 1-25.
  • Zia, K., Farooq, U., Shafi, M., & Ferscha, A. (2021). On the effectiveness of multi-feature evacuation systems: an agent-based exploratory simulation study. PeerJ Computer Science, 7, e531.
  • Zijlstra, T. W., de Vries, H., & Sterck, E. H. (2021). Emotional bookkeeping and differentiated affiliative relationships: Exploring the role of dynamics and speed in updating relationship quality in the EMO-model. PloS one, 16(4), e0249519.
  • Zohar, A. R., & Levy, S. T. (2021). From feeling forces to understanding forces: The impact of bodily engagement on learning in science. Journal of Research in Science Teaching, 1– 36.
  • Zolfagharipoor, M. A., & Ahmadi, A. (2021). Agent-based modeling of participants' behaviors in an inter-sectoral groundwater market. Journal of Environmental Management, 299, 113560.
  • Zong, X., Liu, A., Wang, C., Ye, Z., & Du, J. (2021, September). Indoor evacuation model based on visual-guidance artificial bee colony algorithm. In Building Simulation (pp. 1-14). Tsinghua University Press.
  • Zouhri, S., El Baroudi, M., & Saadi, S. (2021). Agent-Based Model for Proteins Interaction inside Cancer Cell. American Journal of Computational and Applied Mathematics, 11(2), 42-50.
  • Zouhri, S., El Baroudi, M., & Saadi, S. (2021). Agent-Based Model for Proteins Interaction inside Cancer Cell. American Journal of Computational and Applied Mathematics, 11(2), 42-50.
  • Zuccotti, C. V., Lorenz, J., Paolillo, R., Sánchez, A. R., & Serka, S. (2021). Exploring the dynamics of neighborhood ethnic segregation with agent-based modelling: an empirical application to Bradford.
  • Züfle, A., Wenk, C., Pfoser, D., Crooks, A., Kim, J. S., Kavak, H., ... & Jin, H. (2021). Urban life: a model of people and places. Computational and Mathematical Organization Theory, 1-32.
  • Zuo, Y., & Zhao, X. (2021). Effects of herding behavior of tradable green certificate market players on market efficiency: insights from heterogeneous agent model. Frontiers in Energy, 1-20.
  • Бурова, А. А., Буров, С. С., Парыгин, Д. С., Финогеев, А. Г., & Смирнова, Т. В. (2021). ПАНЕЛЬ АДМИНИСТРИРОВАНИЯ ПЛАТФОРМЫ МНОГОАГЕНТНОГО МОДЕЛИРОВАНИЯ С ВОЗМОЖНОСТЬЮ ПОСТРОЕНИЯ ГРАФИЧЕСКИХ ОТЧЕТОВ. International Journal of Open Information Technologies, 9(12), 4-14.
  • Волобуев, Н. А., Гайдамашко, И. В., Грошев, И. В., Логинов, Е. Л., Эриашвили, Н. Д., & Шкута, А. А. (2021). Информационно-цифровая детерминация учебного процесса в современных условиях информационного общества и глобальных коммуникаций. Образование. Наука. Научные кадры, (4), 245-249.
  • Ильинский, А. И. (2021). АГЕНТНОЕ МОДЕЛИРОВАНИЕ РАЗВИТИЯ СЛОЖНОЙ НАЛОГОВОЙ ЭКОСИСТЕМЫ В СЛУЧАЕ РАЗМЫВАНИЯ НАЛОГОВОЙ БАЗЫ ПРИ ВНЕДРЕНИИ SUPTECH И REGTECH. Хроноэкономика, (4 (32)), 60-63.
  • Петров, А. П. Ч., Ахременко, А. С., Жеглов, С. А., & Кручинская, Е. В. (2021). Is Network Structure Important for Protest Mobilization? Findings from Agent-Based Modeling. Мониторинг общественного мнения: экономические и социальные перемены, (6).
  • 张爽. (2021). 珠海市香洲城区洪涝灾害模拟及行车影响仿真 (Master's thesis, 河北工程大学).
  • 郑荣, 王晓宇, & 张艺源. (2021). 基于 ACP 理论的企业竞争情报智能系统构建研究. 情报理论与实践, 44(12), 148.
  • 陈榕, & 吴才琴. (2021). 新冠肺炎疫情防控措施效果仿真研究. 台州学院学报.

2020

  • Abdalbaki, S. M. (2020). A cellular automata modelling approach in household water use. Journal of Water, Sanitation and Hygiene for Development.
  • Abdulsattar, H., Siam, M. R. K., & Wang, H. (2020). Characterisation of the impacts of autonomous driving on highway capacity in a mixed traffic environment: an agent-based approach.
  • Abubakar, H., Yusuf, S., & Abdurrahman, Y. (2020). Discrete Artificial Dragonflies Algorithm in Agent Based Modelling for Exact Boolean kSatisfiability Problem. Journal of Advances in Mathematics and Computer Science, 115-134.
  • Accolla, C., Vaugeois, M., Grimm, V., Moore, A. P., Rueda‐Cediel, P., Schmolke, A., & Forbes, V. E. (2020). A review of key features and their implementation in unstructured, structured, and agent‐based population models for ecological risk assessment. Integrated Environmental Assessment and Management.
  • Adamatti, D. F. Circadian Rhythm and Pain: Mathematical Model Based on Multiagent Simulation. In Ambient Intelligence–Software and Applications: 11th International Symposium on Ambient Intelligence (Vol. 1, p. 309). Springer Nature.
  • Adamatti, D. F. Development of a Multiagent Simulator to Genetic Regulatory Networks. In Ambient Intelligence–Software and Applications: 11th International Symposium on Ambient Intelligence (p. 279). Springer Nature.
  • Aghaie, V., Alizadeh, H., & Afshar, A. (2020). Emergence of social norms in the cap-and-trade policy: An agent-based groundwater market. Journal of Hydrology, 125057.
  • Ahmed, N., Alo, R., Amelink, C., Baek, Y. Y., Chudhary, A., Collins, K., ... & Kenyon, R. (2020). net. science: A Cyberinfrastructure for Sustained Innovation in Network Science and Engineering.
  • Akwafuo, S. E., Abah, T., & Oppong, J. R. (2020). Evaluation of the Burden and Intervention Strat-effigies of TB-HIV Co-Infection in West Africa. J Infect Dis Epidemiol, 6, 143.
  • Al Barghuthi, N. B., & Togher, M. (2020, March). Analysis of Frameworks for Traffic Agent Simulations. In International Symposium on Intelligent Computing Systems (pp. 44-54). Springer, Cham.
  • Al Shamsi, A. A. (2020). School Auditorium Evacuation Simulation. International Journal of Information Technology and Language Studies, 4(2).
  • Al-Khulaidy, A., & Swartz, M. (2020, May). Along the border: an agent-based model of migration along the United States-Mexico border. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-12).
  • Al-Madhlom, Q., Al-Ansari, N., Hamza, B. A., Laue, J., & Hussain, H. M. (2020). Seepage Velocity: Large Scale Mapping and the Evaluation of Two Different Aquifer Conditions (Silty Clayey and Sandy). Hydrology, 7(3), 60.
  • Alaghband, M., & Garibay, I. (2020). Effects of Non-Cognitive Factors on Post-Secondary Persistence of Deaf Students: An Agent-Based Modeling Approach. arXiv preprint arXiv:2006.12624.
  • Aljarah, R., & Mahmood, B. (2020, February). Towards the Impact of Mobility Patterns on Network Resources in Smart Cities. In 2020 6th International Engineering Conference “Sustainable Technology and Development"(IEC) (pp. 126-130). IEEE.
  • Aljumah, A., Kaur, A., Bhatia, M., & Ahamed Ahanger, T. (2020). Internet of things‐fog computing‐based framework for smart disaster management. Transactions on Emerging Telecommunications Technologies, e4078.
  • Alm, J., Gerbrands, P., & Kirchler, E. (2020). Using "responsive regulation" to reduce tax base erosion. Regulation & Governance.
  • Alonso Vicario, S., Mazzoleni, M., Bhamidipati, S., Gharesifard, M., Ridolfi, E., Pandolfo, C., & Alfonso, L. (2020). Unravelling the influence of human behaviour on reducing casualties during flood evacuation. Hydrological Sciences Journal.
  • Alsassa, S., Lefèvre, T., Laugier, V., Stindel, E., & Ansart, S. (2020). Modeling Early Stages of Bone and Joint Infections Dynamics in Humans: A Multi-Agent, Multi-System Based Model. Frontiers in molecular biosciences, 7, 26.
  • Alvarado, M., & Arroyo, R. (2020). Cancer Metastasis and the Immune System Response: CM-IS Modeling by Ising Model. Research in Computing Science, 149(5), 123-129.
  • Amadei, B. (2020). Agent-Based and System Dynamics Modeling of Water Field Services. Challenges, 11(2), 13.
  • Amir, S., Asif, F. M., & Roci, M. (2020). Towards Circular Economy: Enhanced Decision-Making in Circular Manufacturing Systems. In Sustainable Consumption and Production, Volume II (pp. 257-279). Palgrave Macmillan, Cham.
  • An, S., Bates, R., Hammock, J., Rugaber, S., Weigel, E., & Goel, A. (2020). Scientific Modeling Using Large Scale Knowledge. In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., & Millán, E. (eds), Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12164. Springer, Cham.
  • Anderson, E., & Wendel, D. (2020). Learning Science Through Coding: An Investigation Into the Design of a Domain Specific Modeling Experience. The Interdisciplinarity of the Learning Sciences.
  • Anderson, S., & Anderson, S. D. (2020, June). Coding and Music Creation in a Multi-Agent Environment. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 527-528).
  • Anderson, T., Leung, A., Dragicevic, S., & Perez, L. (2020). Modeling the geospatial dynamics of residential segregation in three Canadian cities: An agent‐based approach. Transactions in GIS.
  • Anderson, T., Leung, A., Perez, L., & Dragićević, S. (2020). Investigating the Effects of Panethnicity in Geospatial Models of Segregation. Applied Spatial Analysis and Policy, 1-23.
  • Angenendt, M., Bäcker, A., Merten, H., Müller, M. M., Zons, G., Sokolov, E., ... & Symposion, P. The impact of the opposition in established democracies.
  • Angourakis, A., Bates, J., Baudouin, J. P., Giesche, A., Ustunkaya, M. C., Wright, N., ... & Petrie, C. A. (2020). How to ‘downsize’a complex society: an agent-based modelling approach to assess the resilience of Indus Civilisation settlements to past climate change. Environmental Research Letters.
  • Aniruddha, B., & Abi Tamim, V. (2020). Modelling the challenges of managing free-ranging dog populations. Scientific Reports (Nature Publisher Group), 10(1).
  • Anne, L., Anandakumar, S., Mahendran, A., Ghalib, M. R., & Ghosh, U. A. Study and Analysis of Trust Management System in Cloud Technologies. In Applications of Artificial Intelligence for Smart Technology (pp. 220-232). IGI Global.
  • Antczak, T., Weron, R., & Zabawa, J. (2020). Data-driven simulation modeling of the checkout process in supermarkets: Insights for decision support in retail operations (No. WORMS/20/16). Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
  • Aqib, M., & Ukil, A. (2020, November). Modelling of electric vehicle charging and discharging profile to mimic real life scenario at charging stations. In 2020 IEEE REGION 10 CONFERENCE (TENCON) (pp. 501-505). IEEE.
  • Aragón, A., Gaither, M. J., & Madden, M. (2020). MIXED GEOSPATIAL METHODS BASELINE STUDY TO EVALUATE AND MODEL GENTRIFICATION ALONG THE WESTSIDE ATLANTA BELTLINE, USA. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 527-531.
  • Araujo-Granda, P., Gras, A., Ginovart, M., & Moulton, V. (2020). INDISIM-Denitrification, an individual-based model for study the denitrification process. Journal of industrial microbiology & biotechnology, 47(1), 1-20.
  • Araújo, E., & Gerritsen, C. (2020). Creating a temporal pattern for street robberies using ABM and data from a small city in South East Brazil. Agent-Based Modelling for Criminological Theory Testing and Development, 146.
  • Araújo, M. S., da Silva, T. F., Sampaio, V. A., Melo, G. F., Junior, R. J. F., da Costa, L. F., ... & de Campos, G. A. (2020, October). Cooperative Observation of Smart Target Agents. In Brazilian Conference on Intelligent Systems (pp. 77-92). Springer, Cham.
  • Arden, R., Ruseno, N., & Hidayat, Y. A. (2020). CARGO OPTIMIZATION IN AN AIRLINE USING AGENT–BASED MODELLING.
  • Arnould-Pétré, M., Guillaumot, C., Danis, B., Féral, J. P., & Saucède, T. Individual-based model of population dynamics in a sea urchin of the Kerguelen Plateau (Southern Ocean), Abatus cordatus, under changing environmental conditions. Ecological Modelling, 440, 109352.
  • Asgharpourmasouleh, A., Fattahzadeh, M., Mayerhoffer, D., & Lorenz, J. (2020). On the Fate of Protests: Dynamics of Social Activation and Topic Selection Online and in the Streets. In Computational Conflict Research (pp. 141-164). Springer, Cham.
  • Ashiku, L., & Dagli, C. (2020). Agent Based Cybersecurity Model for Business Entity Risk Assessment. In 2020 IEEE International Symposium on Systems Engineering (ISSE) (pp. 1-6). IEEE.
  • Aslan, U., LaGrassa, N., Horn, M., & Wilensky, U. (2020). Putting the Taxonomy into Practice: Investigating Students’ Learning of Chemistry with Integrated Computational Thinking Activities. Paper presented at the American Education Research Association (AERA) Conference. San Francisco, CA.
  • Assa, J., & Lengfelder, C. (2020). Can Enhancing Capabilities Promote Energy Justice? An Agent-Based Model Approach. Mendeley Data, 1.
  • Augustijn, E. W., Abdulkareem, S. A., Sadiq, M. H., & Albabawat, A. A. (2020, April). Machine Learning to Derive Complex Behaviour in Agent-Based Modellzing. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 284-289). IEEE.
  • Azim, M. A., Sathasivam, S., Alzaeemi, S. A. S., & Mahmood, M. (2019). Agent Based Modeling for Comparing the Performances of Hyperbolic and Zeng and Martinez Activations Functions. International Journal of Computer Networks and Communications Security, 7(12), 250-257.
  • Azucena, J., Alkhaleel, B., Liao, H., & Nachtmann, H. (2020). Hybrid simulation to support interdependence modeling of a multimodal transportation network. Simulation Modelling Practice and Theory, 102237.
  • Baccega, D. (2020). An agent-based model to support SARS-CoV-2 infection control strategies at school.
  • Badham, J., Kee, F., & Hunter, R. F. (2020). Network structure influence on simulated network interventions for behaviour change. Social Networks, 64, 55-62.
  • Baena, B., Cobian, C., Larios, V. M., Orizaga, J. A., Maciel, R., Cisneros, M. P., & Beltran-Ramirez, J. R. (2020). Adapting food supply chains in Smart Cities to address the impacts of COVID19 a case study from Guadalajara metropolitan area. In 2020 IEEE International Smart Cities Conference (ISC2) (pp. 1-8). IEEE.
  • Bai, S. (2020). Simulations of COVID-19 spread by spatial agent-based model and ordinary differential equations. International Journal of Simulation and Process Modelling, 15(3), 268-277.
  • Bain, C., & Wilensky, U. (2020, February). Vectors of CT-ification: Integrating Computational Activities in STEM Classrooms. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 1361-1361).
  • Bain, C., Anton, G., Horn, M., Wilensky, U. (2020). Back to Computational Transparency: Co-design with Teachers to Integrate Computational Thinking in Science Classrooms. Proceedings of the International Conference for the Learning Sciences (ICLS 2020), Nashville, USA: ISLS.
  • BAIN, C., DABHOLKAR, S., & WILENSKY, U. (2020). Confronting Frame Alignment in CT Infused STEM Classrooms. CoolThink@ JC, 91.
  • Baker, E., Barbillon, P., Fadikar, A., Gramacy, R. B., Herbei, R., Higdon, D., ... & Sacks, J. (2020). Stochastic Simulators: An Overview with Opportunities. arXiv preprint arXiv:2002.01321.
  • Bano, A., Ud Din, I., & Al-Huqail, A. A. (2020). AIoT-Based Smart Bin for Real-Time Monitoring and Management of Solid Waste. Scientific Programming, 2020.
  • Bao, H., Dong, H., Jia, J., Peng, Y., & Li, Q. (2020). Impacts of land expropriation on the entrepreneurial decision-making behavior of land-lost peasants: An agent-based simulation. Habitat International, 95, 102096.
  • Barabashev, A. G. (2020). Constructive ending: how to finalize the conclusion and discussion of a research project and a journal article. Handbook of Research Methods in Public Administration, Management and Policy, 395.
  • Baral, N., Gunaratne, C., Jayalath, C., Rand, W., Senevirathna, C., & Garibay, I. (2020). Negative Influence Gradients Lead to Lowered Response Capacity on Social Networks.
  • Barazza, E., & Strachan, N. (2020). The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors. Energy Research & Social Science, 65, 101458.
  • Barazza, E., & Strachan, N. The key role of historic path-dependency and competitor imitation on the electricity sector low-carbon transition. Energy Strategy Reviews, 33, 100588.
  • Barbet, V., Bourlès, R., & Rouchier, J. (2020). Informal risk-sharing cooperatives: the effect of learning and other-regarding preferences. Journal of Evolutionary Economics, 1-28.
  • Barbosa, P., Schumaker, N. H., Brandon, K. R., Bager, A., & Grilo, C. (2020). Simulating the consequences of roads for wildlife population dynamics. Landscape and Urban Planning, 193, 103672.
  • Barker, A. K., Scaria, E., Alagoz, O., Sethi, A. K., & Safdar, N. (2020). Reducing C. difficile in children: An agent-based modeling approach to evaluate intervention effectiveness. Infection Control & Hospital Epidemiology, 1-9.
  • Barthelemy, J., Amirghasemi, M., Arshad, B., Fay, C., Forehead, H., Hutchison, N., ... & Perez, P. (2020). Problem-Driven and Technology-Enabled Solutions for Safer Communities: The case of stormwater management in the Illawarra-Shoalhaven region (NSW, Australia). Handbook of Smart Cities, 1-28.
  • Bartlett, T. (2020). Privacy and Security Management Practices of Emerging Technologies: Internet of Things (Doctoral dissertation, Robert Morris University).
  • Bassi, F., Bauermann, T., Lang, D., & Setterfield, M. (2020). Is capacity utilization variable in the long run? An agent-based sectoral approach tomodeling hysteresis in the normal rate of capacity utilization.
  • Basu, D., & Panorkou, N. (2020, January). Utilizing mathematics to examine sea level rise as an environmental and a social issue. In Mathematics Education Across Cultures: Proceedings of the 42nd Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education.
  • Bautista-Torres, A., Bautista-Aleman, E. A., & Espitia-Cuchango, H. E. (2020). Cellular Automata Implemented on FPGA Based on Totalistic Rules for Deterministic Systems.
  • Beckedorf, J., Hartung, D., & Sittig, P. (2020). 15. Analyzing high volumes of German court decisions in an interdisciplinary class of law and computer science students. Computational Legal Studies: The Promise and Challenge of Data-Driven Research, 328.
  • Bedar, R. A. H., & Al-Shboul, M. (2020). The Effect of Using STEAM Approach on Developing Computational Thinking Skills among High School Students in Jordan. International Journal of Interactive Mobile Technologies, 14(14).
  • Belgrad, B. A., & Griffen, B. D. (2020). Which mechanisms are responsible for population patterns across different quality habitats? A new approach. Oikos.
  • Bell, A. (2020). Two Approaches to Teaching with NetLogo: Examining the Role of Structure and Agency.
  • Belsare, A. V., & Stewart, C. M. (2020). OvCWD: An agent‐based modeling framework for informing chronic wasting disease management in white‐tailed deer populations. Ecological Solutions and Evidence, 1(1).
  • Belsare, A. V., Gompper, M. E., Keller, B., Sumners, J., Hansen, L., & Millspaugh, J. J. (2020). An agent-based framework for improving wildlife disease surveillance: A case study of chronic wasting disease in Missouri white-tailed deer. Ecological Modelling, 417, 108919.
  • Belsare, A., Gompper, M., Keller, B., Sumners, J., Hansen, L., & Millspaugh, J. (2020). Size Matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling framework. MethodsX, 100953.
  • Berger, C., & Mahdavi, A. (2020). Review of current trends in agent-based modeling of building occupants for energy and indoor-environmental performance analysis. Building and Environment, 173, 106726.
  • Bezzaoucha, F. S., Sahnoun, M. H., & Benslimane, S. M. (2020). Multi-component modeling and classification for failure propagation of an offshore wind turbine. International Journal of Energy Sector Management.
  • Biagetti, A., Ferrando, A., & Mascardi, V. (2020, October). The DigForSim Agent Based Simulator of People Movements in Crime Scenes. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 42-54). Springer, Cham.
  • Bidoki, N. H., Schiappa, M., Sukthankar, G., & Garibay, I. (2020). Modeling social coding dynamics with sampled historical data. Online Social Networks and Media, 16.
  • Bielik, T., Fonio, E., Feinerman, O., Duncan, R. G., & Levy, S. T. (2020). Working Together: Integrating Computational Modeling Approaches to Investigate Complex Phenomena. Journal of Science Education and Technology, 1-18.
  • Biermann, L. (2020). Assessing the impacts of human disturbance on wildlife: insights from wildfowl on the Exe Estuary (Doctoral dissertation, Bournemouth University).
  • Bilsborrow, R. E. (2020). Economic and Related Aspects of Land Use on Islands: A Meta Perspective. In Land Cover and Land Use Change on Islands (pp. 11-62). Springer, Cham.
  • Bina, K., & Moghadas, N. (2020). BIM-ABM simulation for emergency evacuation from conference hall, considering gender segregation and architectural design. Architectural Engineering and Design Management, 1-15.
  • Binhomaid, O., & Hegazy, T. (2020). Agent-based Simulation of Workers’ Behaviors, Productivity, and Safety around Construction Obstacles. Canadian Journal of Civil Engineering, (ja).
  • Bodine, E. N., Panoff, R. M., Voit, E. O., & Weisstein, A. E. (2020). Agent-Based Modeling and Simulation in Mathematics and Biology Education. Bulletin of Mathematical Biology, 82(8), 1-19.
  • Bologov, A. (2020). Assessement of the Ability of Elective Choice System. Review of Business and Economics Studies, (3).
  • Bommel, P. (2020). Participatory modelling and interactive simulation to support the management of the commons (Doctoral dissertation, Université de Montpellier).
  • Borer, B. (2020). A marriage made in soil-quantifying bacterial life in soil hotspots using individual-based and metabolic network modeling (Doctoral dissertation, ETH Zurich).
  • Bosse, S. (2020, September). Self-organising Urban Traffic Control on Micro-level Using Reinforcement Learning and Agent-Based Modelling. In Proceedings of SAI Intelligent Systems Conference (pp. 745-764). Springer, Cham.
  • Boukehila, A., & Taleb, N. (2020, February). Statistical Study To Detect Emergent Behaviours. In 2020 2nd International Conference on Mathematics and Information Technology (ICMIT) (pp. 164-168). IEEE.
  • Boyd, R., Walker, N., Hyder, K., Thorpe, R., Roy, S., & Sibly, R. (2020). SEASIM-NEAM: a Spatially-Explicit Agent-based SIMulator of NorthEast Atlantic Mackerel population dynamics. MethodsX, 101044.
  • Brady, C., Gresalfi, M., Steinberg, S., & Knowe, M. (2020). Debugging for Art’s Sake: Beginning Programmers’ Debugging Activity in an Expressive Coding Context. The Interdisciplinarity of the Learning Sciences.
  • Brady, C., Stroup, W. M., Petrosino, A. & Wilensky, U. J. (2020) Amplifying the Restructuration Potential of Agent-Based Modeling Through Group-Based Activity Structures [Symposium]. AERA Annual Meeting San Francisco, CA http://tinyurl.com/szuaxl3 (Conference Canceled)
  • Brahmbhatt, M., & Sonar, S. (2020). Transit Time Comparison of Different Modes of Transportation. Studies in Indian Place Names, 40(9), 88-93.
  • Brainard, J., Hunter, P. R., & Hall, I. R. (2020). An agent-based model about the effects of fake news on a norovirus outbreak. Revue d'Épidémiologie et de Santé Publique.
  • Braun, B., Taraktaş, B., Beckage, B., & Molofsky, J. (2020). Phase transitions and social distancing control measures for SARS-CoV-2 on small world networks. arXiv preprint arXiv:2005.09751.
  • Braun, B., Taraktaş, B., Beckage, B., & Molofsky, J. (2020). Simulating phase transitions and control measures for network epidemics caused by infections with presymptomatic, asymptomatic, and symptomatic stages. PLOS ONE, 15(9), e0238412.
  • Brearcliffe, D. (2020). Non-Pharmaceutical Herd Immunity using Homemade Masks (No. 4432). EasyChair.
  • Brearcliffe, D., & Crooks, A. (2020). Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning (No. 4403). EasyChair.
  • Breen, C. D., & Frezza, S. (2020). Charismatic Leadership and the Formation of Hate Groups. International Annals of Criminology, 1-36.
  • Brignone, S., Grimaldi, R., Denicolai, L., & Palmieri, S. (2020). Intelligenza artificiale, robot e rappresentazione della conoscenza. Il Laboratorio di simulazione del comportamento e robotica educativa" Luciano Gallino".
  • Brodskiy, V. A., Pimenov, D. M., Chernov, P. L., Dzhamaldinova, M. D., & Kurdyukova, N. O. (2020). A Review of Agent-Based Modeling in the Cooperative Sector of Economics. Frontier Information Technology and Systems Research in Cooperative Economics, 261-268.
  • Broniec, W., An, S., Rugaber, S., & Goel, A. K. (2020). Using VERA to explain the impact of social distancing on the spread of COVID-19. arXiv preprint arXiv:2003.13762.
  • Brughmans, T. (2020). Evaluating the Potential of Computational Modelling for Informing Debates on Roman Economic Integration. In Complexity Economics (pp. 105-123). Palgrave Macmillan, Cham.
  • Buechley, L. Self-Directed Constructionist Communities: Interview with Leah Buechley. In Holbert, N., Berland, M., & Kafai, Y. B. (eds.), Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs, 381.
  • Buenaventura, A., Calgo, C. J., Bardeloza, D. K. D., Libatique, N. J. C., & Tangonan, G. L. (2020). Agent-based modeling of the spread of fire in urban settlements in the Philippines. Proceedings of the Samahang Pisika ng Pilipinas.
  • Buhat, C. A. H., Lutero, D. S., Olave, Y. H., Torres, M. C., & Rabajante, J. F. (2020). Modeling the Transmission of Respiratory Infectious Diseases in Mass Transportation Systems. medRxiv.
  • Buhat, C. A. H., Rabajante, J. F., & Paller, V. G. V. (2020). Spatiotemporal modeling of parasite aggregation among fish hosts in a lentic ecosystem. Modeling Earth Systems and Environment, 1-17.
  • Buhat, C. A., & Villanueva, S. K. (2020). Determining the effectiveness of practicing non-pharmaceutical interventions in improving virus control in a pandemic using agent-based modelling. Mathematics in Applied Sciences and Engineering, 1(4), 423-438.
  • Bulson, L., Becher, M. A., McKinley, T. J., & Wilfert, L. (2020). Long‐term effects of antibiotic treatments on honeybee colony fitness–a modelling approach. Journal of Applied Ecology.
  • Burbach, L., Belavadi, P., Halbach, P., Kojan, L., Plettenberg, N., Nakayama, J., ... & Valdez, A. C. (2020, July). Netlogo vs. Julia: Evaluating Different Options for the Simulation of Opinion Dynamics. In International Conference on Human-Computer Interaction (pp. 3-19). Springer, Cham.
  • Cabrera-Becerril, A., Peralta, R., Miramontes, P., Vargas-de-Leon, C., & Alonso, R. (2020). Increase of non-vaccine human papillomavirustypes in a group of HPV-vaccinated Mexicanwomen. Evidence of Pathogenic StrainReplacement. medRxiv.
  • Cabrera, M. A. S., & Barrientos, A. H. (2020). Feasibility Study for Using Energy-Harvesting Floor in Urban Public Transportation System: Case of Subway Stations. Journal of Electrical Power & Energy Systems, 4(1), 11-21.
  • Caetano-Anollés, G., Mughal, F., Aziz, M. F., Koç, I., Caetano-Anollés, K., Caetano-Anollés, D., & Mittenthal, J. E. (2020). Linkage: A “double tale” of module creation in evolving networks. Untangling Molecular Biodiversity, pp. 91-168. https://doi.org/10.1142/9789814656627_0003
  • Calabrò, G., Inturri, G., Le Pira, M., Pluchino, A., & Ignaccolo, M. (2020). Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization. Transportation Research Procedia, 45, 234-241.
  • Calabrò, G., Torrisi, V., Inturri, G., & Ignaccolo, M. (2020). Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization. European Transport Research Review, 12, 1-11.
  • Calcagno, S. F., Mailleret, L., Malausa, T., & Vercken, E. (2020). Shifts from pulled to pushed range expansions.
  • Camargo, P., Mattos, S., & Goldenberg, C. (2020, February). Complexity and Collective Intelligence on Demand for a Sustainable Future. In 2020 IEEE 14th International Conference on Semantic Computing (ICSC) (pp. 347-349). IEEE.
  • Camp, J., Nelson, K., Philip, C. E., Moravec, M., Scheffler, D. W., & Johnson, P. (2020). Utilizing Agent-Based Modeling to Evaluate Operational Impacts of an Incident and Possible Alternatives on US Waterways. Transportation Research Record, 0361198120941504.
  • Camparotti, C. E. S. (2020). Analysis of industrial symbiosis through agent-based simulation: application in the agro-industrial sector.
  • Campos, R. F. D. A., Cunha, D. A. D., & Bueno, N. P. (2020). Information dissemination in socio-ecological systems: Analysis of a hybrid model of System Dynamics and Agent-Based Modeling. Nova Economia, 30(1), 257-286.
  • Caprioli, C., Bottero, M., & De Angelis, E. (2020). Supporting Policy Design for the Diffusion of Cleaner Technologies: A Spatial Empirical Agent-Based Model. ISPRS International Journal of Geo-Information, 9(10), 581.
  • Cárdenas González, L., & Soto Lozano, C. (2020). Modelación de un sistema inteligente de tráfico vehicular por medio de una simulación basada en agentes.
  • Cardinot, M. (2020). Coevolutionary spatial game theory: The impact of abstention. Small, 4(4), 1-7.
  • Carney, M., & Davies, B. (2020). Agent-Based Modeling, Scientific Reproducibility, and Taphonomy: A Successful Model Implementation Case Study.
  • Carpente, M. S., Guijarro-Berdiñas, B., Alonso-Betanzos, A., Rodríguez-Arias, A., & Dimitru, A. (2020). An Agent-Based Model to Simulate the Spread of a Virus Based on Social Behavior and Containment Measures. In Multidisciplinary Digital Publishing Institute Proceedings (Vol. 54, No. 1, p. 22).
  • Carr-Markell, M. (2020). Effects of native prairie forbs on the foraging choices and recruitment behavior of honey bees (Apis mellifera).
  • Carrella, E., Bailey, R., & Madsen, J. (2020). Calibrating agent-based models with linear regressions. Journal Of Artificial Societies and Social Simulation, 23(1).
  • Ceballos, Y. F., Galarcio-Noguera, J. D., Maya-Duque, P. A., & Ramirez-Cordoba, G. L. (2020). Agent-based Model for Environmental Awareness and Extended Producer Responsibility in Developing Countries. Scientia et Technica, 25(3), 430-437.
  • Centorrino, P., Corbetta, A., Cristiani, E., & Onofri, E. (2020). Managing Crowded Museums: Visitors Flow Measurement, Analysis, Modeling, and Optimization. arXiv preprint arXiv:2006.16830.
  • Chakarov, A. G. (2020). Integrating Computational Thinking into Middle School Science Curriculum Using Programmable Sensor Technologies (Doctoral dissertation, University of Colorado at Boulder).
  • Challenge, S., Jansens, R., Kingston, M., Landess, M., Morrison, B., Dubey, M., & Guerin, S. (2020). It’s ‘Bout To Get Lit Up In Here.
  • Chappin, É. J., Nikolic, I., & Yorke-Smith, N. (2020). Agent-based modelling of the social dynamics of energy end use. In Energy and Behaviour (pp. 321-351). Academic Press.
  • Chattoe-Brown, E. (2020). Why questions like ‘do networks matter?’matter to methodology: how Agent-Based Modelling makes it possible to answer them. International Journal of Social Research Methodology, 1-14.
  • Chen, B., Chen, H., Ning, D., Zhu, M., Ai, C., Qiu, X., & Dai, W. (2020). A Two-Tier Partition Algorithm for the Optimization of the Large-scale Simulation of Information Diffusion in Social Networks. Symmetry, 12(5), 843.
  • Chen, H. C., Han, Q., & De Vries, B. (2020). Modeling the spatial relation between urban morphology, land surface temperature and urban energy demand. Sustainable Cities and Society, 102246.
  • Chen, K., Li, Y., & Linderman, K. (2020). Supply Network Resilience Learning: An Exploratory Data Analytics Study.
  • Chen, S., He, Q., & Xiao, H. (2020). A study on cross-border e-commerce partner selection in B2B mode. Electronic Commerce Research, 1-21.
  • Chen, S., Wu, J., Pan, Y., Ge, J., & Huang, Z. (2020). Simulation and case study on residential stochastic energy use behaviors based on human dynamics. Energy and Buildings, 110182.
  • Chen, S., Zhang, H., Guan, J., & Rao, Z. (2020, March). Agent-based modeling and simulation of stochastic heat pump usage behavior in residential communities. In Building Simulation (pp. 1-19). Tsinghua University Press
  • Chen, Y., Chen, F., Lin, Z., & Pan, X. (2020). Comprehensive Evaluation of Underground Garage Traffic Design Scheme Based on Data Envelopment Analysis. In CICTP 2020 (pp. 4077-4088).
  • Cheng, C., Luo, Y., & Yu, C. (2020). Dynamic mechanism of social bots interfering with public opinion in network. Physica A: Statistical Mechanics and its Applications, 124163.
  • Chennoufi, M., & Bendella, F. (2020). Fuzzy controller and emotional model for evacuation of virtual crowd behaviors. Intelligent Decision Technologies, 14(2), 199-214.
  • Chennoufi, M., Bendella, F., & Bouzid, M. (2020). Best A* discovery for multi agents planning. International Journal of Operational Research, 38(3), 343-363.
  • CHIRIȚĂ, N., & NICA, I. (2020). Analysis of the impact generated by COVID-19 in banking institutions and possible economic effects. Theoretical and Applied Economics, 22(3 (624), Autumn), 21-40.
  • Christensen, C., & Salmon, J. (2020). An agent-based modeling approach for simulating the impact of small unmanned aircraft systems on future battlefields. The Journal of Defense Modeling and Simulation, 1548512920963904.
  • Christensen, D., & Lombardi, D. (2020). Understanding Biological Evolution Through Computational Thinking. Science & Education, 1-43.
  • Chudzinska, M., Dupont, Y. L., Nabe-Nielsen, J., Maia, K. P., Henriksen, M. V., Rasmussen, C., ... & Trøjelsgaard, K. (2020). Combining the strengths of agent-based modelling and network statistics to understand animal movement and interactions with resources: example from within-patch foraging decisions of bumblebees. Ecological Modelling, 430, 109119.
  • Chudzinska, M., Nabe-Nielsen, J., Smout, S., Aarts, G., Brasseur, S., Graham, I., ... & McConnell, B. AgentSeal: Agent-based model describing movement of marine central-place foragers. Ecological Modelling, 440, 109397.
  • Collard, P. (2020). Second-order micromotives and macrobehaviour. Journal of Computational Social Science, 1-21.
  • Colombi, A., Scianna, M., & Preziosi, L. (2020). Collective migration and patterning during early development of zebrafish posterior lateral line. Philosophical Transactions of the Royal Society B, 375(1807), 20190385.
  • Conti, E., Di Mauro, L. S., Pluchino, A., & Mulder, C. (2020). Testing for top‐down cascading effects in a biomass‐driven ecological network of soil invertebrates. Ecology and Evolution.
  • Cooksey, R. W. (2020). Specialised Statistical Procedures. In Illustrating Statistical Procedures: Finding Meaning in Quantitative Data (pp. 557-693). Springer, Singapore.
  • Cooper, G. S., Willcock, S., & Dearing, J. A. (2020). Regime shifts occur disproportionately faster in larger ecosystems. Nature Communications, 11(1), 1-10.
  • Costa, L., Araújo, M., Silva, T., Junior, R., Andrade, J., & Campos, G. (2020, January). Comparative Study of Neural Networks Techniques in the Context of Cooperative Observations. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional (pp. 563-574). SBC.
  • Cotfas, L. A., Delcea, C., Milne, R. J., & Salari, M. (2020). Evaluating Classical Airplane Boarding Methods Considering COVID-19 Flying Restrictions.
  • Craze, G. J. (2020). Inflammation-Associated Mood Deterioration and the Degradation of Affective Climate: An Agent-Based Model (Doctoral dissertation, Case Western Reserve University).
  • Crespi, C., Fargetta, G., Pavone, M., Scollo, R. A., & Scrimali, L. (2020, November). A Game Theory Approach for Crowd Evacuation Modelling. In International Conference on Bioinspired Methods and Their Applications (pp. 228-239). Springer, Cham.
  • Cuevas, E. (2020). An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in Biology and Medicine, 103827.
  • Cuevas, E., Gálvez, J., Avila, K., Toski, M., & Rafe, V. (2020). A new metaheuristic approach based on agent systems principles. Journal of Computational Science, 101244.
  • Cui, L., He, T., Jiang, Y., Li, M., Wang, O., Jiajue, R., ... & Xia, W. (2020). Predicting the intervention threshold for initiating osteoporosis treatment among postmenopausal women in China: a cost-effectiveness analysis based on real-world data. Osteoporosis International, 31(2), 307-316.
  • Cunha, M. E. S., Rossetti, R. J., & Campos, P. (2020). Modelling Smart Cities Through Socio-Technical Systems. In 2020 IEEE International Smart Cities Conference (ISC2) (pp. 1-8). IEEE.
  • D'Auria, M., Scott, E. O., Lather, R. S., Hilty, J., & Luke, S. (2020). Assisted Parameter and Behavior Calibration in Agent-based Models with Distributed Optimization.
  • D'Orazio, M., Bernardini, G., & Quagliarini, E. (2020). A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria.
  • D'Orazio, M., Bernardini, G., & Quagliarini, E. (2020). How to restart? An agent-based simulation model towards the definition of strategies for COVID-19" second phase" in public buildings. arXiv preprint arXiv:2004.12927.
  • Dabholkar, S., & Wilensky, U. (2020). DESIGNING COMPUTATIONAL MODELS AS EMERGENT SYSTEMS MICROWORLDS TO SUPPORT LEARNING OF SCIENTIFIC INQUIRY.
  • Dabholkar, S., Peel, A, Anton, G., Horn, M. & Wilensky, U. (2020). Analysis of teachers’ involvement in co-design and implementation of CT (Computational Thinking) integrated biology units. Paper accepted at the American Education Research Association (AERA) Conference, San Francisco, CA, USA.
  • Daems, D. (2020). A Review and Roadmap of Online Learning Platforms and Tutorials in Digital Archaeology. Advances in Archaeological Practice, 8(1), 87-92.
  • Daghriri, T., & Ozmen, O. (2020). Quantifying the Effects of Social Distancing on the Spread of COVID-19. Available at SSRN 3696983.
  • Dahirel, M., Bertin, A., Haond, M., Blin, A., Lombaert, E., Calcagno, V., ... & Vercken, E. (2020). Shifts from pulled to pushed range expansions caused by reductions in connectedness. bioRxiv.
  • Dalle Nogare, D., & Chitnis, A. B. (2020, April). NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium. In Seminars in cell & developmental biology (Vol. 100, pp. 186-198). Academic Press.
  • Davidson, A. L., Khaddage, F., & Ogata, H. (2020). Thematic Working Group. Learners and learning contexts: New alignments for the digital age, 18.
  • Davis, N., Polhill, J. G., & Aitkenhead, M. J. Measuring heterogeneity in soil networks: a network analysis and simulation-based approach. Ecological Modelling, 439, 109308.
  • de Boer, A., Krul, L., Fehr, M., Geurts, L., Kramer, N., Urbieta, M. T., ... & Hepburn, P. A. (2020). Animal-free strategies in food safety & nutrition: What are we waiting for? Part I: Food safety. Trends in Food Science & Technology.
  • De Kock, P., & Carta, S. (2020). Trojans of ambiguity vs resilient regeneration: visual meaning in cities. Construction Economics and Building.
  • De Masi, G., & Ferrante, E. (2020). Quality-dependent adaptation in a swarm of drones for environmental monitoring. In 2020 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1-6). IEEE.
  • de Mingo López, L. F., Blas, N. G., Castellanos Peñuela, A. L., & Castellanos Peñuela, J. B. (2020). Swarm Intelligence Models: Ant Colony Systems Applied to BNF Grammars Rule Derivation. International Journal of Foundations of Computer Science, 31(01), 103-116.
  • de Oca, E. M., Suppi, R., De Giusti, L., & Naiouf, M. (2020). Green High Performance Simulation for AMB models of Aedes aegypti/Simulacion Green de Alto Rendimiento de un Modelo Basado en Agentes del Mosquito Aedes aegypti. Journal of Computer Science & Technology, 20(1), 15-23.
  • de Oliveira Zamberlan, A., Bordini, R. H., Kurtz, G. C., & Fagan, S. B. (2020). Multi-Agent Systems, Simulation and Nanotechnology. In Multi Agent Systems-Strategies and Applications. IntechOpen.
  • Deboscker, S., Séverac, F., Gaudart, J., Ménard, C., Meyer, N., & Lavigne, T. (2020). An agent-based model to simulate the transmission of vancomycin-resistant enterococci according different prevention and control measures. Infection Control & Hospital Epidemiology, 1-7.
  • Delcea, C., Cotfas, L. A., Bradea, I. A., Boloș, M. I., & Ferruzzi, G. (2020). Investigating the Exits’ Symmetry Impact on the Evacuation Process of Classrooms and Lecture Halls: An Agent-Based Modeling Approach. Symmetry, 12(4), 627.
  • Delcea, C., Cotfas, L. A., Craciun, L., & Molanescu, A. G. (2020). An agent-based modeling approach to collaborative classrooms evacuation process. Safety science, 121, 414-429.
  • Delcea, C., Milne, R. J., & Cotfas, L. A. (2020). Determining the Number of Passengers for Each of Three Reverse Pyramid Boarding Groups with COVID-19 Flying Restrictions. Symmetry, 12(12), 2038.
  • Derbal, Y. (2020). Agent-Based Model of Cell Signaling in Cancer. In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-4). IEEE.
  • Dhariwal, M., & Dhariwal, S. (2020, April). Let's Chance: Playful Probabilistic Programming for Children. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems Extended Abstracts (pp. 1-7).
  • Dhou, K. (2020). A new chain coding mechanism for compression stimulated by a virtual environment of a predator–prey ecosystem. Future Generation Computer Systems, 102, 650-669.
  • Dhou, K., & Cruzen, C. (2020). A highly efficient chain code for compression using an agent-based modeling simulation of territories in biological beavers. Future Generation Computer Systems.
  • Di Fiore, A. (2020). The Promise of Spatially Explicit Agent-Based Models for Primatology Research. Spatial Analysis in Field Primatology: Applying GIS at Varying Scales, 280.
  • Di Mauro, L. S., Mulder, C., Conti, E., & Pluchino, A. (2020). Robustness of soil ecosystems under different regimes of management. arXiv preprint arXiv:2005.13414.
  • Diaconescu, A., Di Felice, L. J., & Mellodge, P. (2020). Exogenous coordination in multi-scale systems: How information flows and timing affect system properties. Future Generation Computer Systems.
  • Díaz Monsalvea, J., Enríquez Corredor, I., Pinto Moreno, Á. M., & Sánchez Santamaría, J. (2020). Evaluación de una emulación de un sistema ASRS acoplado a un sistema de compras.
  • Dignum, F., Dignum, V., Davidsson, P., Ghorbani, A., van der Hurk, M., Jensen, M., ... & Mellema, R. (2020). Analysing the combined health, social and economic impacts of the corovanvirus pandemic using agent-based social simulation. arXiv preprint arXiv:2004.12809.
  • Dinh, L., & Parulian, N. (2020). COVID‐19 pandemic and information diffusion analysis on Twitter. Proceedings of the Association for Information Science and Technology, 57(1), e252.
  • Dogaroglu, B., & Caliskanelli, S. P. (2020). Investigation of car park preference by intelligent system guidance. Research in Transportation Business & Management, 100567.
  • Domínguez Cañizares, R., & Cannella, S. (2020). Insights on Multi-Agent Systems Applications for Supply Chain Management.
  • Dominguez, R., & Cannella, S. (2020). Insights on Multi-Agent Systems Applications for Supply Chain Management. Sustainability, 12(5), 1935.
  • Dong, J., Liu, R., Qiu, Y., & Crossan, M. (2020). Should knowledge be distorted? Managers' knowledge distortion strategies and organizational learning in different environments. The Leadership Quarterly, 101477.
  • Dore, K. M., Sewell, D., Mattenet, E. M., & Turner, T. R. (2020). GIS and GPS Techniques in an Ethnoprimatological Investigation of St Kitts Green Monkey (Chlorocebus sabaeus) Crop-Foraging Behavior. Spatial Analysis in Field Primatology: Applying GIS at Varying Scales, 403.
  • Dos Santos, A. T., Machado, C. M., & Adamatti, D. F. (2020). Circadian Rhythm and Pain: Mathematical Model based on Multiagent Simulation. Journal of Medical Systems, 44(10), 1-9.
  • Dou, Y., Xue, X., Wu, C., Luo, X., & Wang, Y. (2020). Interorganizational Diffusion of Prefabricated Construction Technology: Two-Stage Evolution Framework. Journal of Construction Engineering and Management, 146(9), 04020114.
  • Douglas, A., Mazzuchi, T., & Sarkani, S. (2020). A stakeholder framework for evaluating the‐ilities of autonomous behaviors in complex adaptive systems. Systems Engineering.
  • Drummond, F. A., & Collins, J. A. (2020). Field Perimeter Trapping to Manage Rhagoletis mendax (Diptera: Tephritidae) in Wild Blueberry. Journal of Economic Entomology.
  • Du, J., Zhao, D., Issa, R. R., & Singh, N. (2020). BIM for Improved Project Communication Networks: Empirical Evidence from Email Logs. Journal of Computing in Civil Engineering, 34(5), 04020027.
  • Duarte, R. A., Silva, D. F., & Alvarado, M. (2020). Cancer metastasis and the immune system response: modeling the micro-environment by Ising hamiltonian. Suplemento de la Revista Mexicana de Física, 1(4), 25-31.
  • Dubovi, I., Levy, S. T., Levy, M., Zuckerman Levin, N., & Dagan, E. (2020). Glycemic control in adolescents with type 1 diabetes: Are computerized simulations effective learning tools?. Pediatric Diabetes, 21(2), 328-338.
  • DuHadway, S., & Narasimhan, R. (2020). Subverting Process‐Based Controls: Oscillation in Automotive Recalls and a Simulation on Opportunism within a Network. Decision Sciences.
  • Eitzel, M. V., Solera, J., Wilson, K. B., Neves, K., Fisher, A. C., Veski, A., ... & Mhike Hove, E. (2020). Using mixed methods to construct and analyze a participatory agent-based model of a complex Zimbabwean agro-pastoral system. PloS one, 15(8), e0237638.
  • Eitzel, M. V., Solera, J., Wilson, K., Neves, K., Fisher, A., Veski, A., ... & Mhike Hove, E. (2020). Indigenous climate adaptation sovereignty in a Zimbabwean agro-pastoral system: exploring definitions of sustainability success using a participatory agent-based model. Ecology and Society, 25(4).
  • El-Khateeb, E., Burkhill, S., Murby, S., Amirat, H., Rostami-Hodjegan, A., & Ahmad, A. (2020). Physiological-based pharmacokinetic modeling trends in pharmaceutical drug.
  • Elfakir, A., Tkiouat, M., Pakgohara, A., & Fairchild, R. (2020). Can Real Options Reduce Moral hazards in Profit and Loss sahring contracts?: A Behavioural Approach Using Game Theory and Agent Based Simulation.
  • Elffers, H., Gerritsen, C., & Birks, D. (2020). Agent-based modeling for testing and developing theories. Agent-Based Modelling for Criminological Theory Testing and Development, 187.
  • Elzinga, D. C., Boggess, E., Collignon, J., Riederer, A., & Capaldi, A. (2020). An agent‐based model determining a successful reintroduction of the extinct passenger pigeon. Natural Resource Modeling, e12292.
  • Engebretsen, B. J. (2020). Teaching Through COVID-19 Part I: COVID-19, Public, and Global Health: It's Personal. Teaching through COVID-19 in Science Education and Civic Engagement, 12(2), 27.
  • ERÜMİT, A. K., ÖNGÖZ, S., & AKSOY, D. A. (2020). Designing A Computer Programming Environment For Gifted Students: A Case Study.
  • Escudero Marin, P. (2020). Using agent-based modelling and simulation to model performance measurement in healthcare (Doctoral dissertation, Lancaster University).
  • Evans, B. P., Glavatskiy, K., Harré, M. S., & Prokopenko, M. (2020). The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model. arXiv preprint arXiv:2009.06914.
  • Evans, L. C., Oliver, T. H., Sims, I., Greenwell, M. P., Melero, Y., Watson, A., ... & Walters, R. J. (2020). Behavioural modes in butterflies: their implications for movement and searching behaviour. Animal Behaviour, 169, 23-33.
  • Ezzat, H. M. (2020). Behavioral agent-based framework for interacting financial markets. Review of Economics and Political Science.
  • Fajardo, S., Hofstede, G. J., de Vries, M., Kramer, M. R., & Bernal, A. (2020). Gregarious Behavior, Human Colonization and Social Diferentiation: An Agent-based Model. SocArXiv. September, 26.
  • Fard, G. G., Bradley, E., & Peleg, O. (2020). Data-Driven Modeling of Resource Distribution in Honeybee Swarms. bioRxiv.
  • Faria, L. F. F. D., Asevedo, L. F. D., Vieira, J. G. V., & Silva, J. E. A. R. D. (2020). A combined approach of multiple-criteria decision analysis and discrete-event simulation: lessons learned from a fleet composition study. World Review of Intermodal Transportation Research, 9(2), 97-119.
  • Farias, G., Leitzke, B., Born, M., Aguiar, M., & Adamatti, D. (2020). Water Resources Analysis: An Approach based on Agent-Based Modeling. Revista de Informática Teórica e Aplicada, 27(2), 81-95.
  • Farjam, M., & Bravo, G. (2020). Fixing Sample Biases in Experimental Data Using Agent-Based Modelling. In Advances in Social Simulation (pp. 155-159). Springer, Cham.
  • Farjamirad, M., & Niknami, K. A. (2020). Frequency of Using Stone Ossuaries in Marvdasht Plain (Fourth–Seventh Century AD): Explaining Funerary Patterns Through Agent-Based Modelling. In Archaeology of Iran in the Historical Period (pp. 363-371). Springer, Cham.
  • Fazio, C. (2020). Active Learning Methods and Strategies to Improve Student Conceptual Understanding: Some Considerations from Physics Education Research. In Research and Innovation in Physics Education: Two Sides of the Same Coin (pp. 15-35). Springer, Cham.
  • Fedriani, J. M., Ayllón, D., Wiegand, T., & Grimm, V. (2020). Intertwined effects of defaunation, increased tree mortality and density compensation on seed dispersal. Ecography.
  • Feinberg, A., Ghorbani, A., & Herder, P. M. (2020). Commoning toward urban resilience: The role of trust, social cohesion, and involvement in a simulated urban commons setting. Journal of Urban Affairs, 1-26.
  • Feinberg, A., Hooijschuur, E., & Ghorbani, A. (2020). Simulation of Behavioural Dynamics Within Urban Gardening Communities. In Advances in Social Simulation (pp. 161-167). Springer, Cham.
  • Fekih, S. (2020). People Displacement in a Conflict Zone–A Case Study of Mosul Battle, Iraq (Master's thesis, University of Twente).
  • Feliciani, T., Flache, A., & Mäs, M. (2020). Persuasion without polarization? Modelling persuasive argument communication in teams with strong faultlines. Computational and Mathematical Organization Theory, 1-32.
  • Feliciani, T., Moorthy, R., Lucas, P., & Shankar, K. (2020). Grade Language Heterogeneity in Simulation Models of Peer Review. Journal of Artificial Societies and Social Simulation, 23(3), 1-8.
  • Ferreira, D. C. S., Tejada, J., & dos Santos Bispo, G. R. (2020). A NetLogo implementation of the norms and meta-norms game: Behavior Analysis meets Agent Based Modeling.
  • Fichera, A., Pluchino, A., & Volpe, R. (2020). From self-consumption to decentralized distribution among prosumers: A model including technological, operational and spatial issues. Energy Conversion and Management, 217, 112932.
  • Fichera, A., Pluchino, A., & Volpe, R. (2020). Modelling Energy Distribution in Residential Areas: A Case Study Including Energy Storage Systems in Catania, Southern Italy. Energies, 13(14), 3715.
  • Fioretti, G., & Lomi, A. (2020). Emergence of Organizations out of Garbage Can Dynamics. Available at SSRN 3581357.
  • Fioretti, G., & Policarpi, A. (2020). The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?. Or, Possibly Not.
  • Flood, V. J., Shvarts, A., & Abrahamson, D. (2020). Teaching with embodied learning technologies for mathematics: Responsive teaching for embodied learning. ZDM Mathematics Education, 52(7), 1307-1331. https://doi.org/10.1007/s11858-020-01165-7
  • Fouladvand, J., Mouter, N., Ghorbani, A., & Herder, P. (2020). Formation and Continuation of Thermal Energy Community Systems: An Explorative Agent-Based Model for the Netherlands. Energies, 13(11), 2829.
  • Fränken, J. P., & Pilditch, T. (2020). Cascades across networks are sufficient for the formation of echo chambers: An agent-based model.
  • Frantz, C. K. (2020). Impact of Meta-roles on the Evolution of Organisational Institutions. In Multi-Agent-Based Simulation XXI: 21st International Workshop, MABS 2020, Auckland, New Zealand, May 10, 2020, Revised Selected Papers (p. 66). Springer Nature.
  • Friedrich, I. D., Hirnsperger, M., & Bauer, S. Understanding the Demographic Future of Small Arctic Villages Using Agent-Based Modeling.
  • Fu, Q., Tian, Y., & Sun, J. (2020). Integration of an Agent-Based Joint Route and Departure Time Choice Model with the Dynamic Traffic Assignment Package. In CICTP 2020 (pp. 3087-3099).
  • Füllsack, M., Kapeller, M., Plakolb, S., & Jäger, G. (2020). Training LSTM-Neural Networks on Early Warning Signals of declining cooperation in simulated Repeated Public Good Games. MethodsX, 100920.
  • Gah, E. (2020). Ant-Inspired Control Strategies for Collective Transport by Dynamic Multi-Robot Teams with Temporary Leaders (Doctoral dissertation, Arizona State University).
  • Gajary, L. C. (2020). Hybridizing agent-based with system dynamics models: principles for theory development in public policy and management research. Handbook of Research Methods in Public Administration, Management and Policy, 63.
  • Galindro, A., Matias, J., Cerveira, A., Santos, C., & Marta-Costa, A. (2020). Prediction of Viticulture Farms Behaviour: An Agent-Based Model Approach. In The Changing Role of SMEs in Global Business (pp. 155-178). Palgrave Macmillan, Cham.
  • Gallagher, C. A., Grimm, V., Kyhn, L. A., Kinze, C. C., & Nabe-Nielsen, J. (2020). Movement and seasonal energetics mediate vulnerability to disturbance in marine mammal populations.
  • Gao, J., Zheng, D., & Yang, S. (2020). Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics. Personal and Ubiquitous Computing, 1-14.
  • Gao, S., Song, X., & Ding, R. (2020). Promoting Information Transfer in Collaborative Projects through Network Structure Adjustment. Journal of Construction Engineering and Management, 146(2), 04019108.
  • Garcia Filho, C. (2020). Simulating social distancing measures in household and close contact transmission of SARS-CoV-2. Cadernos de Saúde Pública, 36(5).
  • García, A. P., & Rodríguez-Patón, A. (2020). Applying Evolutionary Metaheuristics for Parameter Estimation of Individual-Based Models. arXiv preprint arXiv:2005.12841.
  • Garibo i Orts, Ó., Conejero, J. A., & Urchueguía, J. F. (2020). Rational Design of a Genetic Finite State Machine: Combining Biology, Engineering, and Mathematics for Bio-Computer Research. Mathematics, 8(8), 1362.
  • Garrigan, S. R. (2020). Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools. In Handbook of Research on Integrating Computer Science and Computational Thinking in K-12 Education (pp. 30-44). IGI Global.
  • Gatto, J. V., & Trexler, J. C. (2020). Speed and directedness predict colonization sequence post-disturbance. Oecologia, 1-15.
  • Gautam, A., Bortz, W., & Tatar, D. (2020, February). Abstraction Through Multiple Representations in an Integrated Computational Thinking Environment. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 393-399).
  • Geaves, L. (2020). Agent-Based Modeling of Flood Insurance Futures. In Oxford Research Encyclopedia of Natural Hazard Science.
  • Gendreau Chakarov, A., Biddy, Q., Jacobs, J., Recker, M., & Sumner, T. (2020, August). Opening the Black Box: Investigating Student Understanding of Data Displays Using Programmable Sensor Technology. In Proceedings of the 2020 ACM Conference on International Computing Education Research (pp. 291-301).
  • Gerritsen, C., & Elffers, H. (2020). Agent-based modeling for criminological theory testing and development. Agent-Based Modelling for Criminological Theory Testing and Development, 1.
  • Gersie, S. (2020). PREDICTING CATTLE GRAZING DISTRIBUTIONS: AN AGENT-BASED MODELING APPROACH. 2020-CSU Theses and Dissertations.
  • Gesell, S. B., de la Haye, K., Sommer, E. C., Saldana, S. J., Barkin, S. L., & Ip, E. H. (2020). Identifying Social Network Conditions that Facilitate Sedentary Behavior Change: The Benefit of Being a “Bridge” in a Group-based Intervention. International Journal of Environmental Research and Public Health, 17(12), 4197.
  • Geyer, J., Nguyen, J., Farrenkopf, T., & Guckert, M. (2020, October). AGADE Traffic 2.0-A Knowledge-Based Approach for Multi-agent Traffic Simulations. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 417-420). Springer, Cham.
  • Ghaitaranpour, A., Mohebbi, M., Koocheki, A., & Ngadi, M. O. (2020). An agent-based coupled heat and water transfer model for air frying of doughnut as a heterogeneous multiscale porous material. Innovative Food Science & Emerging Technologies, 102335.
  • Gharakhanlou, N. M., & Hooshangi, N. (2020, July). Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (case study: Urmia, Iran). Informatics in Medicine Unlocked, 20.
  • Gharakhanlou, N. M., Hooshangi, N., & Helbich, M. (2020). A Spatial Agent-Based Model to Assess the Spread of Malaria in Relation to Anti-Malaria Interventions in Southeast Iran. ISPRS International Journal of Geo-Information, 9(9), 549.
  • Gilbert, L. Teaching Geoscience Tools for Addressing Societal Grand Challenges: A Unique Study-Away Experience During COVID-19. Teaching through COVID-19, 32.
  • Girwidz, R. (2020). Simulating waves and macroscopic phonons. European Journal of Physics.
  • Gkiolmas, A., Stoumpa, A., Chalkidis, A., & Skordoulis, C. (2020). A Combination of Historical Physics Documents and Other Teaching Tools for the Instruction of Prospective Teachers in Chaos and Complexity. In Fundamental Physics and Physics Education Research (pp. 251-261). Springer, Cham.
  • Godois, L. M., Adamatti, D. F., & Emmendorfer, L. R. (2020). A multi-agent-based algorithm for data clustering. Progress in Artificial Intelligence, 1-9.
  • Gomes, E., Banos, A., Abrantes, P., Rocha, J., & Schläpfer, M. (2020). Future land use changes in a peri-urban context: Local stakeholder views. Science of The Total Environment, 137381.
  • Gomez, M. M., & Weiss, M. B. (2020). A comprehensive secondary market model for virtualized wireless connectivity. Telecommunications Policy, 44(10), 102021.
  • Gona, R. (2020). Application of Micro Cloud for Cooperative Vehicles (Doctoral dissertation, Southern Illinois University at Carbondale).
  • Gong, Y. (2020). Influence Among Preferences and Its Transformation to Behaviors in Groups. In Group Decision and Negotiation: A Multidisciplinary Perspective: 20th International Conference on Group Decision and Negotiation, GDN 2020, Toronto, ON, Canada, June 7–11, 2020, Proceedings (p. 104). Springer Nature.
  • González-Méndez, M., Olaya, C., Fasolino, I., Grimaldi, M., & Obregón, N. (2020). Agent-Based Modeling for Urban Development Planning based on Human Needs. Conceptual Basis and Model Formulation. Land Use Policy, 105110.
  • Grajdura, S. A., Borjigin, S. G., & Niemeier, D. A. (2020, November). Agent-based wildfire evacuation with spatial simulation: a case study. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (pp. 56-59).
  • Grantham, E. O., & Giabbanelli, P. J. (2020, May). Creating perceptual uncertainty in agent-based models with social interactions. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-12).
  • Gravel-Miguel, C., & Coward, F. Palaeolithic Social Networks and Behavioural Modernity. In Brughmans, T., Mills, B., Munson, J., & Peeples, M. (eds.), The Oxford Handbook of Archaeological Network Research. Oxford: Oxford University Press.
  • Greco, A., Pluchino, A., Caddemi, S., Caliò, I., & Cannizzaro, F. (2020). On profile reconstruction of Euler–Bernoulli beams by means of an energy based genetic algorithm. Engineering with Computers, 36(1), 239-250.
  • Green, D. G., Klomp, N. I., Rimmington, G., & Sadedin, S. (2020). Virtual Worlds: The Role of Simulation in Ecology. In Complexity in Landscape Ecology (pp. 177-195). Springer, Cham.
  • Greenberg, B. (2020). Improving Information Exchange in Disaster Response: Responder Behavior and the Effects of Organizational Design (Doctoral dissertation, The George Washington University).
  • Gregg, R. (2020). Multi-Scale Modeling of the Innate Immune System: A Dynamic Investigation into Pathogenic Detection (Doctoral dissertation, University of Pittsburgh).
  • Gresalfi, M., Brady, C., Knowe, M., & Steinberg, S. (2020). Engaging in a New Practice: What Are Students Doing When They Are “Doing” Debugging?.
  • Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAngelis, D. L., ... & Johnston, A. S. (2020). The ODD protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism. Journal of Artificial Societies and Social Simulation, 23(2).
  • Groff, E. R., & Badham, J. (2020). Examining guardianship against theft. Agent-Based Modelling for Criminological Theory Testing and Development, 71.
  • GRONAU, S., HADERSDORFER, J., NÖLDEKE, B., PETRUSJANZ, N., STÜTZEL, H., & WINTER, E. (2020). Food security in rural Zambia.
  • Grover, S., Ventures, L. G., Biswas, G., Farris, A. V., Sengupta, P., Covitt, B. A., ... & Horn, M. (2020). Integrating STEM and Computing in PK-12: Operationalizing Computational Thinking for STEM Learning and Assessment. The Interdisciplinarity of the Learning Sciences.
  • Guerrin, F. (2020). Agent-Based Modelling of a Simple Synthetic Rangeland Ecosystem. In Landscape Modelling and Decision Support (pp. 179-215). Springer, Cham.
  • Gulied, M., Al Nouss, A., Khraisheh, M., & AlMomani, F. (2020). Modeling and simulation of fertilizer drawn forward osmosis process using Aspen Plus-MATLAB model. Science of The Total Environment, 700, 134461.
  • Gumzej, R., & Rakovska, M. (2020). Simulation Modeling and Analysis for Sustainable Supply Chains. In Sustainable Logistics and Production in Industry 4.0 (pp. 145-160). Springer, Cham.
  • Guo, X., Chen, J., Azizi, A., Fewell, J., & Kang, Y. (2020). Dynamics of Social Interactions, in the Flow of Information and Disease Spreading in Social Insects Colonies: Effects of Environmental Events and Spatial Heterogeneity. Journal of Theoretical Biology, 110191.
  • Hadjimichael, A., Gold, D., Hadka, D., & Reed, P. (2020). Rhodium: Python Library for Many-Objective Robust Decision Making and Exploratory Modeling. Journal of Open Research Software, 8(1).
  • Hai-xiang, G. U. O., Jin-ling, L. I., Long-hui, L. I. U., & Xiao-ling, K. E. (2020). й Ⱥ ݱ Ķ Agent . ϵͳ ѧ , 22(4), 579-585.
  • Hajmohammad, S., & Shevchenko, A. (2020). Mitigating sustainability risk in supplier populations: an agent-based simulation study. International Journal of Operations & Production Management.
  • Hall, S. M. (2020). Opportunities and Obstacles in the Transition to a Distributed Network of Rooftop Solar: A Multi-Method Approach.
  • Han, C. K., Sadiq, A. S., Mirjalili, S., & Tahir, A. (2020). Trust aware crowd associated network-based approach for optimal waste management in smart cities.
  • Han, X. (2020, December). Influence of exits and evacuees on evacuation efficiency. In IOP Conference Series: Earth and Environmental Science (Vol. 608, No. 1, p. 012031). IOP Publishing.
  • Hanaček, K., Langemeyer, J., Bileva, T., & Rodríguez-Labajos, B. Understanding environmental conflicts through cultural ecosystem services-the case of agroecosystems in Bulgaria. Ecological Economics, 179, 106834.
  • Hannoun, G. J., Murray-Tuite, P., Heaslip, K., & Chantem, T. (2020). Meso-and Micro-scopic Routing of an Emergency Response Vehicle with Connected Vehicle Technologies.
  • Hartbauer, M. (2020). From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising. In Bio-Inspired Technology. IntechOpen.
  • Hartmann, S. (2020). A Conversation about Modeling in Philosophy.
  • Hasbach, J. D., Witte, T. E., & Bennewitz, M. (2020, July). On the Importance of Adaptive Operator Training in Human-Swarm Interaction. In International Conference on Human-Computer Interaction (pp. 311-329). Springer, Cham.
  • Hassan, I. M., & Hassan, K. R. (2020). Vehicular Social Networks and Vehicular Ad-hoc Networks, Applications, Modelling Tools and Challenges: A Survey. International Journal of Computer Applications, 975, 8887.
  • Hasson, S. T., & Hussein, Z. (2020, February). Correlation among network centrality metrics in complex networks. In 2020 6th International Engineering Conference “Sustainable Technology and Development"(IEC) (pp. 54-58). IEEE.
  • Hauke, J., Achter, S., & Meyer, M. (2020). Theory development via replicated simulations and the added value of standards. The journal of artificial societies and social simulation, 23(1).
  • He, Z., Huang, D., Fang, J., & Wang, B. (2020). Stakeholder Conflict Amplification of Large-Scale Engineering Projects in China: An Evolutionary Game Model on Complex Networks. Complexity, 2020.
  • Healy, C., Pekins, P. J., Atallah, S., & Congalton, R. G. (2020). Using agent-based models to inform the dynamics of winter tick parasitism of moose. Ecological Complexity, 41, 100813.
  • Helikar, T. (2020). The Need for Research-Grade Systems Modeling Technologies for Life Science Education. Trends in Molecular Medicine.
  • Herberich, M. M., Gayler, S., Anand, M., & Tielbörger, K. (2020). Biomass–density relationships of plant communities deviate from the self‐thinning rule due to age structure and abiotic stress. Oikos.
  • Hernández, P., Pena, C., Ramos, A., & Gómez-Cadenas, J. J. (2020). A simple formulation of non-Markovian SEIR. arXiv preprint arXiv:2005.09975.
  • Hess, B., Dreber, N., Liu, Y., Wiegand, K., Ludwig, M., Meyer, H., & Meyer, K. M. (2020). PioLaG: a piosphere landscape generator for savanna rangeland modelling. Landscape Ecology, 1-22.
  • Highlander, H., & Singley, A. (2020). COVID-19: A Mathematical Model for the Effect of Social Distancing on the Spread of COVID-19. Letters in Biomathematics.
  • Hjorth, A., Head, B., Brady, C. & Wilensky, U. (2020). LevelSpace – a NetLogo Extension for Multi-Level Agent-Based Modeling. Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-4.
  • Hoffmann, B., Urquhart, N., Chalmers, K., & Guckert, M. (2020). Athos: An Extensible DSL for Model Driven Traffic and Transport Simulation. Modellierung 2020.
  • Hokamp, S. Agent-based Modeling of Human Exposure to Urban Environmental Stressors–A Docking Study.
  • Hooten, M., Wikle, C., & Schwob, M. (2020). Statistical Implementations of Agent‐Based Demographic Models. International Statistical Review.
  • Hou, J., Yu, T., & Xiao, R. (2020, September). Structure Reversal of Online Public Opinion for the Heterogeneous Health Concerns under NIMBY Conflict Environmental Mass Events in China. In Healthcare (Vol. 8, No. 3, p. 324). Multidisciplinary Digital Publishing Institute.
  • Hsiao, Y. (2020). Evaluating the Mobilization Effect of Online Political Network Structures: A Comparison between the Black Lives Matter Network and Ideal Type Network Configurations. Social Forces.
  • Huang, M., & Pape, A. D. (2020). The Impact of Online Consumer Reviews on Online Sales: The Case-Based Decision Theory Approach. Journal of Consumer Policy, 1-28.
  • Huber, R., Hang, X., Keller, K., & Finger, R. (2020). FARMIND: Farm Interaction and Decision Model.
  • Hui, W., Xin-gang, Z., Ling-zhi, R., & Fan, L. (2020). An agent-based modeling approach for analyzing the influence of market participants' strategic behavior on green certificate trading. Energy, 119463.
  • Hussein, S. E. (2020). Rehabilitation Center Planning using Multi-agent Systems (Dept. E). MEJ. Mansoura Engineering Journal, 34(1), 22-30.
  • Husssein, A. A., Salman, M. A., Al Essa, H. A., & Hussein, N. Y. (2020). Developing Agent-Based Model for Colorization. Journal of University of Babylon for Pure and Applied Sciences, 147-157.
  • Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., ... & McElhaney, K. (2020). C2STEM: a System for Synergistic Learning of Physics and Computational Thinking. Journal of Science Education and Technology, 29(1), 83-100.
  • Hutchins, N. M., Biswas, G., Wolf, R. C., Chin, D. B., Grover, S., Ventures, L. G., & Blair, K. P. (2020). Computational Thinking in Support of Learning and Transfer. The Interdisciplinarity of the Learning Sciences.
  • Hutchins, N. M., Biswas, G., Zhang, N., Snyder, C., Lédeczi, Á., & Maróti, M. (2020). Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Learning through Computational Modeling. International Journal of Artificial Intelligence in Education, 1-44.
  • Hwang, I. (2020). An Agent-Based Model of Firm Size Distribution and Collaborative Innovation. Journal of Artificial Societies and Social Simulation, 23(1), 1-9.
  • Iasiello, C. A. (2020, May). Using agent based modeling to interpret underlying factors of underrepresentation of minorities in Hollywood films. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-12).
  • Iasiello, C., Crooks, A., & Wittman, S. (2020, October). The Human Resource Management Parameter Experimentation Tool. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 298-307). Springer, Cham.
  • Ibrahim, B. K., Mahdi, M. A., & Salman, M. A. (2020, April). Triple Mobile Anchors Approach for Localization in WSN. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 174-179). IEEE.
  • Imran, M., Rodrigues, J. J., Kamal, A. E., Ahmed, E., Xia, F., & Awan, I. (2020). IEEE Access Special Section Editorial: Survivability Strategies for Emerging Wireless Networks. IEEE Access, 8, 225219-225225.
  • Innocenti, E., Detotto, C., Idda, C., Parker, D. C., & Prunetti, D. (2020). An iterative process to construct an interdisciplinary ABM using MR POTATOHEAD: An application to Housing Market Models in touristic areas. Ecological Complexity, 44, 100882.
  • Insua, D. R., Baylon, C., & Vila, J. (Eds.). (2020). Security Risk Models for Cyber Insurance. CRC Press.
  • Ionuț, N. I. C. A. (2020). Simulation of financial contagion effect using the NetLogo software at the level of the banking network. Theoretical and Applied Economics, 22(3 (624), Autumn), 55-74.
  • Irgens, G. A., Dabholkar, S., Bain, C., Woods, P., Hall, K., Swanson, H., ... & Wilensky, U. (2020). Modeling and Measuring High School Students’ Computational Thinking Practices in Science. Journal of Science Education and Technology, 29(1), 137-161.
  • Isgro, F. (2020). Fuselage Design Studies to Improve Boarding Performance of Novel Passenger Aircraft: An Approach from Knowledge-Based Engineering and Agent-Based Modelling.
  • Ivanek, R., & Wiedmann, M. (2020). CPS 2018 RFP FINAL PROJECT REPORT.
  • Ivars-Silva, F., Rossetti, R. J., & Porto, P. (2020). Emotional Contagion Modeled Through the Empathy Quotient: An Epidemiological Analogy Towards Social Sustainability. In 2020 IEEE International Smart Cities Conference (ISC2) (pp. 1-8). IEEE.
  • Izquierdo, S. S., & Izquierdo, L. R. “TEST TWO, CHOOSE THE BETTER” LEADS TO HIGH COOPERATION IN THE CENTIPEDE GAME.
  • Jablonski, K. E., Boone, R. B., & Meiman, P. J. (2020). Predatory plants and patchy cows: modeling cattle interactions with toxic larkspur amid variable heterogeneity. Rangeland Ecology & Management, 73(1), 73-83.
  • Jacobson, M. J., Goldwater, M., Markauskaite, L., Lai, P. K., Kapur, M., Roberts, G., & Hilton, C. (2020). Schema abstraction with productive failure and analogical comparison: Learning designs for far across domain transfer. Learning and Instruction, 65, 101222.
  • Jacquet, J. M., & Barkallah, M. (2020). Anemone: A workbench for the Multi-Bach coordination language. Science of Computer Programming, 102579.
  • Jaffer, M. A. (2020, November). Can Zakat Charity Help Reduce Economic Inequality?. In International Conference of Zakat (pp. 279-294).
  • Jager, W., & Yamu, C. (2020). 19. Simulating community dynamics for transitional urban planning processes. Handbook on Planning and Complexity, 373.
  • Jager, W., Abramczuk, K., Komendant-Brodowska, A., Baczko-Dombi, A., Fecher, B., Sokolovska, N., & Spits, T. (2020). Looking into the Educational Mirror: Why Computation Is Hardly Being Taught in the Social Sciences, and What to Do About It. In Advances in Social Simulation (pp. 239-245). Springer, Cham.
  • Jagielski, P. M. (2020). Exploring the Energetic Consequences and Decision-Making Behaviours of Polar Bears (Ursus maritimus) Foraging on Common Eider (Somateria mollissima) Seaduck Eggs on Mitivik Island, Nunavut (Doctoral dissertation, University of Windsor (Canada)).
  • Jayadevan, A., Nayak, R., Karanth, K. K., Krishnaswamy, J., DeFries, R., Karanth, K. U., & Vaidyanathan, S. (2020). Navigating paved paradise: Evaluating landscape permeability to movement for large mammals in two conservation priority landscapes in India. Biological Conservation, 247, 108613.
  • Jessica, S. Y. (2020). Multi-Scale, Multi-Class Agent-Based Models of Biological Systems (Doctoral dissertation, Northwestern University).
  • Jiang, F., Zhang, J., & Zhao, X. (2020). Research on the influence mechanism of resettlers participation in migrant work in the context of relationship network. Peer-to-Peer Networking and Applications, 1-10.
  • Jiang, G., Feng, X., Liu, W., & Liu, X. (2020). Clicking position and user posting behavior in online review systems: A data-driven agent-based modeling approach. Information Sciences, 512, 161-174.
  • Jiang, H., Chen, C., Zhao, S., & Wu, Y. (2020). Evolution of a Technology Standard Alliance Based on an Echo Model Developed through Complex Adaptive System Theory. Complexity, 2020.
  • Jiang, X., & Zhao, B. (2020). Modeling on the epidemic of coronavirus disease 2019. J Bio Med Open Access, 1(1), 103.
  • Jimenez, A. F., Cardenas, P. F., Canales, A., Jimenez, F., & Portacio, A. (2020). A survey on intelligent agents and multi-agents for irrigation scheduling. Computers and Electronics in Agriculture, 105474.
  • Johanes, P. (2020). Technology as a Gateway to a Philosophy of the Learning Sciences. Stanford University.
  • Joshi, M. Y., Flacke, J., & Schwarz, N. (2020). Do microfinance institutes help slum-dwellers in coping with frequent disasters? An agent-based modelling study. International Journal of Disaster Risk Reduction, 101627.
  • Kaaronen, R. O., & Strelkovskii, N. (2020). Cultural Evolution of Sustainable Behaviors: Pro-environmental Tipping Points in an Agent-Based Model. One Earth, 2(1), 85-97.
  • Kabora, T. K., Stump, D., & Wainwright, J. (2020). How did that get there? Understanding sediment transport and accumulation rates in agricultural landscapes using the ESTTraP agent-based model. Journal of Archaeological Science: Reports, 29, 102115.
  • Kafai, Y., Hutchins, N., Snyder, C., Brennan, K., Haduong, P., DesPortes, K., ... & Fields, D. (2020). Turning Bugs into Learning Opportunities: Understanding Debugging Processes, Perspectives and Pedagogies.
  • Kaiser, K. E., Flores, A. N., & Vernon, C. R. (2020). Janus: A Python Package for Agent-Based Modeling of Land Use and Land Cover Change. Journal of Open Research Software, 8(1).
  • Kaligotla, C., Yücesan, E., & Chick, S. E. (2020). Diffusion of competing rumours on social media. Journal of Simulation, 1-21.
  • Kampik, T., & Najjar, A. (2020). Simulating, Off-Chain and On-Chain: Agent-Based Simulations in Cross-Organizational Business Processes. Information, 11(1), 34.
  • Kantasa-ard, A., Nouiri, M., Bekrar, A., Ait el cadi, A., & Sallez, Y. (2020). Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand. International Journal of Production Research, 1-25.
  • Karimi, M. J., & Vaez-Zadeh, S. (2020). An Agent-Based Model for Electric Energy Policy Assessment. Electric Power Systems Research, 106903.
  • Karnouskos, S., Leitao, P., Ribeiro, L., & Colombo, A. W. (2020). Industrial Agents as a Key Enabler for Realizing Industrial Cyber-Physical Systems: Multiagent Systems Entering Industry 4.0. IEEE Industrial Electronics Magazine, 14(3), 18-32.
  • Karsai, I., Schmickl, T., & Kampis, G. (2020). Forest Fires: Fire Management and the Power Law. In Resilience and Stability of Ecological and Social Systems (pp. 63-77). Springer, Cham.
  • Karsai, I., Schmickl, T., & Kampis, G. (2020). Habitat Fragmentation. In Resilience and Stability of Ecological and Social Systems (pp. 47-61). Springer, Cham.
  • Katerndahl, D. A., Burge, S. K., Ferrer, R. L., Wood, R., & Montanez Villacampa, M. D. P. (2020). Modeling Women’s Need For Action in Violent Relationships. Journal of interpersonal violence, 0886260519900943.
  • Katz, K., & Naug, D. (2020). A mechanistic model of how metabolic rate can interact with resource environment to influence foraging success and lifespan. Ecological Modelling, 416, 108899.
  • Kaur, H., Kaur, H., & Singh, A. (2020). Multi-agent Based Recommender System for Netflix. In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India (pp. 211-221). Springer, Singapore.
  • Kazil, J., Masad, D., & Crooks, A. (2020, October). Utilizing Python for Agent-Based Modeling: The Mesa Framework. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 308-317). Springer, Cham.
  • Kelsey, J., & Kadivar, S. (2020). Stakeholder-driven design and appraisal in hydroelectric projects: a participatory gaming approach.
  • Kelter, J. Z., Peel, A., Bain, C., Anton, G., Dabholkar, S., Aslan, Ü., Horn, M., & Wilensky, U. (2020). Seeds of (r)Evolution: Constructionist Co-Design with High School Science Teachers. In B. Tangney, J. Rowan Byrne, & C. Girvan (Eds.), Proceedings of the 2020 Constructionism Conference, Dublin, Ireland, May 26—May 29, 2020 (p.497-505). (ISBN 978-1-911566-09-0)
  • Khadim, S., Riaz, F., Jabbar, S., Khalid, S., & Aloqaily, M. (2020). A non-cooperative rear-end collision avoidance scheme for non-connected and heterogeneous environment. Computer Communications, 150, 828-840.
  • Khan, I., Lewis, M., & Cañamero, L. (2020, July). Modelling the Social Buffering Hypothesis in an Artificial Life Environment. In Artificial Life Conference Proceedings (pp. 393-401). One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press.
  • Khansari, N., & Hewitt, E. (2020). Incorporating an agent-based decision tool to better understand occupant pathways to GHG reductions in NYC buildings. Cities, 97, 102503.
  • Kharlov, L. (2020). Modelling the Disruptive Innovations. Review of Business and Economics Studies, (3).
  • Khelfa, B., & Tordeux, A. (2020). DYNAMIC SAFETY ANALYSIS FOR AUTOMATED DRIVING.
  • Kim, I., & Kwon, H. (2020). Assessing the Impacts of Urban Land Use Changes on Regional Ecosystem Services According to Urban Green Space Policies Via the Patch-Based Cellular Automata Model. Environmental Management, 1-13.
  • Klaßmann, S., Dahmen, N., & Seifert, U. (2020). A Digital Habitat for interdisciplinary music research and teaching.
  • Knöös Franzén, L., Schön, S., Papageorgiou, A., Staack, I., Ölvander, J., Krus, P., ... & Jouannet, C. (2020). A System of Systems Approach for Search and Rescue Missions. In AIAA Scitech 2020 Forum (p. 0455).
  • Koehler, M., Slater, D. M., Jacyna, G., & Thompson, J. R. (2020). Modeling COVID-19 for lifting non-pharmaceutical interventions. medRxiv.
  • Koľveková, G., Raisová, M., Zoričak, M., & Gazda, V. (2020). Endogenous Shared Punishment Model in Threshold Public Goods Games. Computational Economics, 1-25.
  • Kooi, B. W., & Kooijman, S. A. L. M. (2020). A cohort projection method to follow deb-structured populations with periodic, synchronized and iteroparous reproduction. Ecological Modelling, 436, 109298.
  • Koralewski, T. E., Wang, H. H., Grant, W. E., Brewer, M. J., Elliott, N. C., Westbrook, J. K., ... & Michaud, J. P. (2020). Integrating Models of Atmospheric Dispersion and Crop-Pest Dynamics: Linking Detection of Local Aphid Infestations to Forecasts of Region-Wide Invasion of Cereal Crops. Annals of the Entomological Society of America.
  • Koralewski, T. E., Wang, H. H., Grant, W. E., LaForest, J. H., Brewer, M. J., Elliott, N. C., & Westbrook, J. K. (2020). Toward near-real-time forecasts of airborne crop pests: Aphid invasions of cereal grains in North America. Computers and Electronics in Agriculture, 179, 105861.
  • Korb, S., & Sacks, R. (2020). Agent-Based Simulation of General Contractor–Subcontractor Interactions in a Multiproject Environment. Journal of Construction Engineering and Management, 147(1), 04020151.
  • Koretsky, M. D. (2020). An interactive virtual laboratory addressing student difficulty in differentiating between chemical reaction kinetics and equilibrium. Computer Applications in Engineering Education, 28(1), 105-116.
  • Krawczyk, M. J., & Kułakowski, K. (2020). How to be influential being weakly connected. Physica D: Nonlinear Phenomena, 132644.
  • Kshirsagar, J., Hayatnagarkar, H., & Dewan, A. (2020). EPIRUST: TOWARDS A FRAMEWORK FOR LARGE-SCALE AGENT-BASED EPIDEMIOLOGICAL SIMULATIONS USING RUST LANGUAGE.
  • Kuznetsov, A. V., Halaimova, A. V., Ufimtseva, M. A., & Chelebieva, E. S. (2020). Blocking a chemical communication between Trichoplax organisms leads to their disorderly movement. International Journal of Parallel, Emergent and Distributed Systems, 1-10.
  • Kynigos, C. Half-Baked Constructionism: A Strategy to Address the Challenge of Infusing Constructionism in Education in Greece. In Holbert, N., Berland, M., & Kafai, Y. B. (eds.), Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs, 61.
  • Laguna-Sanchez, G. A., & Lopez-Sauceda, J. (2020). Agent-Supported Heuristic Model for the Dynamic Spread of Infectious Diseases.
  • Laili, Y., Zhang, L., & Luo, Y. (2020). A pattern-based validation method for the credibility evaluation of simulation models. SIMULATION, 96(2), 151-167.
  • Lall, M. (2020). AN AGENT-BASED SIMULATION OF AN ALTERNATIVE PARKING BAY CHOICE STRATEGY. The South African Journal of Industrial Engineering, 31(2), 107-115.
  • Larson, H. (2020). Agent-Based Modeling of Locust Foraging and Social Behavior.
  • LEBLOND, V., DESBUREAUX, L., & BIELECKI, V. (2020). A NEW AGENT-BASED SOFTWARE FOR DESIGNING AND OPTIMIZING EMERGING MOBILITY SERVICES: APPLICATION TO CITY OF RENNES.
  • Lee, J. S., & Wolf-Branigin, M. (2020). Innovations in modeling social good: A demonstration with juvenile justice intervention. Research on Social Work Practice, 30(2), 174-185.
  • Lee, J. Y., Sadler, N. C., Egbert, R. G., Anderton, C. R., Hofmockel, K. S., Jansson, J. K., & Song, H. S. (2020). Deep Learning Predicts Microbial Interactions from Self-organized Spatiotemporal Patterns. Computational and Structural Biotechnology Journal.
  • Lee, S., & Clinedinst, L. (2020). Mathematical Biology: Expand, Expose, and Educate!. Bulletin of Mathematical Biology, 82(9), 1-15.
  • Legaspi, J., Canfield, C. I., Gill, K. S., Wyglinski, A. M., & Bhadai, S. V. (2020, May). Integrated Agent-Based Model for Broadband Resource Allocation Analysis. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (pp. 1-5). IEEE.
  • Leitzke, B., Pereira, L., & Adamatti, D. (2020, January). Simulação Multiagente para Controle de Poluição na Água. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional (pp. 142-153). SBC.
  • Lestari, D. P., Sabri, A., Handhika, T., Sari, I., & Fahrurozi, A. (2020, May). The simulation of evacuation from multistorey building using NetLogo. In IOP Conference Series: Materials Science and Engineering (Vol. 854, No. 1, p. 012060). IOP Publishing.
  • Lévy, P., Zhang, Y., & van de Pol, M. (2020). DESIGNING A POLICY MAK-ING TOOL TO MITIGATE SE-CURITY OF SUPPLY RISKS.
  • Li, F., Du, T. C., & Wei, Y. (2020). Enhancing supply chain decisions with consumers’ behavioral factors: An illustration of decoy effect. Transportation Research Part E: Logistics and Transportation Review, 144, 102154.
  • Li, S., Liu, Z., & Li, Y. (2020). Temporal and spatial evolution of online public sentiment on emergencies. Information Processing & Management, 57(2), 102177.
  • Li, Y., Schoenfeld, A. H., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On Computational Thinking and STEM Education.
  • Li, Z., Fang, A., Cui, H., Ding, J., Liu, B., Xie, G., ... & Xing, D. (2020). Synthetic bacterial consortium enhances hydrogen production in microbial electrolysis cells and anaerobic fermentation. Chemical Engineering Journal, 127986.
  • Liao, M., Qi, L., & Zhang, J. (2020). The Dynamic Evolution Mechanism of Heterogeneous OWOM—An Improved Viral Marketing Model. Information, 11(3), 140.
  • Liao, M., Zhang, J., & Wang, R. (2020). A dynamic evolutionary game model of web celebrity brand eWOM marketing control strategy. Asia Pacific Journal of Marketing and Logistics.
  • Lin, G., Palopoli, M., & Dadwal, V. (2020). From Causal Loop Diagrams to System Dynamics Models in a Data-Rich Ecosystem. In Leveraging Data Science for Global Health (pp. 77-98). Springer, Cham.
  • Lin, S. Y. (2020). Distributed Simulation of Interdependencies in Community Resilience (Doctoral dissertation).
  • Lira, M. (2020). How Knowledge-in-Pieces Informs Research in Math-Bio Education.
  • Lira, M. (2020). Why Knowledge Analysis Changes the Design of Computational Learning Environments in Biology Education.
  • Lisianti, S., Hagijanto, A. D., & Malkisedek, M. H. (2020). Kajian Visual Siger dalam Budaya Kontemporer Masyarakat Lampung. Jurnal DKV Adiwarna, 1(16), 11.
  • Liu, C. J., Liu, Z., Chai, Y. J., & Liu, T. T. (2020). Review of Virtual Traffic Simulation and Its Applications. Journal of Advanced Transportation, 2020.
  • Liu, C., Jackson, L. V., Hutchings, S. J., Tuesca, D., Moreno, R., Mcindoe, E., & Kaundun, S. S. (2020). A holistic approach in herbicide resistance research and management: from resistance detection to sustainable weed control. Scientific Reports, 10(1), 1-9.
  • Liu, C., Zhou, H., & Liu, H. (2020). The Difference of FOIPW between the Eastern Coastal Areas and Other Areas of China. Journal of Coastal Research, 103(sp1), 222-225.
  • Liu, G., Ye, J., & Argyres, C. (2020). Modeling and simulation of the knowledge growth process among new energy technology firms in the distributed innovation network. DYNA-Ingeniería e Industria, 95(1).
  • Liu, J., Zhang, M., & Nikita, N. Agent-based design research to explore the effectiveness of bottom-up organizational design in shaping sustainable vernacular landscapes: A case in Hailar, China. Landscape and Urban Planning, 205, 103961.
  • London, J. O. N. (2020). African-American High-Tech Enterprises: Agent-Based Modeling and Simulation for Innovation (Doctoral dissertation, University of Bridgeport).
  • London, J. O. N., & Sheikh, N. J. (2020). Innovation in African-American high-tech enterprises: a multi-agent approach. Entrepreneurship and Sustainability Issues, 7(4), 3101-3121.
  • Lorenz, F., & Jeyapragasan, G. (2020). The impact of climate change on tri-trophic interactions and crop production. The iScientist, 5(1), 4-12.
  • Lorenz, J., Neumann, M., & Schröder, T. (2020). Individual attitude change and societal dynamics: Computational experiments with psychological theories.
  • LORENZ, W. E., & WURZER, G. (2020). FLÄVIZ IN THE REZONING PROCESS.
  • Lorenz, W., & Wurzer, G. (2020). FRACAM: A 2.5 D Fractal Analysis Method for Facades. Education and Digital Theory - Ethics, Cybernetics, Feedback, Theory, 1, 495-504.
  • Lorig, F., & Timm, I. J. (2020). Simulation-Based Data Acquisition. In Principles of Data Science (pp. 1-15). Springer, Cham.
  • Lu, J., Liu, X., Feng, Y., & Lin, X. Simulation and Analysis of Community Energy Consumption Based on Multi-agent Modeling. In 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) (pp. 1280-1284). IEEE.
  • Lu, P., Zhang, Z., Li, M., Chen, D., & Yang, H. (2020). Agent-based modeling and simulations of terrorist attacks combined with stampedes. Knowledge-Based Systems, 106291.
  • Lu, Q., Fricke, G. M., Ericksen, J. C., & Moses, M. E. (2020). Swarm Foraging Review: Closing the Gap Between Proof and Practice. Current Robotics Reports, 1-11.
  • Lubida, A. P., Rajabi, M., Pilesjö, P., & Mansourian, A. (2020). Investigating an Agent Based Modelling approach for SDI planning: A case study of Tanzania NSDI development. South African Journal of Geomatics, 9(2), 198-218.
  • Luke, S. (2020). Assisted Parameter and Behavior Calibration in Agent-Based Models with Distributed Optimization. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection: 18th International Conference, PAAMS 2020, L’Aquila, Italy, October 7–9, 2020, Proceedings (p. 93). Springer Nature.
  • Luo, H., Wang, Z., Yang, S., Yang, H., & Gong, Y. (2020, June). Influence Among Preferences and Its Transformation to Behaviors in Groups. In International Conference on Group Decision and Negotiation (pp. 104-119). Springer, Cham.
  • Luthra, M., Izquierdo, E. J., & Todd, P. M. (2020, July). Cognition Evolves with the Emergence of Environmental Patchiness. In Artificial Life Conference Proceedings (pp. 450-458). One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press.
  • Luurssen-Masurel, N., Weel, A., Hazes, J., & De Jong, P. (2020). OP0283 COMPARING COST-UTILITY OF DMARDS IN SERONEGATIVE RHEUMATOID ARTHRITIS PATIENTS; A TREACH SUBANALYSIS.
  • Lv, X., Li, N., Xu, X., & Yang, Y. (2020). Understanding the emergence and development of online travel agencies: a dynamic evaluation and simulation approach. Internet Research.
  • Lynch, C. J., Diallo, S. Y., Kavak, H., & Padilla, J. J. (2020). A content analysis-based approach to explore simulation verification and identify its current challenges. Plos one, 15(5), e0232929.
  • Maestro-Prieto, J. A., Rodríguez, S., Casado, R., & Corchado, J. M. (2020). Agent organisations: from independent agents to virtual organisations and societies of agents. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(4), 55-70.
  • Magessi, N. T., & Antunes, L. (2020). Influences of Innovation in Market Value. In Advances in Social Simulation (pp. 291-305). Springer, Cham.
  • Maggi, E., & Vallino, E. (2020). Price-based and motivation-based policies for sustainable urban commuting: An agent-based model. Research in Transportation Business & Management, 100588.
  • Mahdavi, A. (2020). Bringing HIM closer to HER.
  • Mahmood, B. M., & Dabdawb, M. M. (2020). The Pandemic COVID-19 Infection Spreading Spatial Aspects: A Network-Based Software Approach. AL-Rafidain Journal of Computer Sciences and Mathematics, 14(1), 159-170.
  • Manson, S., An, L., Clarke, K. C., Heppenstall, A., Koch, J., Krzyzanowski, B., ... & Tesfatsion, L. (2020). Methodological issues of spatial agent-based models. JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 23(1).
  • Mao, C., Yu, X., Zhou, Q., Harms, R., & Fang, G. (2020). Knowledge growth in university-industry innovation networks–Results from a simulation study. Technological forecasting and social change, 151, 119746.
  • Maqbool, A., Afzal, F., & Razia, A. (2020). Disaster Mitigation in Urban Pakistan Using Agent Based Modeling with GIS. ISPRS International Journal of Geo-Information, 9(4), 203.
  • Maradan, M. (2020). Uncertainty in deliberate lexical interventions: Exploring Esperanto speakers’ opinions through corpora.
  • Marconi, L., & Cecconi, F. (2020). Opinion dynamics and consensus formation in a Deffuant model with extremists and moderates. arXiv preprint arXiv:2010.01534.
  • Mardani, S., Rahman, A., & Nafissi, N. (2020). An Agent-based Modeling for Breast Tissue Simulation and the Growth and Spread of Tumor in Various Breast Cancer States. Journal of Health and Biomedical Informatics, 6(4), 272-287.
  • Marilisa, C., Chiara, L., Mercatali, L., Ibrahim, T., & Emanuele, G. (2020). An in-silico study of cancer cell survival and spatial distribution within a 3D microenvironment. Scientific Reports (Nature Publisher Group), 10(1).
  • Marshall, S. (2020). Modelling the impact of alternative educational qualifications on the New Zealand higher education system.
  • Martin Bicher, S., Rippinger, C., Urach, C., Brunmeir, D., Siebert, U., & Popper, N. (2020). Evaluation of Contact-Tracing Policies Against the Spread of SARS-CoV-2 in Austria–An Agent-Based Simulation.
  • Martin, Bain, Swanson, Horn & Wilensky (2020). Building Blocks: Designing scientific, domain-specific block-based modeling environments. ICLS, Nashville, TN.
  • Martin, K., & Sengupta, P. (2020). Multi-Agent Simulations of Intra-colony Violence in Ants. Proceedings of the International Conference of Complex Systems.
  • Martin, K., Horn, M., & Wilensky, U. (2020). Constructivist Dialogue Mapping Analysis of Ant Adaptation. Informatics in Education, 18(1).
  • Martins, A. C. (2020). Senescence, change, and competition: when the desire to pick one model harms our understanding. arXiv preprint arXiv:2011.04172.
  • Marvuglia, A., Koppelaar, R., & Rugani, B. (2020). The effect of green roofs on the reduction of mortality due to heatwaves: Results from the application of a spatial microsimulation model to four European cities. Ecological Modelling, 109351.
  • Matsuda, M., Kondo, T., Kawai, W., Hamanaka, J., Matsushita, N., Chino, S., ... & Kimura, F. (2020). E-Catalogues of Equipment for Constructing an Injection Molding Digital Eco-Factory. In EcoDesign and Sustainability I (pp. 501-516). Springer, Singapore.
  • Matsumoto, Y. (2020). Effects of mucus trail following on the distance between individuals of opposite sex and its influence on the evolution of the trait in the Ezo abalone Haliotis discus hannai. PeerJ, 8, e8710.
  • Mazar, M., Bettayeb, B., Klement, N., & LOUIS, A. (2020). Dynamic scheduling of robotic mildew treatmentby UV-c in horticulture.
  • McAlpine, A., Kiss, L., Zimmerman, C., & Chalabi, Z. (2020). Agent-based modeling for migration and modern slavery research: a systematic review. Journal of Computational Social Science, 1-90.
  • McDonald, W. (2020). Design and Implementation of an Agent-Based Model of Pertussis with Performance Considerations (Doctoral dissertation, University of Saskatchewan).
  • McGill, E., Petticrew, M., Marks, D., McGrath, M., Rinaldi, C., & Egan, M. (2020). Applying a complex systems perspective to alcohol consumption and the prevention of alcohol‐related harms in the 21st century: a scoping review. Addiction.
  • McGill, M. M., & Decker, A. (2020, June). Tools, Languages, and Environments Used in Primary and Secondary Computing Education. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 103-109).
  • McKay, V. R., Cambey, C. L., Combs, T. B., Stubbs, A. W., Pichon, L. C., & Gaur, A. H. (2020). Using a Modeling-Based Approach to Assess and Optimize HIV Linkage to Care Services. AIDS and Behavior, 1-11.
  • McLean, A., McDonald, W., & Goodridge, D. (2020). Simulation Modeling as a Novel and Promising Strategy for Improving Success Rates With Research Funding Applications: A Constructive Thought Experiment. JMIR Nursing, 3(1), e18983.
  • McMullen, P. R. (2020). An Agent-Based Approach to the Newsvendor Problem with Price-Dependent Demand. American Journal of Operations Research, 10(4), 101-110.
  • McMullen, P. R. (2020). Social Distancing via Coulomb’s Law. Applied Mathematics, 11(07), 532.
  • Meles, T., & Ryan, L. (2020). Adoption of Renewable Home Heating Systems: An Agent-Based Model of Heat Pump Systems in Ireland.
  • Mellacher, P., & Scheuer, T. (2020). Wage Inequality, Labor Market Polarization and Skill-Biased Technological Change: An Evolutionary (Agent-Based) Approach. Computational Economics, 1-46.
  • Menczer, F., Fortunato, S., & Davis, C. A. (2020). A First Course in Network Science. Cambridge University Press.
  • Meskini, F. Z., & Aboulaich, R. (2020, June). A New Cooperative Insurance Based On Blockchain Technology: Six Simulations To Evaluate The Model. In 2020 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-7). IEEE.
  • Miao, H., Hashemi-Beni, L., Mulrooney, T., Kurkalova, L. A., Liang, C. L., Jha, M., & Monty, G. (2020). SPATIAL DIFFERENCES IN FRESH VEGETABLE SPENDING: A CASE STUDY IN GUILFORD COUNTY, NORTH CAROLINA. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 44, 73-77.
  • Mierlo, S. V., Vangheluwe, H., Breslav, S., Goldstein, R., & Khan, A. (2020). Extending Explicitly Modelled Simulation Debugging Environments with Dynamic Structure. ACM Transactions on Modeling and Computer Simulation (TOMACS), 30(1), 1-25.
  • Milne, R. J., Cotfas, L. A., Delcea, C., Crăciun, L., & Molănescu, A. G. (2020). Adapting the reverse pyramid airplane boarding method for social distancing in times of COVID-19. Plos one, 15(11), e0242131.
  • Milne, R. J., Cotfas, L. A., Delcea, C., Salari, M., Crăciun, L., & Molănescu, A. G. (2020). Airplane Boarding Method for Passenger Groups When Using Apron Buses. IEEE Access, 8, 18019-18035.
  • Milne, R. J., Delcea, C., & Cotfas, L. A. (2020). Airplane Boarding Methods that Reduce Risk from COVID-19. Safety Science, 105061.
  • Mintram, K. S., Maynard, S. K., Brown, A. R., Boyd, R., Johnston, A. S. A., Sibly, R. M., ... & Tyler, C. R. (2020). Applying a Mechanistic Model to Predict Interacting Effects of Chemical Exposure and Food Availability on Fish Populations. Aquatic Toxicology, 105483.
  • Misra, A. (2020). Entrepreneurship in the Rural Labour Market: An Agent-Based Modelling Approach (Doctoral dissertation, University of Essex).
  • Modu, B., Polovina, N., & Konur, S. (2020). Agent-Based Modelling of Malaria Transmission Dynamics. arXiv preprint arXiv:2004.06477.
  • Montes de Oca, E. S., Suppi, R., De Giusti, L. C., & Naiouf, M. (2020). Green High Performance Simulation for AMB models of Aedes aegypti. Journal of Computer Science & Technology, 20.
  • Moradi, S., & Nejat, A. (2020). RecovUS: An Agent-Based Model of Post-Disaster Household Recovery. Journal of Artificial Societies and Social Simulation, 23(4), 1-13.
  • Mozahem, N. A. (2020). Social cognitive theory and women’s career choices: an agent—based model simulation. Computational and Mathematical Organization Theory, 1-26.
  • Murphy, K. J., Ciuti, S., & Kane, A. (2020). An introduction to agent‐based models as an accessible surrogate to field‐based research and teaching. Ecology and Evolution.
  • Muschett, G., & Morales, N. S. (2020). Using Ecological Modelling to Assess the Long-Term Survival of the West-Indian Manatee (Trichechus manatus) in the Panama Canal. Water, 12(5), 1275.
  • Nagasawa, R., Mas, E., Moya, L., & Koshimura, S. (2020). Multi-UAV path planning methodology for postdisaster building damage surveying.
  • Nakagawa, M., Bahr, K., & Lo-Iacono-Ferreira, V. (2020). Human Aspects of Project Management: Agent-Based Modeling. In Project Management and Engineering Research (pp. 117-129). Springer, Cham.
  • Naredo, E., Ryan, C., Guevara, I., Margaria, T., Urbano, P., & Trujillo, L. (2020, July). General controllers evolved through grammatical evolution with a divergent search. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 243-244).
  • Nathan, M. J., & Swart, M. I. (2020). Materialist epistemology lends design wings: educational design as an embodied process. Educational Technology Research and Development, 1-30.
  • Naufel, L. R. M. (2020). Complex Systems Approach for Simulation & Analysis of Socio-Technical Infrastructure Systems: An Empirical Demonstration (Doctoral dissertation, Arizona State University).
  • Navarro-Meneses, F. J. (2020). Agile and Value Creation in Agent-Based Social Simulation. Journal of Creating Value, 2394964320961903.
  • Nery, T. H. D. O. (2020). Segregações no curso de licenciatura em Matemática na perspectiva da segregação de Schelling.[HTML]
  • Nguyen, M. L. K. N., Megiddo, D. I., & Howick, P. S. (2020). Hybrid Simulation for Modelling Healthcare-Associated Infections: Promising but Challenging. Clinical Infectious Diseases.
  • Nguyen, T. H., & Jung, J. J. (2020). Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles. Neural Computing and Applications, 1-10.
  • Niazi, M. A., Iantovics, L. B., & Temkin, A. (2020). Review of “The Model Thinker” by Scott Page.
  • Nishi, T., Matsuda, M., Hasegawa, M., Alizadeh, R., Liu, Z., & Terunuma, T. (2020). Automatic Construction of Virtual Supply Chain as Multi-Agent System Using Enterprise E-Catalogues. International Journal of Automation Technology, 14(5), 713-722.
  • Noah, M. S. A. (2020). BUILDING INTELLIGENT AUTONOMOUS AGENTS AND MULTI AGENTS USING THE FETCH. AI DECENTRALISED OPEN ECONOMIC FRAMEWORK.
  • Noemi, G., Samanta, R., Federica, V., Alessandra, B., & Gabriella, B. (2020). Agent-Based Modeling and Simulation of Care Delivery for Patients With Thrombotic and Bleeding Disorders. Studies in health technology and informatics, 270, 1193-1194.
  • Norouziasl, S., & Jafari, A. (2020, November). Comparing Office Layouts Regarding Lighting Energy Saving Potentials Using Agent-Based Real-Time Simulation of Occupancy Behavioral Patterns. In Construction Research Congress 2020: Computer Applications (pp. 972-981). Reston, VA: American Society of Civil Engineers.
  • Norouziasl, S., Jafari, A., & Wang, C. (2020). An agent-based simulation of occupancy schedule in office buildings. Building and Environment, 107352.
  • Nwokoye, C. H., Umeugoji, C., & Umeh, I. (2020). Evaluating Degrees of Differential Infections on Sensor Networks’ Features Using the SEjIjR-V Epidemic Model. Egyptian Computer Science Journal, 44(3).
  • O'Neill, J. (2020). The Ability to Unite Under Crisis: Ethnic Group Consolidation During Ethnic Conflict in Latin America (Doctoral dissertation, The Ohio State University).
  • O'Shea, T., Bates, P., & Neal, J. (2020). Testing the impact of direct and indirect flood warnings on population behaviour using an agent-based model. Natural Hazards and Earth System Sciences, 20(8), 2281-2305.
  • Oh, H., Trinh, M. P., Vang, C., & Becerra, D. (2020). Addressing Barriers to Primary Care Access for Latinos in the US: An Agent-Based Model. Journal of the Society for Social Work and Research, 11(2), 000-000.
  • Olenick, J. D. (2020). Still Learning: Introducing the Learning Transfer Model, a Formal Model of Transfer (Doctoral dissertation, Michigan State University).
  • Oliveira, J. D., de Borba Campos, M., & Paixão-Cortes, V. S. M. (2020, July). Usable and Accessible Robot Programming System for People Who Are Visually Impaired. In International Conference on Human-Computer Interaction (pp. 445-464). Springer, Cham.
  • Ornelas, N. O. 2020. An Ecosystem: Computational Thinking, Project-Based Learning & Logo.
  • Orsi, F., Scuttari, A., & Marcher, A. (2020). How much traffic is too much? Finding the right vehicle quota for a scenic mountain road in the Italian Alps. Case Studies on Transport Policy, 8, 1270-1284. https://doi.org/10.1016/j.cstp.2020.08.007
  • Oruro, E. M., Pardo, G. V., Lucion, A. B., Calcagnotto, M. E., & Idiart, M. A. (2020). Maturation of pyramidal cells in anterior piriform cortex may be sufficient to explain the end of early olfactory learning in rats. Learning & Memory, 27(12), 20-32.
  • Oruro, E. M., Pardo, G. V., Lucion, A. B., Calcagnotto, M. E., & Idiart, M. A. (2020). The maturational characteristics of the GABA input in the anterior piriform cortex may also contribute to the rapid learning of the maternal odor during the sensitive period. Learning & Memory, 27(12), 493-502.
  • Osterritter, L. J., & Carley, K. M. (2020, October). Modeling Interventions for Insider Threat. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 55-64). Springer, Cham.
  • Pal, C. V., Leon, F., Paprzycki, M., & Ganzha, M. (2020). A Review of Platforms for the Development of Agent Systems. arXiv preprint arXiv:2007.08961.
  • Pala, D., Annovazzi-Lodi, L., Bellazzi, R., Fiscante, N., Franzini, M., Larizza, C., ... & Casella, V. (2020). THE KEY ROLE OF GEOGRAPHIC INFORMATION IN EXPOSOMICS: THE EXAMPLE OF THE H2020 PULSE PROJECT. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 283-289.
  • Palau, A. S., Liang, Z., Lütgehetmann, D., & Parlikad, A. K. (2020). Collaborative Prognostics in Social Asset Networks. In Value Based and Intelligent Asset Management (pp. 329-349). Springer, Cham.
  • Panorkou, N., & Germia, E. (2020). Examining Students’ Quantitative Reasoning in a Virtual Ecosystem Simulation of the Water Cycle.
  • Panov, S. (2020). To Derogate (and Notify), or Not to Derogate (and Not to Notify), that is the Question!: An Analysis of the Legal Framework of the COVID-19 State of Emergency in the Republic of Bulgaria and ECHR Practice.
  • Papastamatiou, Y. P., Bodey, T. W., Caselle, J. E., Bradley, D., Freeman, R., Friedlander, A. M., & Jacoby, D. M. (2020). Multiyear social stability and social information use in reef sharks with diel fission–fusion dynamics. Proceedings of the Royal Society B, 287(1932), 20201063.
  • Papparlardo, F., Russo, G., Pennisi, M., Palumbo, G. A. P., Sgroi, G., Motta, S., & Maimone, D. (2020). The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis.
  • Park, H. (2020). Evolutionary Ecological Model of Defense Activation Disorders Via the Marginal Value Theorem. Psychiatry Investigation.
  • Park, S. H. (2020). Does capitalism need a government to be nice: Robert Axelrod and his iterated prisoner's dilemma computer tournament [Original paper]. Frontiers in Education Technology, 3(2).
  • Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. K. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention, 136, 105405.
  • Parthiban, M., & Ram Kumar, P. (2020). Applications of discrete event simulation: A literature review.
  • Pathak, A., Mohan, V. M., & Banerjee, A. (2020). An agent based modelling approach to study lockdown efficacy for infectious disease spreads. medRxiv.
  • Paudel, R. (2020). A Systems Approach to Analyze Household Vulnerability to Food Insecurity in Rural Southern Mali Using a Spatially-Explicit Integrated Social and Biophysical Model (Doctoral dissertation, Michigan State University).
  • Paul, R., Eaton, S. E., Laird, G., Nelson, N., & Brennan, R. (2020). USING AGENT-BASED MODELLING FOR EER EXPERIMENTAL DESIGN: PRELIMINARY VALIDATION BASED ON STUDENT CHEATING BEHAVIOURS. Proceedings of the Canadian Engineering Education Association (CEEA).
  • Pawlowski, T., & van Dinther, C. (2020). Assessing the Impact of Electric Vehicle Charging Behavior on the Distribution Grid.
  • Payette, N. (2020). Collaborating Like Professionals: Integrating NetLogo and GitHub. In Advances in Social Simulation (pp. 343-348). Springer, Cham.
  • Pea, R., Grover, S., & Ventures, L. G. (2020). Weaving the Fabric of Adaptive STEM Learning Environments Across Domains and Settings. The Interdisciplinarity of the Learning Sciences.
  • Peel, A., Dabholkar, S., Anton, G., Wu, S., Wilensky, U., & Horn, M. (2020). A Case Study of Teacher Professional Growth Through Co-design and Implementation of Computationally Enriched Biology Units.
  • Petrasova, A., Gaydos, D. A., Petras, V., Jones, C. M., Mitasova, H., & Meentemeyer, R. K. (2020). Geospatial simulation steering for adaptive management. Environmental Modelling & Software, 104801.
  • Phetheet, J. (2020). Simulating and Analyzing Use of Water and Renewable Energy in Agricultural Areas Using FEWCalc and DSSAT (Doctoral dissertation, University of Kansas).
  • Phetheet, J., Hill, M. C., Barron, R. W., Rossi, M. W., Amanor-Boadu, V., Wu, H., & Kisekka, I. (2020). Consequences of climate change on food-energy-water systems in arid regions without agricultural adaptation, analyzed using FEWCalc and DSSAT. Resources, Conservation and Recycling, 105309.
  • Philips, I. (2020). An Agent Based Model to Estimate Lynx Dispersal if Re-Introduced to Scotland. Applied Spatial Analysis and Policy, 13(1), 161-185.
  • Phuttharak, J., & W Loke, S. (2020). Iterative Spatial Crowdsourcing in Peer-to-Peer Opportunistic Networks. Electronics, 9(7), 1085.
  • Platas-López, A., Guerra-Hernández, A., Cruz-Ramírez, N., Quiroz-Castellanos, M., Grimaldo, F., Paolucci, M., & Cecconi, F. (2020). Towards an Agent-Based Model for the Analysis of Macroeconomic Signals. In Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications (pp. 551-565). Springer, Cham.
  • Pobuda, P. (2020). The Digital Twin of the Economy: Proposed Tool for Policy Design and Evaluation. Real-World Economics Review, issue no. 94, pp. 140-148.
  • Premo, L. S. (2020). Population Size Limits the Coefficient of Variation in Continuous Traits Affected by Proportional Copying Error (and Why This Matters for Studying Cultural Transmission). Journal of Archaeological Method and Theory, 1-23.
  • Proctor, C. C. (2020). Supporting Critical Computational Literacies Through Interactive Storytelling (Doctoral dissertation, Stanford University).
  • Pulley, M., Rodriguez, L., Lewis, M., Kohler, B., & Gordillo, L. (2020). Guiding Students to Understand Functional Responses: Holling's Disc Experiment Revisited. PRIMUS, 1-20.
  • Pumain, D. (Ed.). (2020). Geographical Modelling: Cities and Territories. John Wiley & Sons.
  • Punzalan, A. (2020). Predicting Condor Range Expansion in California to Reduce Development Threats. 2020-CSU Theses and Dissertations.
  • Putriani, D., Ghani, G. M., & Kartiwi, M. (2020). EXPLORATION OF AGENT-BASED SIMULATION: THE MULTIPLIER EFFECT OF ZAKAH ON ECONOMIC GROWTH1. Journal of Islamic Monetary Economics and Finance, 6(3), 641-666.
  • Qian, C., Yu, K., & Gu, H. (2020). Flexibility mechanisms in a dynamic distribution network. Journal of Business & Industrial Marketing.
  • Qin, M., Chen, L., Jing, N., & Chen, Q. (2020, July). Simulation Research on the Formation Behavior of a Compound College Students’ Entrepreneurship Team Based on NetLogo. In 2020 International Conference on Advanced Education, Management and Social Science (AEMSS2020) (pp. 236-239). Atlantis Press.
  • Raczynski, S. (2020). Prey-Predator Models Revisited: Uncertainty, Herd Instinct, Fear, Limited Food, Epidemics, Evolution, and Competition. In Interacting Complexities of Herds and Social Organizations (pp. 107-132). Springer, Singapore.
  • Radzvilas, M., De Pretis, F., Peden, W., Tortoli, D., & Osimani, B. (2020). Double blind vs. open review: an evolutionary game logit-simulating the behavior of authors and reviewers. arXiv preprint arXiv:2011.07797.
  • Rahman, A., Naufal, F., & Partiwi, S. G. (2020, June). Measuring the entropy of organizational culture using agent-based simulation. In Managing Learning Organization in Industry 4.0: Proceedings of the International Seminar and Conference on Learning Organization (ISCLO 2019), Bandung, Indonesia, October 9-10, 2019 (p. 109). Routledge.
  • Rahmoeller, M., & Steinweg, J. M. (2020). Implementation of a New Quantitative Biology Course: Assessment of Students’ Abilities and Confidence. PRIMUS, 1-35.
  • Railsback, S. F., Harvey, B. C., & Ayllón, D. (2020). Contingent trade-off decisions with feedbacks in cyclical environments: testing alternative theories. Behavioral Ecology.
  • Railsback, S. F., Harvey, B. C., & Ayllón, D. (2020). Importance of the Daily Light Cycle in Population‐Habitat Relations: A Simulation Study. Transactions of the American Fisheries Society.
  • Raimbault, J. (2020). An agent-based model of interdisciplinary interactions in science. arXiv preprint arXiv:2006.16399.
  • Rajabi, A., Talebzadehhosseini, S., & Garibay, I. (2020). Resistance of communities against disinformation. arXiv preprint arXiv:2004.00379.
  • Ralph, M. (2020). Emergent patterns in deterministic modelling. International Journal of Mathematical Education in Science and Technology, 1-11.
  • Razakatiana, M., Kolski, C., Mandiau, R., & Mahatody, T. (2020, June). Human-agent Interaction based on Game Theory: Case of a road traffic supervision task. In 2020 13th International Conference on Human System Interaction (HSI) (pp. 88-93). IEEE.
  • Razakatiana, M., Kolski, C., Mandiau, R., & Mahatody, T. (2020, November). Game Theory-based Human-Assistant Agent Interaction Model: Feasibility Study for a Complex Task. In Proceedings of the 8th International Conference on Human-Agent Interaction (pp. 187-195).
  • Recio, M. R., Singer, A., Wabakken, P., & Sand, H. (2020). Agent-based models predict patterns and identify constraints of large carnivore recolonizations, a case study of wolves in Scandinavia. Biological Conservation, 251, 108752.
  • Reddy, A. S., Subha, T. D., Suresh, T., & Subash, T. D. (2020). A DTM Research based on the strategic process. Materials Today: Proceedings.
  • Remiszewski, K. (2020). NUTRIENT CYCLING ALONG MICROBIAL AND LITHOLOGIC GRADIENTS AND FOSTERING STUDENT SELF-CONFIDENCE IN SCIENCE.
  • Reymond, D. (2020). Patents information for humanities research: could there be something?. Iberoamerican Journal of Science Measurement and Communication, 1(1).
  • Ribas-Xirgo, L. (2020, November). Multi-agent System Model of Taxi Fleets. In Workshop of Physical Agents (pp. 123-134). Springer, Cham.
  • Riedel, L., Herdeanu, B., Mack, H., Sevinchan, Y., & Weninger, J. (2020). Utopia: a comprehensive and collaborative modeling framework for complex and evolving systems. Journal of Open Source Software, 5(53), 2165.
  • Rizana, A. F., & Ramadhan, F. (2020). Penerapan Agent-Based Simulation dalam Memprediksi Penggunaan Berkelanjutan Sistem ERP. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 7(2).
  • Roach, A., Scott, I., Macfarlane, G., Jones, G. T., & Macgregor, A. (2020). OP0284 AN AGENT-BASED SIMULATION OF THE EFFECTS OF VARYING TIME TO TREATMENT WITH BIOLOGICAL AGENTS ON PATIENT HEALTH AND COST IN AXIAL SPONDYLOARTHRITIS USING NATIONAL REGISTER DATA.
  • Robertson, J. J., Swannack, T. M., McGarrity, M., & Schwalb, A. N. (2020). Zebra mussel invasion of Texas lakes: estimating dispersal potential via boats. Biological Invasions, 1-31.
  • Robeva, R. S., Jungck, J. R., & Gross, L. J. (2020). Changing the nature of quantitative biology education: data science as a driver. Bulletin of Mathematical Biology, 82(10), 1-30.
  • Rodriguez Recio, M., Singer, A., Wabakken, P., & Sand, H. (2020). Agent-based models predict patterns and identify constraints of large carnivore recolonizations, a case study of wolves in Scandinavia.
  • Rosenbusch, H., Röttger, J., & Rosenbusch, D. (2020). Would Chuck Norris certainly win the Hunger Games?: Simulating the result reliability of Battle Royale games through agent-based models. Simulation and Gaming.
  • Ruiz-Martin, C., Wainer, G., & Lopez-Paredes, A. (2020). Discrete-Event Modeling and Simulation of Diffusion Processes in Multiplex Networks. ACM Transactions on Modeling and Computer Simulation (TOMACS), 31(1), 1-32.
  • Ruscheinski, A., Wilsdorf, P., Zimmermann, J., van Rienen, U., & Uhrmacher, A. M. (2020). An Artifact-based Workflow for Finite-Element Simulation Studies. arXiv preprint arXiv:2010.07625.
  • Russo, G., Reche, P., Pennisi, M., & Pappalardo, F. (2020). The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opinion on Drug Discovery, 1-15.
  • Ryder, E., Ruiz, C., Weaver, S., & Gegear, R. (2020). Choosing Your Own Adventure: Engaging the New Learning Society through Integrative Curriculum Design. EPiC Series in Education Science, 3, 188-199.
  • Sabzian, H., Shafia, M. A., Ghazanfari, M., & Bonyadi Naeini, A. (2020). Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory. Sustainability, 12(7), 2904.
  • Sadler, T. D., Friedrichsen, P., Zangori, L., & Ke, L. (2020). Technology-Supported Professional Development for Collaborative Design of COVID-19 Instructional Materials. Journal of Technology and Teacher Education, 28(2), 171-177.
  • Saito, M., & Hayashi, H. (2020). P2P Human-Resource Sharing and Its Redistribution Strategy of Stable Coin.
  • Sakashita, T., Watanabe, S., Hanaoka, H., Ohshima, Y., Ikoma, Y., Ukon, N., ... & Ishioka, N. S. (2020). Absorbed dose simulation of meta-211 At-astato-benzylguanidine using pharmacokinetics of 131 I-MIBG and a novel dose conversion method, RAP. Annals of Nuclear Medicine, 1-11.
  • Sánchez-Cartas, J. M. (2020, June). Platform competition and consumer’s decisions: An ABM simulation of pricing with different behavioral rules.
  • Santoro, M., Mazzetti, P., & Nativi, S. (2020). The VLab Framework: An Orchestrator Component to Support Data to Knowledge Transition. Remote Sensing, 12(11), 1795.
  • Santos, F., Nunes, I., & Bazzan, A. L. (2020). Quantitatively Assessing the Benefits of Model-driven Development in Agent-based Modeling and Simulation. Simulation Modelling Practice and Theory, 102126.
  • Santos, M., Cajaiba, R., Gonzalez, D., Leote, P., Ferreira, D., Bastos, R., ... & Cabral, J. A. (2020). How accurate are estimates of flower visitation rates by pollinators? Lessons from a spatially explicit agent-based model. Ecological Informatics, 101077.
  • Saoud, M. S., Boubetra, A., & Attia, S. (2020). A Simulation Knowledge Extraction-Based Decision Support System for the Healthcare Emergency. Hospital Management and Emergency Medicine: Breakthroughs in Research and Practice: Breakthroughs in Research and Practice, 192.
  • Sapienza, A., & Falcone, R. (2020). Evaluating agents’ trustworthiness within virtual societies in case of no direct experience. Cognitive Systems Research.
  • Sarmah, D. T., Bairagi, N., & Chatterjee, S. (2020). Tracing the footsteps of autophagy in computational biology. Briefings in Bioinformatics.
  • Sauter, J. A., Bixler, K., Kitchen, S., & Chase, R. (2020, April). RF emitter localization with robotic swarms. In Unmanned Systems Technology XXII (Vol. 11425, p. 114250D). International Society for Optics and Photonics.
  • Savaglio, C., Ganzha, M., Paprzycki, M., Bădică, C., Ivanović, M., & Fortino, G. (2020). Agent-based Internet of Things: State-of-the-art and research challenges. Future Generation Computer Systems, 102, 1038-1053.
  • Saxena, N. (2020). Working'Failure'into your Learning Design. The Emerging Learning Design Journal, 7(1), 2
  • Scataglini, S., & Perez Luque, E. (2020). Closing the Gender Gap in DHM. In 6th International Digital Human Modeling Symposium, August 31–September 2, 2020, Skövde, Sweden (Vol. 11, pp. 408-418). IOS Press.
  • Schaff, F. (2020). Conceptualising Artificial Anasazi with an Explicit Knowledge Representation and Population Model. In Advances in Social Simulation (pp. 399-403). Springer, Cham.
  • Schlaile, M. P. (2020). A Case for Economemetics? Why Evolutionary Economists Should Re-evaluate the (F) utility of Memetics. In Memetics and Evolutionary Economics (pp. 33-68). Springer, Cham.
  • Schleicher, J. (2020). Introduction to In Silico Modeling to Study ROS Dynamics. In Reactive Oxygen Species (pp. 1-32). Humana, New York, NY.
  • Schloesser, D. S., Hollenbeck, D., & Kello, C. T. (2020). Social Foraging in Groups of Search Agents with Human Intervention.
  • Schlüter, J., Bossert, A., Rössy, P., & Kersting, M. (2020). Impact assessment of autonomous demand responsive transport as a link between urban and rural areas. Research in Transportation Business & Management, 100613.
  • Schmolke, A., Abi‐Akar, F., Roy, C., Galic, N., & Hinarejos, S. (2020). Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model. Part I: Model Validation. Environmental Toxicology and Chemistry.
  • Schwarz, M., Auzepy, Q., & Knoeri, C. (2020). Can electricity pricing leverage electric vehicles and battery storage to integrate high shares of solar photovoltaics?. Applied Energy, 277, 115548.
  • Schwarz, N., Dressler, G., Frank, K., Jager, W., Janssen, M., Müller, B., ... & Groeneveld, J. (2020). Formalising theories of human decision-making for agent-based modelling of social-ecological systems: practical lessons learned and ways forward. Socio-Environmental Systems Modelling, 2, 16340-16340.
  • Sedigh, A. H. A., Purvis, M. K., Savarimuthu, B. T. R., Frantz, C. K., & Purvis, M. A. (2020). Impact of different belief facets on agents' decision--a refined cognitive architecture. arXiv preprint arXiv:2004.11858.
  • Sedigh, A. H. A., Purvis, M. K., Savarimuthu, B. T. R., Purvis, M. A., & Frantz, C. K. (2020). Impact of meta-roles on the evolution of organisational institutions. arXiv preprint arXiv:2008.04096.
  • Sellers, M. W., Sayama, H., & Pape, A. D. (2020). Simulating Systems Thinking under Bounded Rationality. Complexity, 2020.
  • Sells, S. N., & Mitchell, M. S. (2020). The economics of territory selection. Ecological Modelling, 438, 109329.
  • Sengupta, A., & Sena, V. (2020). Impact of Open Innovation on Industries and Firms–A Dynamic Complex Systems View. Technological Forecasting and Social Change.
  • Sfa, F. E., Nemiche, M., & Rayd, H. (2020). A generic macroscopic cellular automata model for land use change: The case of the Drâa valley. Ecological Complexity, 43, 100851.
  • Shaaban, M. (2020). The roadmap to energy security in Egypt. In M. Brzoska & J. Scheffran (Eds.), Climate change, security risks, and violent conflicts: Essays from integrated climate research in Hamburg (pp. 83-102). Hamburg University Press.
  • Shafiq, A. (2020). Contemporary debates in Islamic monetary economics. Islamic Monetary Economics: Finance and Banking in Contemporary Muslim Economies, 215.
  • Shanaa, M., & Abdallah, S. (2020, November). Agent-based simulation for COVID-19 outbreak within a semi-closed environment. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) (pp. 231-236). IEEE.
  • Shapiro, R. B. New and Future Coding Paradigms: Interview with R. Benjamin Shapiro. In Holbert, N., Berland, M., & Kafai, Y. B. (eds.), Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs, 369.
  • Shareff, R. (2020). Agricultural Contexts as a Platform for Science and Technology: A Cross-Cultural Examination of Classroom, Community, and Modeling Dynamics (Doctoral dissertation, UC Berkeley).
  • Sharma, A., Tale, E., Hernandez, M., & Phuong, V. (2020). Engaging students with computing and climate change through a course in Scientific Computing. Journal of STEM Education: Innovations and Research, 20(2).
  • Sharma, D., Khandekar, N., & Sachdeva, K. (2020). Exploratory agent-based model to understand migration scenarios: a study from the Indian Himalayan Region, Uttarakhand. Development in Practice, 1-12.
  • Shi, L., Zhang, L., & Lu, Y. (2020). Evaluating social network-based weight loss interventions in Chinese population: An agent-based simulation. PloS one, 15(8), e0236716.
  • Shi, X., Sun, Z., & Zhu, T. (2020). Multi-Agent Traffic Simulation Considering Heterogeneous Driving Behaviors and Collision. In CICTP 2020 (pp. 4646-4659).
  • Shiflet, A. B., Shiflet, G. W., Cannataro, M., Guzzi, P. H., Zucco, C., & Kaplun, D. A. (2020). What Are the Chances?—Hidden Markov Models. In An Introduction to Undergraduate Research in Computational and Mathematical Biology (pp. 353-400). Birkhäuser, Cham.
  • Shinde S.B., Kurhekar M.P. (2020) Agent-Based Modeling of the Adaptive Immune System Using Netlogo Simulation Tool. In: Das K., Bansal J., Deep K., Nagar A., Pathipooranam P., Naidu R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore
  • Shoukat, A., & Moghadas, S. M. (2020). Agent-Based Modelling: An Overview with Application to Disease Dynamics. arXiv preprint arXiv:2007.04192.
  • Shrinidhi, K. R., Sneha, V., Jain, V., & Nair, M. K. (2020). Multi-agent-Based Systems in Machine Learning and Its Practical Case Studies. In Machine Learning for Intelligent Decision Science (pp. 153-189). Springer, Singapore.
  • Sibbel, D. M. (2020). Agent-Based Modelling: How climate policies influence population dynamics (Master's thesis).
  • Sidiropoulos, G., Kiourt, C., & Moussiades, L. (2020). Crowd simulation for crisis management: the outcomes of the last decade. arXiv preprint arXiv:2006.01216.
  • Sikk, K., & Caruso, G. (2020). A spatially explicit agent-based model of central place foraging theory and its explanatory power for hunter-gatherers settlement patterns formation processes. Adaptive Behavior, 1059712320922915.
  • Silva J., Varela N., Lezama O.B.P. (2020) Optimizing Street Mobility Through a NetLogo Simulation Environment. In: Smys S., Tavares J., Balas V., Iliyasu A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham
  • Silva, A., & Oliveira, M. (2020, January). Simulando o Jogo de Negociação Pit Game em um Sistema Multi-Agentes Implementado com o Framework JaCaMo. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional (pp. 938-948). SBC.
  • Silva, T., Araújo, M., Junior, R., Costa, L., Andrade, J., & Campos, G. (2020, October). Classifying Organizational Structures on Targets in the Cooperative Target Observation. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional (pp. 718-729). SBC.
  • Silva, W. T., Harding, K. C., Marques, G. M., Bäcklin, B. M., Sonne, C., Dietz, R., ... & Desforges, J. P. (2020). Life cycle bioenergetics of the gray seal (Halichoerus grypus) in the Baltic Sea: Population response to environmental stress. Environment International, 145, 106145.
  • Singh, R. K., Sardar, M., & Das, D. (2020). Assembling Multi-Robots Along a Boundary of a Region with Obstacles—A Performance Upgradation. In Advances in Computational Intelligence (pp. 201-212). Springer, Singapore.
  • Singley, A., & Callender Highlander, H. (2020). A Mathematical Model for the Effect of Social Distancing on the Spread of COVID-19. Spora: A Journal of Biomathematics, 6(1), 40-51.
  • Sistrunk, A., Cedeno, V., & Biswas, S. (2020). On synthetic data generation for anomaly detection in complex social networks. arXiv preprint arXiv:2010.13026.
  • Smaldino, P. (2020). How to translate a verbal theory into a formal model.
  • Smaldino, P., & O'Connor, C. (2020). Interdisciplinarity Can Aid the Spread of Better Methods Between Scientific Communities.
  • Sneider, T. (2020). UNETHICAL BEHAVIOR IN ORGANIZATIONS–AN AGENT-BASED APPROACH. In Economic and Social Development (Book of Proceedings), 58th International Scientific Conference on Economic and Social (p. 250).
  • Solomon, C., Harvey, B., Kahn, K., Lieberman, H., Miller, M. L., Minsky, M., Papert, A., & Silverman, B. (2020). History of Logo. Proceedings of the ACM on Programming Languages, 4(HOPL), 1–66. doi.org
  • Song, W., & Jablonski, P. G. (2020). Evolution of switchable aposematism: insights from individual-based simulations. PeerJ, 8, e8915.
  • Sopamena, P., Andriansyah, R., & Sopamena, K. (2020). Analysis of Understanding of Student Concepts in Solving Absolute Value Problems. Matematika dan Pembelajaran, 7(2), 42-50.
  • Sopha, B. M., Sakti, S., Prasetia, A. C. G., Dwiansarinopa, M. W., & Cullinane, K. (2020). Simulating long-term performance of regional distribution centers in archipelagic logistics systems. Maritime Economics & Logistics, 1-29.
  • Souza, J. K. G. D. (2020). Modelagem baseada em agentes: possibilidades na Educação Matemática e pesquisa ambiental. [a href=http://200.129.179.47/handle/11612/1773]
  • Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., ... & Gilbert, N. (2020). Computational models that matter during a global pandemic outbreak: A call to action. Journal of Artificial Societies and Social Simulation, 23(2).
  • Stevanovic, A., & Mitrovic, N. (2020). Impact of conflict resolution parameters on combined alternate-directions lane assignment and reservation-based intersection control. European Transport Research Review, 12(1), 1-10.
  • Stiner, S., & Chellamuthu, V. (2020). An Agent-Based Model of West Nile Virus: Predicting the Impact of Public Health Agents and Vaccinations on Horses. Curiosity: Interdisciplinary Journal of Research and Innovation, 44-66.
  • Stork, C. (2020). Exploring self-organisation for car-sharing systems: An agent-based approach.
  • Stroup, W. M., Ares, N., Hurford, A. C., & Lesh, R. (2020). Diversity-by-Design. Foundations for the Future in Mathematics Education, 367.
  • Student, J., Kramer, M. R., & Steinmann, P. (2020). Coasting: model description, global sensitivity analysis, and scenario discovery. MethodsX, 101145.
  • Student, J., Kramer, M. R., & Steinmann, P. (2020). Simulating emerging coastal tourism vulnerabilities: an agent-based modelling approach. Annals of Tourism Research, 85, 103034.
  • Su, T. Y. M. (2020). Internal Migration of Foreign-Born in US: Impacts of Population Concentration and Risk Aversion.
  • Suliman, T. (2020). Understanding the dynamics of even-aged stands of Brutia pine (Pinus brutia Ten.) in the coastal region of Syria based on a distance-independent individual-tree growth model.
  • Sulis, E., & Tambuscio, M. (2020, October). Simulation of misinformation spreading processes in social networks: an application with netlogo. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 614-618). IEEE.
  • Sulis, E., Terna, P., Di Leva, A., Boella, G., & Boccuzzi, A. (2020). Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare. Journal of Medical Systems, 44(9), 1-7.
  • Sullivan, G. B. (2020). Understanding Listeria Dynamics in Produce Operations Using Three Approaches: Sampling, Sequencing, and in silico Modeling (Doctoral dissertation, Cornell University).
  • Sun, T., Bu, F., Liu, X., & Fu, Y. (2020, December). Modeling and Simulation of Group Drug-related Incident Evolution. In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (pp. 1247-1252). IEEE.
  • Sundar, S., Battistoni, C., McNulty, R., Morales, F., Gorky, J., Foley, H., & Dhurjati, P. (2020). An agent-based model to investigate microbial initiation of Alzheimer’s via the olfactory system. Theoretical Biology and Medical Modelling, 17(1), 1-15.
  • Suneetha, C., Rao, S. S., & Ramesh, K. S. (2020). Ideal frequency rendezvousing for multiuser communication (IFRMC) over cognitive radio network. International Journal of Speech Technology, 1-11.
  • Surro, C. J. (2020). Computational Methods and Models in Macroeconomics (Doctoral dissertation, UCLA).
  • Susaki, K., & Kaneda, T. (2020). The Potential of Vision-Driven Agent Simulation: The VD-Walker. In Downtown Dynamics (pp. 187-205). Springer, Tokyo.
  • Swain, A., Devereux, M., & Fagan, W. F. (2020). Deciphering trophic interactions in a mid-Cambrian assemblage. bioRxiv.
  • Swanson, H., Martin, K. (*), Jones, B. (*), Hedayati, M. (*), Wu, S. (*), Sherin, B., Wilensky, U. (2020, April).Characterizing the nature of student theory building in the context of computational modeling activities. Paper accepted for presentation at the annual meeting of the American Education Research Association.
  • Swanson, Martin, Sherin & Wilensky (2020). Characterizing Student Theory Building in the Context of Block-Based Computational Modeling. ICLS, Nashville, TN.
  • Szczepanska, T., Priebe, M., & Schröder, T. (2020). Teaching the Complexity of Urban Systems with Participatory Social Simulation. In Advances in Social Simulation (pp. 427-439). Springer, Cham.
  • Tabasi, M., Alesheikh, A. A., Sofizadeh, A., Saeidian, B., Pradhan, B., & AlAmri, A. (2020). A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran. Parasites & Vectors, 13(1), 1-17.
  • Tabrett, A., & Way, A. M. (2020). A new Monte Carlo simulation tool for designing an archaeological landscape sampling strategy. MethodsX, 101124.
  • Tan, M., Hatef, E., Taghipour, D., Vyas, K., Kharrazi, H., Gottlieb, L., & Weiner, J. (2020). Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities. JMIR Medical Informatics, 8(9), e18084.
  • Tang, W., Grimm, V., Tesfatsion, L., Shook, E., Bennett, D., An, L., ... & Ye, X. (2020). Code Reusability and Transparency of Agent-Based Modeling: A Review from a Cyberinfrastructure Perspective. In High Performance Computing for Geospatial Applications (pp. 115-134). Springer, Cham.
  • Tang, Y., Liu, M., & Sun, Z. (2020). Indirect Effects of Grazing on Wind-Dispersed Elm Seeds in Sparse Woodlands of Northern China. Land, 9(12), 490.
  • Tang, Z., & Zhu, H. (2020). Nonlinear Dynamic Analysis of New Product Diffusion considering Consumer Heterogeneity. Complexity, 2020.
  • Tatar, D., Roschelle, J., & Hegedus, S. (2020). Democratizing Access to Advanced Mathematics (1992–Present). Historical Instructional Design Cases: ID Knowledge in Context and Practice, 283.
  • Tekdoğan, Ö. F., & Saraç, M. (2020). The problems with fractional reserve banking and proposing a shariah-compliant full reserve banking model. Islamic Monetary Economics: Finance and Banking in Contemporary Muslim Economies, 133.
  • Tether, V., Malleson, N., Steenbeek, W., & Birks, D. (2020). Using agent-based models to investigate the presence of edge effects around crime generators and attractors. Agent-Based Modelling for Criminological Theory Testing and Development, 45.
  • Tews, A. C. (2020). Predictive Ecological Modeling of Grey Wolf (Canis lupus) Movement using Agent-Based Modeling and GIS (Doctoral dissertation, University of Colorado Colorado Springs).
  • Thiel, D. (2020). A pricing-based location model for deploying a hydrogen fueling station network. International Journal of Hydrogen Energy.
  • Thiriot, S. (2020). Impact of the Interaction Network on the Dynamics of Word-of-Mouth with Information Seeking. arXiv preprint arXiv:2002.02728.
  • Thomas, Y., Razafimahefa, N. R., Ménesguen, A., & Bacher, C. (2020). Multi-scale interaction processes modulate the population response of a benthic species to global warming. Ecological Modelling, 436, 109295.
  • Thompson, J., Read, G.J.M., Wijnands, J.S., & Salmon, P.M. (2020). The perils of perfect performance; considering the effects of introducing autonomous vehicles on rates of car vs cyclist conflict. Ergonomics, 63(8), 981–996. https://doi.org/10.1080/00140139.2020.1739326
  • Thongsukdee, P., & Weerawat, W. (2020). Physician workforce planning and allocation model using agent‐based modeling: A case study in Thailand. The International Journal of Health Planning and Management.
  • Tolk, A., Dinh, K., Comer, K., & Scott, S. (2020, May). Exploratory analysis to address deep uncertainty: using calibratable system models for exploratory simulation of complex missions. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-11).
  • Tolk, A., Harper, A., & Mustafee, N. (2020). Hybrid Models as Transdisciplinary Research Enablers. European Journal of Operational Research.
  • Tomasiello, D. B., Giannotti, M., & Feitosa, F. F. (2020). ACCESS: An agent-based model to explore job accessibility inequalities. Computers, Environment and Urban Systems, 81, 101462.
  • Tosselli, L., Bogado, V., & Martínez, E. (2020). A repeated-negotiation game approach to distributed (re) scheduling of multiple projects using decoupled learning. Simulation Modelling Practice and Theory, 98, 101980.
  • Tran, M., Ngo, M., Pham-Hi, D., & Bui, M. (2020, October). Bayesian Calibration of Hyperparameters in Agent-Based Stock Market. In 2020 RIVF International Conference on Computing and Communication Technologies (RIVF) (pp. 1-6). IEEE.
  • Trinh, L. T., Sano, K., & Hatoyama, A. K. (2020). Modelling and Simulating Head-On Conflict-Solving Behaviour of Motorcycles under Heterogeneous Traffic Condition in Developing Countries. Transportmetrica A: Transport Science, 1-48.
  • Tsai, M. H., & Huang, S. R. (2020). Team Efficiency Estimation for Construction Process Considering the Collaborative Behaviors. Journal of Applied Science and Engineering, 23(1), 79г92.
  • Tseng, S. H., & Son Nguyen, T. (2020). Agent-Based Modeling of Rumor Propagation Using Expected Integrated Mean Squared Error Optimal Design. Applied System Innovation, 3(4), 48.
  • Tuesta, E. F., Bolaños-Pizarro, M., Neves, D. P., Fernández, G., & Axel-Berg, J. (2020). Complex networks for benchmarking in global universities rankings. Scientometrics, 1-21.
  • Tyszberowicz, S., & Faitelson, D. (2020). Emergence in cyber-physical systems: potential and risk. Frontiers of Information Technology & Electronic Engineering, 21(11), 1554-1566.
  • Uyar, T., & Özel, M. E. (2020). Agent-based modelling of interstellar contacts using rumour spread models. International Journal of Astrobiology, 1-7.
  • van Doormaal, N., Ruiter, S., & Lemieux, A. M. (2020). Corruption and the shadow of the future. Agent-Based Modelling for Criminological Theory Testing and Development, 167.
  • van Doren, D. (2020). Enabling Innovation Within Public Research Institutes: A Modelling Approach. In Advances in Social Simulation (pp. 441-449). Springer, Cham.
  • van Tol, M. C. M., Moncada, J. A., Lukszo, Z., & Weijnen, M. (2020). Modelling the interaction between policies and international trade flows for liquid biofuels: an agent-based modelling approach. Energy Policy, 112021.
  • Van Voorn, G., Hengeveld, G., & Verhagen, J. (2020). An agent based model representation to assess resilience and efficiency of food supply chains. Plos one, 15(11), e0242323.
  • van Weerden, J. F., Verbrugge, R., & Hemelrijk, C. K. (2020). Modelling non-attentional visual information transmission in groups under predation. Ecological Modelling, 431, 109073.
  • Veldt, N., Benson, A. R., & Kleinberg, J. (2020, August). Minimizing Localized Ratio Cut Objectives in Hypergraphs. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1708-1718).
  • Veloso, P., & Krishnamurti, R. (2020, September). An academy of spatial agents: Generating spatial configurations with deep reinforcement learning. Cognizant Architecture - What if Buildings Could Think?, 2, 191-200.
  • Vendome, C., Rao, D. M., & Giabbanelli, P. J. (2020, May). How do modelers code artificial societies? investigating practices and quality of netlogo codes from large repositories. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-12).
  • Verhagen, P. (2020). NetLogo palaeodemography scripts.
  • Vermeer, W., Hjorth, A., Jenness, S. M., Brown, C. H., & Wilensky, U. (2020). Leveraging modularity during replication of high-fidelity models: Lessons from replicating an agent-based model for HIV prevention. Journal of Artificial Societies and Social Simulation, 23(4), 7. doi.org/10.18564/jasss.4352
  • Vernon-Bido, D., & Collins, A. J. (2020). Finding Core Members of Cooperative Games using Agent-Based Modeling. arXiv preprint arXiv:2009.00519.
  • Vidal-Cordasco, M., & Nuevo-López, A. Resilience and vulnerability to climate change in the Greek Dark Ages. Journal of Anthropological Archaeology, 61, 101239.
  • Vieira, A. A., Dias, L. M., Santos, M. Y., Pereira, G. A., & Oliveira, J. A. (2020). Supply Chain Data Integration: A Literature Review. Journal of Industrial Information Integration, 100161.
  • Vigoda-Gadot, E., & Vashdi, D. R. (2020). Towards a new age of research methods in public administration, public management and public policy. Handbook of Research Methods in Public Administration, Management and Policy, 1.
  • Villanuevaa, S. K. D., & Buhata, C. A. H. Determining the Effectiveness of Practicing Non-Pharmaceutical Interventions in Improving Virus Control in a Pandemic using Agent-Based Modelling.
  • Viloria, A., Arias, Y. A. O., Balaguera, M. I., Lis-Gutiérrez, J. P., Angulo, M. G., & Lis-Gutierrez, M. (2020). Modeling and Simulating Human Occupation: A NetLogo-Agent-Based Toy Model. In Advances in Electrical and Computer Technologies (pp. 135-145). Springer, Singapore.
  • Vlug, J. H. (2020). Impact of Migration and Urbanization on Cities: an Agent-Based Model on the effects of Migration on the city of The Hague.
  • Vodovotz, Y., & An, G. (2020). Agent-Based Modeling of Wound Healing: Examples for Basic and Translational Research. In Complex Systems and Computational Biology Approaches to Acute Inflammation (pp. 223-243). Springer, Cham.
  • Voinov, A., Perez, P., Castilla-Rho, J. C., & Kenny, D. C. (2020). Integrated ecological economic modeling: what is it good for?. In Sustainable Wellbeing Futures. Edward Elgar Publishing.
  • von Briesen, E. M. (2020). Modeling Identity-Based Conflict and Genocide-An Approach Informed by Complexity Theory and Computational Social Science (Doctoral dissertation, The University of North Carolina at Charlotte).
  • Vu, T., Probst, C., Nielsen, A., Bai, H., Buckley, C., Meier, P., ... & Purshouse, R. (2020). A software architecture for mechanism-based social systems modelling in agent-based simulation models. Journal of Artificial Societies and Social Simulation.
  • Walker, N. D., Boyd, R., Watson, J., Kotz, M., Radford, Z., Readdy, L., ... & Hyder, K. (2020). A spatially explicit individual-based model to support management of commercial and recreational fisheries for European sea bass Dicentrarchus labrax. Ecological Modelling, 431, 109179.
  • Waltemath, D., Golebiewski, M., Blinov, M. L., Gleeson, P., Hermjakob, H., Hucka, M., ... & Malik-Sheriff, R. S. (2020). The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE). Journal of Integrative Bioinformatics, 1(ahead-of-print).
  • Wang, A., & Chan, E. H. (2020). The impact of power-geometry in participatory planning on urban greening. Urban Forestry & Urban Greening, 48, 126571.
  • Wang, A., Wang, H., & Chan, E. (2020). The incompatibility in urban green space provision: An agent-based comparative study. Journal of Cleaner Production, 120007.
  • Wang, M., Tsanas, A., Blin, G., & Robertson, D. (2020). Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model. Scientific Reports, 10(1), 1-13.
  • Wang, Q., & Mao, X. (2020). Dynamic Task Allocation Method of Swarm Robots Based on Optimal Mass Transport Theory. Symmetry, 12(10), 1682.
  • Wang, Y., Li, X., Zhang, F., Wang, W., & Xiao, R. (2020). Effects of rapid urbanization on ecological functional vulnerability of the land system in Wuhan, China: A flow and stock perspective. Journal of Cleaner Production, 248, 119284.
  • Wang, Z., & Jia, G. (2020). A novel agent-based model for tsunami evacuation simulation and risk assessment. Natural Hazards, 1-27.
  • Waziri, N. (2020). Education for All? Complex solutions to complex problems in the Nigerian education sector (Doctoral dissertation, University of Cambridge).
  • Webster, K. (2020, June). Negotiating an Inefficient Market: An Agent-Based Model Approach to Property Insurance Claim Negotiations. In Advances in Simulation and Digital Human Modeling: Proceedings of the AHFE 2020 Virtual Conferences on Human Factors and Simulation, and Digital Human Modeling and Applied Optimization, July 16-20, 2020, USA (Vol. 1206, p. 65). Springer Nature.
  • Wheatley, R., Pavlic, T. P., Levy, O., & Wilson, R. S. (2020). Habitat features and performance interact to determine the outcomes of terrestrial predator‐prey pursuits. Journal of Animal Ecology.
  • Widyastuti, K., Imron, M. A., Pradopo, S. T., Suryatmojo, H., Sopha, B. M., Spessa, A., & Berger, U. (2020). PeatFire: an agent-based model to simulate fire ignition and spreading in a tropical peatland ecosystem. International Journal of Wildland Fire.
  • Wijermans, N., Boonstra, W. J., Orach, K., Hentati‐Sundberg, J., & Schlüter, M. (2020). Behavioural diversity in fishing—Towards a next generation of fishery models. Fish and Fisheries.
  • Wilensky, U. J. (2020) New Developments in Restructuration Theory and Understanding Complex Systems Through Agent-Based Restructurations. AERA Annual Meeting San Francisco, CA http://tinyurl.com/rvb86xr (Conference Canceled)
  • Wilkerson, M. H. & Gravel, B. Storytelling as a Support for Collective Constructionist Activity. In Holbert, N., Berland, M., & Kafai, Y. B. (eds.), Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs, 213.
  • Williams, R. A. (2020). User Experiences using FLAME: A Case Study Modelling Conflict in Large Enterprise System Implementations. Simulation Modelling Practice and Theory, 102196.
  • Williams, T. G., Guikema, S. D., Brown, D. G., & Agrawal, A. (2020). Assessing model equifinality for robust policy analysis in complex socio-environmental systems. Environmental Modelling & Software, 104831.
  • Wilson, K. M., & Hill, M. G. (2020). Synthesis and assessment of the flat-headed peccary record in North America. Quaternary Science Reviews, 248, 106601.
  • Winterrose, M. L., Carter, K. M., Wagner, N., & Streilein, W. W. (2020). Adaptive attacker strategy development against moving target cyber defenses. In Advances in Cyber Security Analytics and Decision Systems (pp. 1-14). Springer, Cham.
  • Wolf, S., Burrows, A. C., Borowczak, M., Johnson, M., Cooley, R., & Mogenson, K. (2020). Integrated Outreach: Increasing Engagement in Computer Science and Cybersecurity. Education Sciences, 10(12), 353.
  • Wong, S. M., & Montalto, F. A. (2020). Exploring the Long‐Term Economic and Social Impact of Green Infrastructure in New York City. Water Resources Research, e2019WR027008.
  • Wozniak, M. (2020). Virtualising Space–New Directions for Applications of Agent-Based Modelling in Spatial Economics. Acta Universitatis Lodziensis. Folia Oeconomica, 1(346), 7-26.
  • Wu, B. (2020). Investor Behavior and Risk Contagion in an Information-Based Artificial Stock Market. IEEE Access, 8, 126725-126732.
  • Wu, S. P. W., Peel, A. M., Bain, C., Anton, G., Horn, M. S. & Wilensky, U. (2020). Workshops and co-design can help teachers integrate computational thinking into their K-12 STEM classes. Proceedings of CTE2020. Hong Kong, China.
  • Wu, X., Lin, Y., & Zhao, L. (2020). Simulation Modeling of Tourists’ Travel Behaviors at the Intra-City Scale. In CICTP 2020 (pp. 4599-4608).
  • Xia, H., Li, L., Cheng, X., Liu, C., & Qiu, T. (2020). A dynamic virus propagation model based on social attributes in city IoTs. IEEE Internet of Things Journal.
  • Xiang, L., Shen, G., & Tan, Y. (2020, December). A Multi-agent Platform to Inform Strategies for Briefing Age-Friendly Communities in Urban China. In International Conference on Resource Sustainability-Sustainable Urbanisation in the BRI Era (pp. 181-193). Springer, Singapore.
  • Yang, Q., Sun, Y., Liu, X., & Wang, J. (2020). MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China. Sustainability, 12(2), 546.
  • Yang, Q., Wang, J., Liu, X., & Xia, J. (2020). MAS-Based Interaction Simulation within Asymmetric Information on Emergency Management of Urban Rainstorm Disaster. Complexity, 2020.
  • Yao, X., Sun, H., & Fan, B. (2020). A novel simulation framework for crowd co-decisions. International Journal of Crowd Science.
  • Yasrebi-Soppa, P., Bartels, J. J., Viefhaus, S., Reuss, P., & Althoff, K. D. (2020). Visualizing the behavior of CBR agents in an FPS Scenario.
  • Ye, T., Ning, Z., Zhang, J., & Xu, M. (2020). Trusted measurement of behaviors for the Internet of Things. Alexandria Engineering Journal.
  • Yıldız, B., & Çağdaş, G. (2020). Fuzzy logic in agent-based modeling of user movement in urban space: Definition and application to a case study of a square. Building and Environment, 169, 106597.
  • Youl, E., Malo, S., & Ouaro, S. (2020, November). An Agent-Based Study of the Impact of Sensitization on the Spread of Covid 19 in Burkina Faso. In Proceedings of the Future Technologies Conference (pp. 64-77). Springer, Cham.
  • Young, E., & Aguirre, B. (2020). PrioritEvac: an Agent-Based Model (ABM) for Examining Social Factors of Building Fire Evacuation. Information Systems Frontiers, 1-14.
  • Yousefi, M., Yousefi, M., & Fogliatto, F. S. (2020). Simulation-based optimization methods applied in hospital emergency departments: A systematic review. Simulation, 0037549720944483.
  • Yue, T., Long, R., Chen, H., Liu, J., Liu, H., & Gu, Y. (2020). Energy-saving behavior of urban residents in China: A multi-agent simulation. Journal of Cleaner Production, 252, 119623.
  • Yust, A. E., & Smyth, D. S. (2020). Simulating Bacterial Growth, Competition, and Resistance with Agent-Based Models and Laboratory Experiments. In An Introduction to Undergraduate Research in Computational and Mathematical Biology (pp. 217-271). Birkhäuser, Cham.
  • Zahmani, M. H., & Atmani, B. (2020). Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation. Journal of Scheduling, 1-22.
  • Zakharova, L., Meyer, K. M., & Seifan, M. (2020). Combining trait-and individual-based modelling to understand desert plant community dynamics. Ecological Modelling, 434, 109260.
  • Zamora-Maldonado, H. C., Avila-Foucat, V. S., Sánchez-Sotomayor, V. G., & Lee, R. Social-ecological Resilience Modeling: Water Stress Effects in the Bighorn Sheep Management System in Baja California Sur, Mexico. Ecological Complexity, 45, 100884.
  • Zapata-Roldan, F., & Sheikh, N. J. (2020). A Design Management Agent-Based Model for New Product Development. IEEE Transactions on Engineering Management.
  • Zarrabi, A. H., Azarbayjani, M., & Tavakoli, M. (2020) Generative Design Tool: Integrated Approach toward Development of Piezoelectric Façade System.
  • Zhang, B. H., & Ahmed, S. A. (2020). Systems Thinking—Ludwig Von Bertalanffy, Peter Senge, and Donella Meadows. In Science Education in Theory and Practice (pp. 419-436). Springer, Cham.
  • Zhang, D. (2020). Teaching Geometry to Students With Learning Disabilities: Introduction to the Special Series. Learning Disability Quarterly, 0731948720959769.
  • Zhang, G., Li, H., & Yan, S. (2020). The Vital Few: Exploring the Role of Expertise in the Process of Team Creativity. The Journal of Creative Behavior.
  • Zhang, H., & Zhang, B. (2020). The unintended impact of carbon trading of China's power sector. Energy Policy, 147, 111876.
  • Zhang, M., Chen, H., & Lyytinen, K. (2020). Validating the coevolutionary principles of business and IS alignment via agent-based modeling. European Journal of Information Systems, 1-16.
  • Zhang, M., Chen, H., Li, X., & Luo, A. (2020). Describing coevolution of business and IS alignment via agent-based modeling.
  • Zhang, R., & Chan, W. K. V. (2020, July). Evaluation of Energy Consumption in Block-Chains with Proof of Work and Proof of Stake. In Journal of Physics: Conference Series (Vol. 1584, No. 1, p. 012023). IOP Publishing.
  • Zhang, R., & Tielbörger, K. (2020). Density-dependence tips the change of plant–plant interactions under environmental stress. Nature Communications, 11(1), 1-9.
  • Zhang, X., Xu, L., & Gao, M. (2020, September). An Efficient Influence Maximization Algorithm Based on Social Relationship Priority in Mobile Social Networks. In International Symposium on Security and Privacy in Social Networks and Big Data (pp. 164-177). Springer, Singapore.
  • Zhang, Y., Gao, J., Cole, S., & Ricci, P. (2020). How the Spread of User-Generated Contents (UGC) Shapes International Tourism Distribution: Using Agent-Based Modeling to Inform Strategic UGC Marketing. Journal of Travel Research, 0047287520951639.
  • Zhao, J., Bai, A., Xi, X., Huang, Y., & Wang, S. (2020). Impacts of malicious attacks on robustness of knowledge networks: a multi-agent-based simulation. Journal of Knowledge Management.
  • Zhao, X., Rivera-Monroy, V. H., Wang, H., Xue, Z. G., Tsai, C. F., Willson, C. S., ... & Twilley, R. R. (2020). Modeling soil porewater salinity in mangrove forests (Everglades, Florida, USA) impacted by hydrological restoration and a warming climate. Ecological Modelling, 436, 109292.
  • Zheng, J., Ma, G., Wei, J., Wei, W., He, Y., Jiao, Y., & Han, X. (2020). Evolutionary process of household waste separation behavior based on social networks. Resources, Conservation and Recycling, 161, 105009.
  • Zheng, Y. (2020, June). The Theme Cooperation Mechanism of Science and Technology Enterprise Incubation Alliance Based on Multi-agent System under Computer Control. In Journal of Physics: Conference Series (Vol. 1574, No. 1, p. 012071). IOP Publishing.
  • Zhou, H., Shen, S., & Liu, J. (2020). Malware propagation model in wireless sensor networks under attack–defense confrontation. Computer Communications.
  • Zhuo, L., & Han, D. (2020). Agent-based modelling and flood risk management: a compendious literature review. Journal of Hydrology, 125600.
  • Ziv, G., Beckmann, M., Bullock, J., Cord, A., Delzeit, R., Domingo, C., ... & Neteler, M. (2020). BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes, 6, e52052.
  • Zoričak, M., Horváth, D., Gazda, V., & Hudec, O. (2020). Spatial evolution of industries modelled by cellular automata. Journal of Business Research.
  • Zou, J., Wang, K., & Sun, H. (2020). An implementation architecture for crowd network simulations. International Journal of Crowd Science.
  • Zukri, N. H. A., Rashid, N. A. M., Awang, N., & Zulkifli, Z. A. (2020). Agent-Based Encryption for Password Management Application. In Charting the Sustainable Future of ASEAN in Science and Technology (pp. 529-541). Springer, Singapore.
  • Zvereva, O. M. (2020). Investigation of Money Turnover in the Computer Agent-Based Model. In Advances in Information Technologies, Telecommunication, and Radioelectronics (pp. 95-105). Springer, Cham.

2019

  • Crabtree, S., Harris, K., Davies, B., & Romanowska, I. (2019, July 6). Outreach in Archaeology with Agent-Based Modeling: Part 3 of 3. https://doi.org/10.1017/aap.2019.4
  • Cruz, A., Carneiro, E., Fontes, X., Kokkinogenis, Z., & Rossetti, R. J. (2019, October). Hermes: a tool for mesoscopic simulation of advanced traveller information systems. In 2019 IEEE International Smart Cities Conference (ISC2) (pp. 638-643). IEEE.
  • Abdulkareem, S. A., Mustafa, Y. T., Augustijn, E. W., & Filatova, T. (2019). Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models. Geoinformatica, 23(2), 243-268.
  • Abdullah, L. (2019). Model Interaksi Pelaku Hutan Rakyat dalam Perdagangan Kayu: Pendekatan Simulasi Model Berbasis Agen (The Interaction Model of Community Forest Behavior in Wood Trade: Agent Based Modelling Approach). Jurnal Penelitian Hutan Tanaman, 16(1), 21-34.
  • Abrahamson, D. (2019). A new world: Educational research on the sensorimotor roots of mathematical reasoning. In A. Shvarts (Ed.), Proceedings of the annual meeting of the Russian chapter of the International Group for the Psychology of Mathematics Education (PME) & Yandex (pp. 48–68). Moscow: Yandex
  • Abrahamson, D., & Shulman, A. (2019). Co-constructing movement in mathematics and dance: An interdisciplinary pedagogical dialogue on subjectivity and awareness. Feldenkrais Research Journal, 6, 1-24. Retrieved from
  • Abrahamson, D., Flood, V. J., Miele, J. A., & Siu, Y.-T. (2019). Enactivism and ethnomethodological conversation analysis as tools for expanding Universal Design for Learning: The case of visually impaired mathematics students. ZDM Mathematics Education, 51(2), 291-303. doi:10.1007/s11858-018-0998-1
  • Adeel, M., Khalid, M., Asif, M., & Faisal, M. N. (2019). Simulation Models for Comparison of Toxicities of Anticancer Drugs. Annals of Punjab Medical College (APMC), 13(3), 216-222.
  • Aghaie, A., & Hajian Heidary, M. (2019). Simulation-based optimization of a stochastic supply chain considering supplier disruption: Agent-based modeling and reinforcement learning. Scientia Iranica, 26(6), 3780-3795.
  • Ahmed, S. H., Bashir, A. K., & Guibene, W. (2019). Introduction to the special section on emerging technologies for connected vehicles and ITS networks. Computers & Electrical Engineering, 75, 309-311.
  • Aji, W. S. (2019). Simulation with Multi Agent Flood Prediction Based on Rain Intensity Using Particle Swarm Otimization. Jurnal Teknologi Informasi, 5(2), 93-98.
  • Al-Najjar, A. A. M., & Chasib, H. S. (2019). Design and implementation weights equation for optimization DSR protocol in MANETs environment. Int. J. Adv. Sci. Technol, 28(8), 457-470.
  • Alves, F., Varela, M. L. R., Rocha, A. M. A., Pereira, A. I., & Leitão, P. (2019). A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment.
  • Amelia, P., & Lathifah, A. (2019). Dynamics analysis of container needs and availability in surabaya container terminal with agent-based modeling and simulation. Procedia Computer Science, 161, 910-918.
  • Anderson, Sven, and Siv Disa Anderson. "Coding and Music Creation in a Multi-Agent Environment." Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. 2020.
  • Antelmi, A., Cordasco, G., D’Auria, M., De Vinco, D., Negro, A., & Spagnuolo, C. (2019, October). On Evaluating Rust as a Programming Language for the Future of Massive Agent-Based Simulations. In Asian Simulation Conference (pp. 15-28). Springer, Singapore.
  • Anton, G. & Wilensky, U. (2019). One size fits all: Designing for socialization in physical computing. In Proceedings of the 50th ACM technical symposium on computer science education (pp. 825 - 831). ACM
  • Arastoopour Irgens, G., Chandra, S., Dabholkar, S., Horn, M., & Wilensky, U. (2019). Classifying Emergent Student Learning in a High School Computational Chemistry Unit. Paper presented at the American Education Research Association (AERA) Conference. Toronto, CA
  • Arastoopour Irgens, G., Dabholkar, S., Bain, C., Woods, P., Hall, K., Swanson, H., Horn, M., & Wilensky, U. (2019). Modeling and Measuring Students' Computational Thinking Practices in Science. Journal of Science Education and Technology.
  • Ashley, D. R., Chockalingam, V., Kuzma, B., & Bulitko, V. (2019, July). Learning to select mates in artificial life. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 103-104).
  • Ashraf, R., Zafar, B., Jabbar, S., Ahmad, M., & Ahmed, S. H. (2019). Modeling and Simulation of Resource-Constrained Vaccination Strategies and Epidemic Outbreaks. In Applications of Intelligent Technologies in Healthcare (pp. 131-141). Springer, Cham.
  • Aslan, U., Anton, G., & Wilensky, U. (2019). Bringing Powerful Ideas to Middle School Students' Lives Through Agent-Based Modeling.a Paper presented at the Annual Meeting of the American Educational Research Association (AERA) 2019. Toronto, CA
  • Azarov, I., Peskov, K., Helmlinger, G., & Kosinsky, Y. (2019). Role of T cell-to-dendritic cell chemoattraction in T cell priming initiation in the lymph node: An agent-based modeling study. Frontiers in immunology, 10, 1289.
  • Bain, C. & Wilensky U. (2019). Sorting Out Algorithms: Learning about Complexity through Participatory Simulations. In E. K. Hawthorne, M. A. Pérez-Quiñones, S. Heckman, & J. Zhang (Eds.). Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE '19), February 27-March 2, 2019, Minneapolis, MN, USA.
  • Bain, C., & Anton, G. (2019, February). Integrating Agent-based Modeling in STEM Classes: From Blocks to Text and Back?. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 1238-1238).
  • Bain, C., Anton, G., Horn, M., & Wilensky, U. (2019, October). Position: Building Blocks for Agent-based Modeling Can Scaffold Computational Thinking Engagement in STEM Classrooms. In 2019 IEEE Blocks and Beyond Workshop (B&B) (pp. 1-4).
  • Barbuto, A., Lopolito, A., Santeramo, F.G. (2019) Improving diffusion in agriculture: an agent-based model to find the predictors for efficient early adopters Agricultural and Food Economics. [PDF]
  • Basu, D. (2019). Examining Students’ Covariational Reasoning Through Mathematical Modeling Activities Embedded in the Context of the Greenhouse Effect.
  • Basu, D., & Panorkou, N. (2019). Integrating Covariational Reasoning and Technology into the Teaching and Learning of the Greenhouse Effect. Journal of Mathematics Education, 12(1), 6-23.
  • Bauduin, S., McIntire, E. J., & Chubaty, A. M. (2019). NetLogoR: a package to build and run spatially explicit agent‐based models in R. Ecography, 42(11), 1841-1849.
  • Bayo, M. (2019). Agend-Based-Modelling. Pond eutrophication in agroecosystems and the influence of combinations of pesticides and fertilizers on aquatic productivity. GRIN Verlag.
  • Benhadi-Marín, J., Pereira, J. A., Sousa, J. P., & Santos, S. A. (2019). EcoPred: an educational individual based model to explain biological control, a case study within an arable land. Journal of Biological Education, 1-16.
  • Bipasha, T., Azucena, J., Alkhaleel, B., Liao, H., & Nachtmann, H. (2019, December). Hybrid simulation to support interdependence modeling of a multimodal transportation network. In 2019 Winter Simulation Conference (WSC) (pp. 1390-1401). IEEE.
  • Bithell, M. Creating a Model of the Earth System (MOTES): Some Experiences with Parallel ABM. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 459). Springer Nature.
  • Blikstein, P., & Moghadam, S. H. (2019). 3 Computing Education. The Cambridge handbook of computing education research, 56.
  • Bo, Y. (2019). The data clustering based dynamic risk identification of biological immune system: mechanism, method and simulation. Cluster Computing, 22(3), 6253-6266.
  • Borong, N., & Galdo, M. (2019). NET LINGO Initialism: An Agent-Based Model on Language. Journal of Educational and Human Resource Development, 7, 150-155.
  • Borowczak, M., & Burrows, A. C. (2019). Ants Go Marching—Integrating Computer Science into Teacher Professional Development with NetLogo. Education Sciences, 9(1), 66.
  • Bortz, W. W., Gautam, A., Lipscomb, K., & Tatar, D. (2019). Integration Computational Thinking into Middle School Science: A search for Synergistic Pedagogy. In ASEE Southeastern Section Conference.
  • Boukehila, A., & Taleb, N. (2019, November). Case-Based Approach to Detect Emergence. In Proceedings of the 2019 3rd International Conference on Big Data Research (pp. 98-102).
  • Brennan, R. W., Hermanson, G., Nelson, N., Paul, R., & Sullivan, M. (2019). Using agent-based modelling for preliminary EER experimental design. Proceedings of the Canadian Engineering Education Association (CEEA).
  • Browning, F., Moore K., Campos, J. (2019) Exploring Negative Absolute Temperature Using NetLogo. The Physics Journal, 57(26), 26-27. [PDF]
  • Bulitko, V., Doucet, K., Evans, D., Docking, H., Walters, M., Oliver, M., ... & Kendal-Freedman, N. (2019, July). A-life Evolution with Human Proxies. In Artificial Life Conference Proceedings (pp. 465-466). One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press.
  • Burbach, L., Belavadi, P., Halbach, P., Plettenberg, N., Nakayama, J., Ziefle, M., & Valdez, A. C. Towards An Understanding of Opinion Formation on the Internet. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 133). Springer Nature.
  • Buss, A., Shepherd, C. E., & Smith, S. M. (2019). Learning from Failure: Growing Roses of Success.
  • Butler, G., Rudge, J., & Dash, P. R. (2019). Mathematical modelling of cell migration. Essays in biochemistry, 63(5), 631-637.
  • Calabrò, G., Torrisi, V., Inturri, G., & Ignaccolo, M. (2020). Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization. European Transport Research Review, 12(1), 1-11.
  • Cardinot, M., O’Riordan, C., Griffith, J., & Perc, M. (2019). Evoplex: A platform for agent-based modeling on networks. SoftwareX, 9, 199-204.
  • Cascalho, J., Trigo, P., Cruz, M. J., Mendes, A., Giacomello, E., Ressurreiçao, A., ... & Morato, T. (2019). SIMSEA: A Multiagent Architecture for Fishing Activity in a Simulated Environment.
  • Castañeda-Martínez R.A., Flores DL., Castro C., Benítez B.(2019). Agent-Based Model of Resistant Bacterial Evolution in an Heterogeneous Medium. In: Sanchez M., Aguilar L., Castañón-Puga M., Rodríguez A. (eds) Applied Decision-Making. Studies in Systems, Decision and Control, vol 209. Springer, Cham
  • Castro, C., Flores, D. L., Cervantes-Vásquez, D., Vargas-Viveros, E., Gutiérrez-López, E., & Muñoz-Muñoz, F. (2019). An agent-based model of the fission yeast cell cycle. Current genetics, 65(1), 193-200.
  • Castro, C., Flores, D. L., Vargas, E., Cervantes, D., & Delgado, E. (2019). Agent-Based Model of the Budding Yeast Cell Cycle Regulatory Network. In World Congress on Medical Physics and Biomedical Engineering 2018 (pp. 531-534). Springer, Singapore.
  • Ceja, A. Y., & Kane, S. (2019, August). An Astroecological Model for Characterizing Exoplanet Habitability. In AAS/Division for Extreme Solar Systems Abstracts (Vol. 4).
  • Chao, D., Hashimoto, H., & Kondo, N. (2019). Social influence of e-cigarette smoking prevalence on smoking behaviours among high-school teenagers: Microsimulation experiments. PloS one, 14(8), e0221557.
  • Chappin, E., Bouwmans, I., & Deijkers, E. EMLab-Consumer—Simulating Energy Efficiency Adoption Decisions of European Households. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 485). Springer Nature.
  • Chaudhari, K. S. (2019).Agent-based modelling of electric vehicle charging for optimized charging station operation. Doctoral thesis, Nanyang Technological University, Singapore
  • Chen, P., Wu, X., & Miao, D. (2019, June). Agent-Based Modeling in a Simple Circular Economy. In International Conference on Applications and Techniques in Cyber Security and Intelligence (pp. 487-497). Springer, Cham.
  • Chen, Z. (2019). An agent-based model for information diffusion over online social networks. Papers in Applied Geography, 5(1-2), 77-97.
  • Chen, Z., Spana, S., Yin, Y., & Du, Y. (2019). An advanced parking navigation system for downtown parking. Networks and Spatial Economics, 19(3), 953-968.
  • Chennoufi, M., & Bendella, F. (2019, April). Decision Making in Complex System. In 2019 5th International Conference on Optimization and Applications (ICOA) (pp. 1-7). IEEE.
  • Chiew, L. S., & Amerudin, S. (2019, June). ANALYSIS OF BURGLARY CRIME PATTERNS THROUGH THE INTEGRATION OF SPATIAL STATISTICS AND AGENT-BASED MODELLING.
  • Chliaoutakis, A., & Chalkiadakis, G. (2019, June). AncientS-ABM: A Novel Tool for Simulating Ancient Societies. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 237-241). Springer, Cham.
  • Chu, H., Yu, J., Wen, J., Yi, M., & Chen, Y. (2019). Emergency evacuation simulation and management optimization in urban residential communities. Sustainability, 11(3), 795.
  • Chumachenko, D., & Yakovlev, S. (2019, February). On intelligent agent-based simulation of network worms propagation. In 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM) (pp. 11-14). IEEE.
  • Chumachenko, D., Meniailov, I., Bazilevych, K., & Chumachenko, T. (2019, September). On Intelligent Decision Making in Multiagent Systems in Conditions of Uncertainty. In 2019 XIth International Scientific and Practical Conference on Electronics and Information Technologies (ELIT) (pp. 150-153). IEEE.
  • Cimino, M. G., Lega, M., Monaco, M., & Vaglini, G. (2019, February). Adaptive Exploration of a UAVs Swarm for Distributed Targets Detection and Tracking. In ICPRAM (pp. 837-844).
  • Cockrell, C., Teague, J., & Axelrod, D. E. (2020). Prevention of Colon Cancer Recurrence From Minimal Residual Disease: Computer Optimized Dose Schedules of Intermittent Apoptotic Adjuvant Therapy. JCO Clinical Cancer Informatics, 4, 514-520.
  • Coronel, A. R., & Alatriste, F. R. (2019, March). Turning caregivers into informed agents as a strategy to disseminate scientific information about cancer. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 938-944). Springer, Cham.
  • Cortier, O., Boutouil, M., & Maquaire, O. (2019). Quantifying Benefits of Permeable Pavement on Surface Runoff, An Agent-Based-Model with NetLogo. In New Trends in Urban Drainage Modelling: UDM 2018 11 (pp. 729-733). Springer International Publishing.
  • Cotfas, L. A., Delcea, C., Milne, R. J., Salari, M., Crăciun, L., & Molănescu, A. G. (2019). Testing new methods for boarding a partially occupied airplane using apron buses. Symmetry, 11(8), 1044.
  • Crouse, K.N., Miller, C.M., & Wilson, M.L. (2019). New approaches to modeling primate socioecology: Does small female group size BEGET loyal males?. Journal of Human Evolution, 137(102671). https://doi.org/10.1016/j.jhevol.2019.102671
  • Cruz, E. G. A. (2019). Modeling Social Learning: An Agent-Based Approach (Doctoral dissertation, Old Dominion University).
  • D’Souza, M., & Kashi, R. N. (2019, January). Avionics Self-adaptive Software: Towards Formal Verification and Validation. In International Conference on Distributed Computing and Internet Technology (pp. 3-23). Springer, Cham.
  • Da Costa, L., & Rajotte, J. F. (2019, May). Crowd Prediction Under Uncertainty. In Canadian Conference on Artificial Intelligence (pp. 308-319). Springer, Cham.
  • Dabholkar, S. (2019) Designing Emergent Systems Microworlds to learn computational thinking in the context of synthetic biology. Poster presented at Learn.Design.Compute with Bio 2019, Philadelphia, USA
  • Dabholkar, S. & Wilensky, U. (2019). Designing ESM-mediated collaborative activity systems for science learning. Poster to be presented at International Conference of Computer Supported Collaborative Learning 2019, Lyon, France.
  • Dabholkar, S., Swanson, H., & Wilensky, U. (2019). Epistemic considerations for modeling: Understanding the usefulness and limitations of models with Emergent Systems Microworlds. In a Related Paper Set, Using Technology to Promote Students’ Modeling Practice and Complex Systems Thinking. The Annual Meeting of the National Association of Research in Science Teaching (NARST), Baltimore, MD, USA.
  • Dabholkar, S., Wilensky, U., & Horn, M. (2019) Supporting a teacher’s integration of Computational Thinking (CT) in a biology class by co-designing an ESM- (Emergent Systems Microworlds) based curricular unit, Poster presented at Inaugural symposium on Computer Science and Learning Science, Evanston, USA
  • Dalle Nogare, D., & Chitnis, A. B. (2019, December). NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium. In Seminars in Cell & Developmental Biology. Academic Press.
  • Davies, B., Romanowska, I., Harris, K., & Crabtree, S. A. (2019). Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3. Advances in Archaeological Practice, 7(2), 185-193.
  • Davis, P., O'Mahony, A. & Pfautz, J. (2019).Social-Behavioral Modeling for Complex Systems. John Wiley & Sons.
  • Davydenko, I. Y., & Fransen, R. W. (2019). Conceptual agent based model simulation for the Port Nautical Services. IFAC-PapersOnLine, 52(3), 19-24.
  • Delcea, C., & Cotfas, L. A. (2019). Increasing awareness in classroom evacuation situations using agent-based modeling. Physica A: Statistical Mechanics and its Applications, 523, 1400-1418.
  • Delcea, C., Milne, R. J., Cotfas, L. A., Crăciun, L., & Molănescu, A. G. (2019). Methods for Accelerating the Airplane Boarding Process in the Presence of Apron Buses. IEEE Access, 7, 134372-134387.
  • DeLuca, C. (2019). Keyword Response: Out of Step. In Key Concepts in Curriculum Studies (pp. 47-49). Routledge.
  • DeMarco, K., Squires, E., Day, M., & Pippin, C. (2019). Simulating collaborative robots in a massive multi-agent game environment (scrimmage). In Distributed Autonomous Robotic Systems (pp. 283-297). Springer, Cham.
  • Dhou, K. (2019). An innovative design of a hybrid chain coding algorithm for bi-level image compression using an agent-based modeling approach. Applied Soft Computing, 79, 94-110.
  • Dhou, K., & Cruzen, C. (2019). An innovative chain coding technique for compression based on the concept of biological reproduction: an agent-based modeling approach. IEEE Internet of Things Journal, 6(6), 9308-9315.
  • Dickes, A.C., Kamarainen, A., Metcalf, S.J., Gün‐Yildiz, S., Brennan, K., Grotzer, T., & Dede, C. (2019). Scaffolding ecosystems science practice by blending immersive environments and computational modeling. British Journal of Educational Technology, 50(5), 2181-2202. https://doi.org/10.1111/bjet.12806
  • Ding, F., & Pan, W. (2019). Simulation Research on Large Passenger Flow Guidance of Urban Rail Transit Based on Multi-Agent. Academic Journal of Computing & Information Science, 2(1).
  • Ding, Feng, and Wenjie Pan. "Simulation Research on Large Passenger Flow Guidance of Urban Rail Transit Based on Multi-Agent." Academic Journal of Computing & Information Science 2.1 (2019).
  • Ding, K., Gilligan, J.M., & Hornberger, G.M. (2019). Avoiding "day-zero": A Testbed for Evaluating Integrated Food-energy-water Management in Cape Town, South Africa. Proceedings of the 2019 Winter Simulation Conference (WSC), 866-877, IEEE. 10.1109/WSC40007.2019.9004889
  • Dobaria, R., & Chilka, A. (2019). IoT Smart Waste Monitoring and Collection Framework. International Journal of Distributed Computing and Technology, 5(1), 27-32.
  • Dragoni, A. F. (2019). An Agent-Swarm Simulator for Dynamic Vehicle Routing Problem Empirical Analysis. In Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection: 17th International Conference, PAAMS 2019, Ávila, Spain, June 26-28, 2019, Proceedings (Vol. 11523, p. 246). Springer.
  • Dragotă, V., & Delcea, C. (2019). How long does it last to systematically make bad decisions? An agent-based application for dividend policy. Journal of Risk and Financial Management, 12(4), 167.
  • Egbert, M. (2019, July). Real-Time Visualization and Interaction with Computational Artefacts. In MethAL workshop with The Conference on Artificial Life 2019.
  • El-dosuky, M. (2019). Taming the Sharing Economy Flood: Modelling the Imposing of Sharing Economy Regulation. Available at SSRN 3372792.
  • Elfakir, A., & Tkiouat, M. (2019). Profit and loss Sharing Negotiations involving a VC and an entrepreneur: A Game Theoretic Approach with Agent Based Simulation.
  • Elfakir, A., Tkiouat, M., & Allam, K. (2019). Entrepreneurial financing under uncertainty: Performance comparison between ROMCA and conventional microloans using agent based simulation.
  • Esmaeili Bidhendi, M. (2019). The Study of CO Symptoms' Impacts on Individuals, Using GIS and Agent-based Modeling (ABM). Pollution, 5(3), 463-471.
  • Falcionelli, N., et al. (2019)."An Agent-Swarm Simulator for Dynamic Vehicle Routing Problem Empirical Analysis." Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection Lecture Notes in Computer Science, 26 June 2019, pp. 246–250., doi:10.1007/978-3-030-24209-1_23.
  • Falletta, J., & Mukheibir, P. (2019). Demand Forecasting: Review of processes and methodologies [prepared for the Water Corporation].a
  • Farris, A. V. (2019, June). The Sensing Bridge Between Perceptuomotor Experience and Scientific Investigation. In Proceedings of the 18th ACM International Conference on Interaction Design and Children (pp. 648-651).
  • Febriandini, I. F., Sutopo, W., & Hisjam, M. (2019, May). Analysis daily newspaper distribution in Solo by Agent Based Simulation. In IOP Conference Series: Materials Science and Engineering (Vol. 528, No. 1, p. 012033). IOP Publishing.
  • Frahm, E., Kandel, A.W. & Gasparyan, B. (2019). Upper Palaeolithic Settlement and Mobility in the Armenian Highlands: Agent-Based Modeling, Obsidian Sourcing, and Lithic Analysis at Aghitu-3 Cave. Journal of Paleolithic Archaeology 2, 418–465. https://doi.org/10.1007/s41982-019-00025-5
  • França da Silva, T., Alves Leite, J. L., Campos Ferro Junior, R. J., Ferreira da Costa, L., Pinheiro de Souza, R., Bernardino Andrade, J. P., & Lima de Campos, G. A. (2019, May). Smart targets to avoid observation in cto problem. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1958-1960).
  • Freelan, D., Spagnuolo, C., Scarano, V., Cordasco, G., & Cioffi-Revilla, C. (2019, June). The MASON Simulation Toolkit: Past, Present, and Future. In Multi-Agent-Based Simulation XIX: 19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers (Vol. 11463, p. 75). Springer.
  • Fuchs, M., & Neumayr, R. (2019, September). Agent-Based Semiology for Simulation and Prediction of Contemporary Spatial Occupation Patterns. In Design Modelling Symposium Berlin (pp. 648-661). Springer, Cham.
  • Fulop, S. A., & Scott, H. (2019). Vowel System Sandbox: Complex System Modelling of Language Change. Journal of Open Research Software, 7(1).
  • Gama, C. A. F., & Vivacqua, A. S. (2019, October). The cooperative dynamics of Brazilian Oil and Gas Innovations Systems a Research Proposal. In Anais do XV Simpósio Brasileiro de Sistemas Colaborativos (pp. 24-29). SBC.
  • Gao, S., Song, X., & Ding, R. (2019). Dynamic Agent-Based Simulation of Information Transfer in Collaborative Project Network. In Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation (pp. 602-610). Reston, VA: American Society of Civil Engineers.
  • García-Peña, C., Gutiérrez-Robledo, L. M., Cabrera-Becerril, A., & Fajardo-Ortiz, D. (2019). Team Assembly Mechanisms and the Knowledge Produced in the Mexico’s National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach. Scientifica, 2019.
  • Garzón, M., & Rojas-Galeano, S. (2019, November). An Agent-Based Model of Urban Pigeon Swarm Optimisation. In 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-6). IEEE.
  • Georgescu, A., Gheorghe, A. V., Piso, M. I., & Katina, P. F. (2019). Governance by Emerging Technologies—The Case for Sand and Blockchain Technology. In Critical Space Infrastructures (pp. 237-247). Springer, Cham.
  • Giabbanelli, P., Fattoruso, M., & Norman, M. L. (2019, May). Cofluences: simulating the spread of social influences via a hybrid agent-based/fuzzy cognitive maps architecture. In Proceedings of the 2019 ACM SIGSIM conference on principles of advanced discrete simulation (pp. 71-82).
  • Gibson, J. B., Page, J., & Mukhlish, F. (2019, September). Simulation of an Unmanned Aerial Vehicle Search and Rescue Swarm for Observation of Emergent Behaviour. In Australasian Simulation Congress (pp. 95-105). Springer, Singapore.
  • Giner Sanz, J. J., García Gabaldón, M., Ortega Navarro, E. M., Shao Horn, Y., & Pérez Herranz, V. (2019, September). A NetLogo® model for introducing students to genetic algorithms. In IN-RED 2019. V Congreso de Innovación Educativa y Docencia en Red (pp. 88-101). Editorial Universitat Politècnica de València.
  • Ginovart Gisbert, M., & Font Marques, M. (2019). Flaix de ciència. Massa petits per poder ser modelitzats? Models basats en l’individu per representar i investigar poblacions microbianes amb creixement no planctònic. Treballs de la Societat Catalana de Biologia, 68, 50-53.
  • Ginovart Gisbert, M., & Font Marques, M. (2019). Flaix de ciència. Massa petits per poder ser modelitzats? Models basats en l’individu per representar i investigar poblacions microbianes amb creixement no planctònic. Treballs de la Societat Catalana de Biologia, 68, 50-53.
  • Golovnev, G. (2019, May). Modeling of Multi-agent Voltage Control in Distribution Electric Networks of Railways. In International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies EMMFT 2018: Volume 1 (Vol. 982, p. 300). Springer.
  • Gooding, T. (2019). Agent-based model history and development. In Economics for a Fairer Society (pp. 25-36). Palgrave Pivot, Cham.
  • Gooding, T. (2019). Evolutionary Price Robustness. In Economics for a Fairer Society (pp. 105-114). Palgrave Pivot, Cham.
  • Gooding, T., & Gooding, T. (2019). Evolution in Netlogo. Economics for a Fairer Society: Going Back to Basics using Agent-Based Models, 45-51.
  • Graham, S., Gupta, N., Smith, J., Angourakis, A., Reinhard, A., Ellenberger, K., ... & Nobles, G. (2019). The Open Digital Archaeology Textbook.
  • Greasley, A. (2019). Case Study: Agent-Based Modeling in Discrete-Event Simulation. In Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics (pp. 325-335). De Gruyter.
  • Greco, A., Cannizzaro, F., & Pluchino, A. (2019). Automatic evaluation of plastic collapse conditions for planar frames with vertical irregularities. Engineering with Computers, 35(1), 57-73.
  • Gružauskas, V., Gimžauskienė, E., & Navickas, V. (2019). Forecasting accuracy influence on logistics clusters activities: The case of the food industry. Journal of Cleaner Production, 240, 118225.
  • Gullichsen, F. (2019). Simulation of social media networks with agent-based modeling: The research and evaluation of using Netlogo, an agent-based modeling software.
  • Guo, Y. & Wilensky, U. (2019). Changing High School Students’ Perceptions of Wealth Inequality in the U.S. through Agent-based Participatory Simulations. Poster presented at the annual meeting of the American Educational Research Association (Special Interest Group: Learning Sciences), Toronto, Canada, April 5-9.
  • Gurkan, C., Rasmussen, L. & Wilensky, U. (2019). Effects of Visual Sensory Range on the Emergence of Cognition in Early Terrestrial Vertebrates: An Agent-Based Modeling Approach. The 2019 Conference on Artificial Life, Newcastle upon Tyne, UK. No. 31, 475-476.
  • Haire, M., Xu, X., Alboul, L., Penders, J., & Zhang, H. (2019, September). Ship hull repair using a swarm of autonomous underwater robots: A self-assembly algorithm. In 2019 European Conference on Mobile Robots (ECMR) (pp. 1-6). IEEE.
  • Hameed, B., Othman, W. A. F. W., Noor, N. M., Bakar, E. A., & Hawary, A. F. (2019). DECENTRALIZED PATH FORMATION TECHNIQUE FOR SWARM ROBOTS USING BATMAN APPROACH. ROBOTIKA, 1(1), 9-15.
  • Hammouda, M., Kaya, C. S., & Yücesoy, C. A. (2019, October). Development of an Agent-Based Model to Study the Mechanism of Effects of Botulinum Toxin on Muscle Tissue Adaptation. In 2019 Medical Technologies Congress (TIPTEKNO) (pp. 1-3). IEEE.
  • Hannum, C. (2019). Market concentration in real estate brokerage in economic downturns: evidence from spatial agent-based models. Applied Economics Letters, 26(19), 1567-1571.
  • Harmsma, W H. (2019). "The Effects of Stocking Configurations in Industrial Symbiotic Networks : an Agent-Based Simulation Study.” The Effects of Stocking Configurations in Industrial Symbiotic Networks : an Agent-Based Simulation Study[HTML].
  • Haryadi, F. N., Imron, M. A., Indrawan, H., & Triani, M. (2019, October). Predicting Rooftop Photovoltaic Adoption In The Residential Consumers of PLN Using Agent-Based Modeling. In 2019 International Conference on Technologies and Policies in Electric Power & Energy (pp. 1-5). IEEE.
  • Hasani, M. F., & Utama, N. P. (2019, September). Analysis of the Effect of the Number of Human Populations on the Spread of Dengue Fever. In 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (pp. 1-5). IEEE.
  • He, Z., Yuan, H., Li, Z., Gao, L., Zhang, E., Yao, Y., & Zhang, X. (2019, December). Automatic Route Guidance Method based on VANETs. In 2019 6th International Conference on Information Science and Control Engineering (ICISCE) (pp. 1009-1012). IEEE.
  • Hernandez-Betancur, J. E., Montoya-Restrepo, L. A., & Montoya-Restrepo, I. (2019). Deliberate Strategy Deconstructing Event for the Arising of the Emergent Strategy. Journal of Engineering and Applied Sciences, 14(22), 8452-8463.
  • Hilbert, M., Barnett, G., Blumenstock, J., Contractor, N., Diesner, J., Frey, S., ... & Zhu, J. J. (2019). Computational communication science: A methodological catalyzer for a maturing discipline.
  • Hilljegerdes, M., & Augustijn-Beckers, E. W. (2019, May). Evaluating the effects of consecutive hurricane hits on evacuation patterns in Dominica. In ISCRAM.
  • Hjorth, A. & Wilensky, U. (2019). Urban Planning-in-Pieces: A Computational Approach to Understanding Conceptual Change and Causal Reasoning about Urban Planning "Constructivist Foundations"
  • Hoffmann, B., Chalmers, K., Urquhart, N., & Guckert, M. (2019, February). Athos-A Model Driven Approach to Describe and Solve Optimisation Problems: An Application to the Vehicle Routing Problem with Time Windows. In Proceedings of the 4th ACM International Workshop on Real World Domain Specific Languages (pp. 1-10).
  • Hofstede, G. J., & Chappin, E. (2019, September). Archetypical Patterns in Agent-Based Models. In Conference of the European Social Simulation Association (pp. 313-332). Springer, Cham.
  • Hofstede, G. J., & Chappin, E. Archetypical Patterns in Agent-Based Models. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 313). Springer Nature.
  • Hofstede, G. J., Franco, E., Damen, F., & Fogliano, V. Healthy Snacks from Mom? An Agent-Based Model of Snackification in Three Countries. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 429). Springer Nature.
  • Holbert, N., & Wilensky, U. (2019). Designing Educational Video Games to Be Objects-to-Think-With. Journal of the Learning Sciences, 28(1), 32–72. https://doi.org/10.1080/10508406.2018.1487302 .
  • Hoyles, C. (2019). Micromundos, Construccionismo y Matemáticas1. Richard Noss y Celia Hoyles, Inglaterra❑ Significados, representaciones y lenguaje: las fracciones en tres generaciones de libros de texto para primaria Alicia Avila, México❑ Cómo trabajar la orientación espacial de modo significativo en Educación, 31(2), 7.
  • Huang, W. D., & Cui, Y. (2019). Effect of Individual Cognitive Behavior Model on Public Opinion Communication Mechanism based on Social Ecosystem. Ekoloji Dergisi, (107).
  • Huang, W., Cui, Y., & Xiao, X. (2019). Two-Way Mutual-Structure-Based Public Opinion Communication System: An Analysis with Simulation. Tehnički vjesnik, 26(1), 201-207.
  • Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., ... & McElhaney, K. (2019). C2STEM: a System for Synergistic Learning of Physics and Computational Thinking. Journal of Science Education and Technology, 1-18.
  • Hwang, Y. S., Lin, H. H., Pai, S. H., & Tu, C. H. (2019). Gpublocks: Gui programming tool for cuda and opencl. Journal of Signal Processing Systems, 91(3), 235-245.
  • Iapăscurtă, V. (2019). Detection of movement toward randomness by applying theblock decomposition method to a simple model of the circulatory system. Complex Systems, 28(1), 59-76.
  • Ilyinsky, A., & Goroshnikova, T. (2019, October). Navigation in NSR as Large-Scale System: Ship Path Analysis in Non-Severe Ice Condition. In 2019 Twelfth International Conference" Management of large-scale system development"(MLSD) (pp. 1-4). IEEE.[HTML]
  • Innocenti, E., Detotto, C., Idda, C., & Prunetti, D. (2019, April). Urban, agricultural and touristic land use patterns: combining spatial econometrics and ABM/LUCC. In 2019 4th World Conference on Complex Systems (WCCS) (pp. 1-6). IEEE.
  • Izquierdo, L. R., Izquierdo, S. S., & Sandholm, W. H. (2019). 0.4. The fundamentals of NetLogo. Agent-Based Evolutionary Game Dynamics.
  • Izquierdo, L. R., Izquierdo, S. S., & Sandholm, W. H. (2019). 2.1. Robustness and fragility. Agent-Based Evolutionary Game Dynamics.
  • Izquierdo, L. R., Izquierdo, S. S., Sandholm, W. H. (2019). An introduction to ABED: Agent-based simulation of evolutionary game dynamics. Games and Economic Behavior 118, 434-462. https://doi.org/10.1016/j.geb.2019.09.014
  • Jacildo, A. J., Rabajante, J. F., & Alcantara, E. P. (2019). Agent-based Modeling of Asian Corn Borer Resistance to BT Corn. bioRxiv, 795724.
  • Jalali, S. H., Vafaeinejad, A. R., Aghamohammadi, H., & Esmaeili Bidhendi, M. (2019). The Study of CO Symptoms' Impacts on Individuals, Using GIS and Agent-based Modeling (ABM). Pollution, 5(3), 463-471.
  • Javed, A. (2019). Understanding Malware Behaviour in Online Social Networks and Predicting Cyber Attacks (Doctoral dissertation, Cardiff University).
  • Jaxa-Rozen, M., Kwakkel.H.J, Bloemendal, M. (2019) A coupled simulation architecture for agent-based/geohydrological modelling with NetLogo and MODFLOW. Environmental Modelling and Software 115 (2019)19-37 [PDF]
  • Kabeer, M., Riaz, F., Jabbar, S., Aloqaily, M., & Abid, S. (2019, June). Real World Modeling and Design of Novel Simulator for Affective Computing Inspired Autonomous Vehicle. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 1923-1928). IEEE.
  • Kamarainen, A., Metcalf, S., Dickes, A., Gun-Yildiz, S., Brennan, K., Grotzer, T., & Dede, C. (2019, April). Impact of blended immersive virtual world and programming curriculum on student perspectives about scientific modeling. In Annual meeting program American Educational Research Association.
  • Kamimura, K., Gardiner, B., Dupont, S., Finnigan, J. (2019) Agent-based modelling of wind damage proceses and patterns in forests. Agricultural and Forest Meteorology 268 (2019) 279-288. [PDF]
  • Kaminski, Y., & Malinouski, I. (2019). MODELING OF TRAFFIC FLOW USING THE NETLOGO ENVIRONMENT. In Progress through Innovations (pp. 38-39).
  • Kapat, S. K., & Tripathy, S. N. (2019). Malware Architectural View with Performance Analysis in Network at Its Activation State. In Cognitive Informatics and Soft Computing (pp. 207-216). Springer, Singapore.
  • Kardas-sloma, L., Perozziello, A., Zahar, J. R., Lescure, X., Yazdanpanah, Y., & Lucet, J. (2019). Transmission d’Escherichia coli résistant aux β-lactamines (E. coli BLSE) dans la communauté: modélisation et évaluation de l’impact des interventions. Médecine et Maladies Infectieuses, 49(4), S30-S31.
  • Kaur, H., & Sharma, A. (2019). Sanction Enforcement for Norm Violation in Multi-agent Systems: A Cafe Case Study. In Proceedings of the 2nd International Conference on Data Engineering and Communication Technology (pp. 325-335). Springer, Singapore.
  • Khan, M. Y. A., Nasir, G., Ahmed, S., Tahir, M., & Ahmad, M. (2019, January). MLA based protocol for monitoring electrical parameters in Smart Gird using WSN. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-7). IEEE.
  • Khan, O., Din, A., Khalid, A., Khan, A., Ahmed, A., & Zia, K. (2019, December). Online Adversarial Coverage for Multi-Agent Systems. In 2019 15th International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE.
  • Kim, C., Jin, Y. G., Park, J., & Kang, D. (2019). A case study of a last-mile solution in a high-density residential neighborhood. Procedia Computer Science, 151, 132-138.
  • Kim, C., Jin, Y. G., Park, J., & Kang, D. (2019). The influence of an autonomous driving car operation on commuters’ departure times. Procedia Computer Science, 151, 85-91.
  • Kim, Y., Son, J., Lee, Y. S., Lee, M., Hong, J., & Cho, K. (2019). Integration of an individual-oriented model into a system dynamics model: an application to a multi-species system. Environmental Modelling & Software, 112, 23-35.
  • Kleiner, G., Rybachuk, M., & Ushakov, D. (2019, September). An investigation of social-behavioral phenomena in the peer-review processes of scientific foundations. In International Conference on Modelling and Simulation of Social-Behavioural Phenomena in Creative Societies (pp. 68-81). Springer, Cham.
  • Kocheril, G., Krebs, F., Nacken, L., & Holzhauer, S. Open and Integrative Modelling in Energy System Transitions—Conceptual Discussion About Model Reusability, Framework Requirements and Features. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 499). Springer Nature.
  • Komendant-Brodowska, A., Jager, W., Abramczuk, K., Baczko-Dombi, A., Fecher, B., Sokolovska, N., & Spits, T. Peek Over the Fence—How to Introduce Students to Computational Social Sciences. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 279). Springer Nature.
  • Kopeček, I., & Daňa, J. (2019). Modeling and Simulating Communication, Stress, and Productivity in Socio-Economic Structures. Slavonic Natural Language Processing in the 21st Century, 151.
  • Koralewski, T. E., Westbrook, J. K., Grant, W. E., & Wang, H. H. (2019). Coupling general physical environmental process models with specific question-driven ecological simulation models. Ecological modelling, 405, 102-105.
  • Kostylenko, O., Rodrigues, H., Torres, D. (2019) The spread of a financial virus through Europe and beyond. AIMS Mathematics, 86-98. [PDF]
  • Kponyo, J. J., Coker, K., Agyemang, J. O., & Der, J. (2019). An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach.
  • Lahav, O., Hagab, N., Levy, S. T., & Talis, V. (2019). Computer-model-based audio and its influence on science learning by people who are blind. Interactive Learning Environments, 27(5-6), 856-868.
  • Lahav, O., Kittany, J., Levy, S. T., & Furst, M. (2019). Perception of sonified representations of complex systems by people who are blind. Assistive Technology, 1-9.
  • Lee, J. S., & Wolf-Branigin, M. (2019) Innovations in Modeling Social Good: A Demonstration With Juvenile Justice Intervention Research on Social Work Practice. [HTML]
  • Legaspi, J., Canfield, C. I., Gill, K. S., Wyglinski, A. M., & Bhadai, S. V. (2020, May). Integrated Agent-Based Model for Broadband Resource Allocation Analysis. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (pp. 1-5). IEEE.
  • Li, J. (2019, October). Simulation Research on Marketing Effect of Enterprise in Social Network Based on SIR Model. In 4th International Conference on Modern Management, Education Technology and Social Science (MMETSS 2019). Atlantis Press.
  • Li, L., Xia, H., Zhang, R., & Li, Y. (2019, June). DDSEIR: A dynamic rumor spreading model in online social networks. In International Conference on Wireless Algorithms, Systems, and Applications (pp. 596-604). Springer, Cham.
  • Linares, S. (2019). Modelos del crecimiento urbano.
  • Line Have Musaeus, P. M. (2019).Computational Thinking in the Danish High School: Learning Coding, Modeling, and Content Knowledge with NetLogo. Proceedings of the SIGCSE '19 Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 913-919). Minneapolis, MN, USA [PDF]
  • Lines, T., & Basiri, A. (2019, November). Simulating and modeling the signal attenuation of wireless local area network for indoor positioning. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (pp. 9-15).
  • Lippe, M., Bithell, M., Gotts, N. Natalie, D., Barbrook, P., Giupponi, C., Hallier, M., Hofsted, G., Le Page, C., Matthews, R., Schluter, M., Smith, P., Teglio, A. & Thellman, K (2019). Using agent-based modelling to simulate social-ecological systems across scales. GeoInformatica 23.2, 269-298.
  • Liu, G., Casazza, M., & Lega, M. (2019). Simulation of coupled impact-management response scenarios for distributed wastewater environmental discharges at basin scale through urban environmental risk network transmission mechanism. Journal of environmental management, 236, 182-194.
  • Liu, G., Ye, J., & Argyres, C. (2019). Modeling and simulation of the knowledge growth process among new energy technology firms in the distributed innovation network. DYNA-Ingeniería e Industria, 95(1).
  • Liu, Y., Li, F. & Su, Y.(2019) Critical Factors Influencing the Evolution of Companies’ Environmental Behavior: An Agent-Based Computational Economic Approach SAGE Open. [PDF]
  • Lorenz, W., & Wurzer, G. (2019). Visual Representation of Adjacencies-A NetLogo application to turn functional matrices into bubble diagrams.
  • Lorenz, W., & Wurzer, G. (2019). Visual Representation of Adjacencies. In Architecture in the Age of the 4th Industrial Revolution Proceedings of the 37th eCAADe and 23rd SIGraDi Conference Volume (Vol. 2, pp. 11-20).
  • Lorig, F. (2019). Hypothesis-Driven Simulation Studies. In Hypothesis-Driven Simulation Studies (pp. 137-170). Springer Vieweg, Wiesbaden.
  • Lovellette, E., Hexmoor, H., & Rodriguez, K. (2019). Automated argumentation for collaboration among cyber-physical system actors at the edge of the Internet of Things. Internet of Things, 5, 84-96.
  • Lu, P. (2019). Heterogeneity, judgment, and social trust of agents in rumor spreading. Applied Mathematics and Computation, 350, 447-461.
  • Lu, P., Deng, L., & Liao, H. (2019). Conditional effects of individual judgment heterogeneity in information dissemination. Physica A: Statistical Mechanics and its Applications, 523, 335-344.
  • Luanda, A. (2019, June). A Gift-Exchange Model for the Maintenance of Group Cohesion in a Telecommunications Scenario. In Distributed Computing and Artificial Intelligence, 16th International Conference (Vol. 1003, p. 189). Springer.
  • Lumbreras Sancho, S., Wogrin, S., Navarro Llevat, G., Bertazzi, I., & Pereda García, M. (2019). A decentralized solution for transmission expansion planning: getting inspiration from nature.
  • MA Mahmoud, R., M Abdel Karim, N., & MA Youssef, A. (2019). Comparative analyses of computational implementations for healthcare building design. JES. Journal of Engineering Sciences, 47(5), 627-643.
  • MacCarthy, E. A. (2019). Modeling the Effect of Contact Rates on Infectious Diseases in Contact Networks. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 6(8), 10556-10560.
  • Mahdizadeh Gharakhanlou, N., & Mesgari, M. S. (2019). DEVELOPING A CELLULAR AUTOMATA MODEL FOR SIMULATING RAINFALL-RUNOFF PROCESS (CASE STUDY: BABOL CATCHMENT). International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
  • Mailleret, L., Davtian, D., & Grognard, F. (2018, July). An individual based model to optimize natural enemies deployment in augmentative biological control. In 11th European Conference on Mathematical and Theoretical Biology (ECMTB).
  • Malaina, A.(2019) The Paradigm of Complexity in Sociology: Epistemological and Methodological Implications, Complexity Applications.Language and Communication Sciences, 31-42. [HTML]
  • Malinowski, A., & Czarnul, P. (2019). Multi-agent large-scale parallel crowd simulation with nvram-based distributed cache. Journal of Computational Science, 33, 83-94.
  • Marcum-Dietrich, N., Bruozas, M., & Staudt, S. (2019). Precipitating Change: Integrating Meteorology, Mathematics, and Computational Thinking: Research on Students' Learning and Use of Data, Modeling, and Prediction Practices for Weather Forecasting. In International Society for Technology in Education (ISTE) Conference, Philadelphia, PA.
  • Martin (2019) Future Visions for the study of Group Learning with Complex Systems Models: The role of multimodal data in group learning analysis. Computer Supported Collaborative Learning, Lyon, France.
  • Martin, K., Horn, M., & Wilensky, U. (2019). Constructivist Dialogue Mapping of learning in Museums and Informal Spaces. Paper Accepted to the Journal of Education Informatics, Vilnius, Lithuania
  • Martin, K., Horn, M., & Wilensky, U. (2019). Emergent Schema Learning in Short Museum Interactions. Paper Accepted to the Journal of Education Informatics, Vilnius, Lithuania.
  • Martin, K., Horn, M., & Wilensky, U. (2019). Learning Complexity through Open-Ended Agent Based Modeling. Paper Submitted to IDC 2019 Annual International conference, Boise, Idaho.
  • MARTIN, K., HORN, M., & WILENSKY, U. (2019). Prevalence of Direct and Emergent Schema and Change after Play. Informatics in Education, 18(1), 183-212.
  • Martin, K., Wang, E. Q., Bain, C., & Worsley, M. (2019, October). Computationally augmented ethnography: Emotion tracking and learning in museum games. In International Conference on Quantitative Ethnography (pp. 141-153). Springer, Cham.
  • Mayes, R. (2019). Quantitative reasoning and its rôle in interdisciplinarity. In Interdisciplinary Mathematics Education (pp. 113-133). Springer, Cham.
  • Mayfield, J., & Mayfield, M. (2019). The diffusion process of strategic motivating language: An examination of the internal organizational environment and emergent properties. International Journal of Business Communication, 56(3), 368-392.
  • McGowan, L., & Westgren, R. A Study of Group Formation Using Agent-Based Modeling. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 115). Springer Nature.
  • McNulty, M., Smith, J.D., Villamar, J., Burnett-Zeigler, I., Vermeer, W., Benbow, N., Gallo, C., Wilensky, U., Hjorth, A., Mustanski, B., Schneider, C. & Brown, H. (2019). Implementation Research Methodologies for Achieving Scientific Equity and Health Equity. Ethnicity & Disease, 29(1), 83-92
  • Mertens, A., Feliciani, T., Heidari, S., Siebers, P. O., & Dignum, F. Are We Done Yet? or When is Our Model Perfect (Enough)?. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 285). Springer Nature.
  • Meskini, F. Z., & Aboulaich, R. (2019, October). Multi-agent based simulation of a smart insurance using blockchain technology. In 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS) (pp. 1-6). IEEE.
  • Milne, R. J., Cotfas, L. A., Delcea, C., Salari, M., Craciun, L., & Molanescu, A. G. (2019). Greedy method for boarding a partially occupied airplane using apron buses. Symmetry, 11(10), 1221.
  • Milne, R. J., Delcea, C., Cotfas, L. A., & Salari, M. (2019). New methods for two-door airplane boarding using apron buses. Journal of Air Transport Management, 80, 101705.
  • Mittal, A., Gibson, N. O., & Krejci, C. C. (2019, December). An agent-based model of surplus food rescue using crowd-shipping. In 2019 Winter Simulation Conference (WSC) (pp. 854-865). IEEE.
  • Mohammed, R., Kennedy-Clark, S., & Reimann, P. (2019). Using immersive and modelling environments to build scientific capacity in primary preservice teacher education. Journal of Computers in Education, 6(4), 451-481.
  • Montes, D. O., Suppi, R., De Giusti, L. C., Leporace, M., Micieli, M. V., Santini, M. S., & Naiouf, M. (2019). Simulación de altas prestaciones (GPU) para la reproducción del mosquito Aedes aegypti en el cementerio de Santo Tomé, Corrientes. In XXV Congreso Argentino de Ciencias de la Computación (CACIC)(Universidad Nacional de Río Cuarto, Córdoba, 14 al 18 de octubre de 2019).
  • Morelle, K., Buchecker, M., Kienast, F., & Tobias, S. (2019). Nearby outdoor recreation modelling: An agent-based approach. Urban Forestry & Urban Greening, 40, 286-298.
  • Mostafizi, A., Wang, H., & Dong, S. (2019). Understanding the multimodal evacuation behavior for a near-field tsunami. Transportation research record, 2673(11), 480-492.
  • Mozahem, N. A. (2019). Always negotiate, sometimes cooperate: an agent-based model. International Journal of Organization Theory & Behavior.
  • Muraru, A., Lile, R., Boșcoianu, E. C., Boșcoianu, M., Vladareanu, L. (2019). The UAV control approach by using multi agent systems. Periodicals of Engineering and Natural Science, vol. 7, no. 1.
  • Musaeus, L. H., & Musaeus, P. (2019, February). Computational Thinking in the Danish High School: Learning Coding, Modeling, and Content Knowledge with NetLogo. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 913-919).
  • Naili, M., Bourahla, M., & Naili, M. (2019). Stability-based model for evacuation system using agent-based social simulation and Monte Carlo method. International Journal of Simulation and Process Modelling, 14(1), 1-16.
  • Ng, H., Othman, W., Bakar, E., Mat Noor, N., & Hawary, A. (2019).[HTML] MEERKATS BEHAVIOR MODELLING USING NETLOGO. ROBOTIKA, 1(1), 16-21.
  • Nie, X. X., Bai, C., & Zhang, J. (2019). Simulation research on the effectiveness of a multiagent mine safety supervision system and its verification. Mathematical Problems in Engineering, 2019.
  • Norouziasl, S., Jafari, A., & Wang, C. (2019). Analysis of lighting occupancy sensor installation in building renovation using agent-based modeling of occupant behavior. In Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation (pp. 593-601). Reston, VA: American Society of Civil Engineers.
  • Noss, R., & Hoyles, C. (2019). Microworlds, Constructionism and Maths. Educación matemática, 31(2), 7-21.
  • O’Connor, J. An Analysis of the Various Factors Involved in the Evolution of Mimicry Using an Agent Based Programming Language and of the Body Massesand Reproduction Rates of Existing Mimetic Species.
  • Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A. A., Burgess, K. J., & Guo, L. (2019). Emerging technologies as pedagogical tools for teaching and learning science: A literature review. Human Behavior and Emerging Technologies, 1(2), 149-160.
  • Opiyo, N. (2019, October). Neighbourhood influence and social acceptance of PV systems in rural developing communities. In 36th European Photovoltaic Solar Energy Conference (pp. 2006-2012). International Solar Energy Society.
  • Opiyo, N. N. (2019). Impacts of neighbourhood influence on social acceptance of small solar home systems in rural western Kenya. Energy Research & Social Science, 52, 91-98.
  • Owusu, P. A., Leonenko, V. N., Mamchik, N. A., & Skorb, E. V. (2019). Modeling the growth of dendritic electroless silver colonies using hexagonal cellular automata. Procedia Computer Science, 156, 43-48.
  • Ozawa, S., Haynie, D., Bessias, S., Laing, S., Ladi, E. (2019) Modeling the Economic Impact of Substandard and Fƒalsified Antimalarials in the Democratic Republic of the Congo. The American Journal of Tropical Medicine and Hygiene.
  • Palau, A. S., Dhada, M. H., & Parlikad, A. K. (2019). Multi-agent system architectures for collaborative prognostics. Journal of Intelligent Manufacturing, 30(8), 2999-3013.
  • Park, J. W., & Arteaga, C. (2019). Human Responses of Emergency Evacuation Using Agent-Based Modeling. In International Conference on Smart Cities, Seoul, Korea (pp. 1-6).
  • Park, J., Redwine, J., Hill, T. D., & Kotun, K. (2019). Water resource and ecotone transformation in coastal ecosystems. Ecological Modelling, 405, 69-85.
  • Párraga-Álava, J., Garzón, G. M., & Valarezo, R. V. (2019). Multi-Objective Genetic Algorithms: are they useful for tuning parameters in Agent-Based Simulation?. Revista Ibérica de Sistemas e Tecnologias de Informação, (E19), 172-184.
  • Peel, A., Dabholkar, S., Anton, G., Horn, M., & Wilensky, U. (2019) Teachers’ professional growth through co-design and implementation of computational thinking (CT) integrated biology units. Annual Meeting of the Association of Science Teacher Education (ASTE) 2019. San Antonio, TX.
  • Peel, A., Dabholkar, S., Granito, T. (2019) Teaching Experimental Design with Computational Thinking. Poster presented at the National Association of Biology Teachers (NABT). 2019 November 14-17; Chicago, IL.
  • Pereira, A. I., Barbosa, J., & Leitao, P. (2019). Hybrid System for Simultaneous Job Shop Scheduling and Layout Optimization Based on Multi-agents and Genetic Algorithm. In Hybrid Intelligent Systems: 18th International Conference on Hybrid Intelligent Systems (HIS 2018) Held in Porto, Portugal, December 13-15, 2018 (Vol. 923, p. 387). Springer.
  • Perez, L., Dragicevic, S., & Gaudreau, J. (2019). A geospatial agent-based model of the spatial urban dynamics of immigrant population: A study of the island of Montreal, Canada. PloS one, 14(7).
  • Petrosino, A. J., Sherard, M. K., Harron, J. R., Brady, C. E., Stroup, W. M., & Wilensky, U. J. (April, 2019). Developing preservice teachers’ conceptualization of models and simulations through group-based cloud computing. Poster presented at the American Education Research Association Annual Meeting, Toronto, Canada
  • Petrosino, T., Sherard, M., & Brady, C. (2019). Using Collaborative Agent-based Modeling to Explore Complex Phenomena with Elementary Preservice Science Teachers. Poster session presented at Computer Supported Collaborative Learning, Lyon, France.
  • Phetheet, J., Heger, W., & Hill, M. C. (2019, December). Evaluating Use of Water and Renewable Energy in Agricultural Areas: A Coupled Simulation of DSSAT and Agent-Based Modeling. In AGU Fall Meeting 2019. AGU.
  • Piccione, A., et al. (2019). An Agent-Based Simulation API for Speculative PDES Runtime Environments. 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Chicago, Il, USA, June 03-05. New York, NY, USA: ACM.
  • Plikynas, D., Laužikas, R., Sakalauskas, L., Miliauskas, A., & Dulskis, V. (2019, September). Agent-based simulation of cultural events impact on social capital dynamics. In Proceedings of SAI Intelligent Systems Conference (pp. 1138-1154). Springer, Cham.
  • Plikynas, D., Miliauskas, A., & Laužikas, R. (2019, December). Simulation of Social Capital Dynamics: Impact of Cultural Events. In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence (pp. 315-319).
  • Ponsiglione, C., Primario, S., & Zollo, G. (2019). Does natural language perform better than formal systems? Results from a fuzzy agent-based model. International Journal of Technology, Policy and Management, 19(2), 171-195.
  • Portocarrero Sarmento, R. (2019). Inventory Management-A Case Study with NetLogo. arXiv preprint arXiv:1905.08041.
  • Putra, H. C., Andrews, C. J., & Senick, J. A. Modeling building occupant behavior during load shedding. Management, 23(15), 3267-95.
  • Quan, J., & Liu, Y. (2019, January). Construction and Simulation Analysis of Cooperative Game Model of Hospital Group in Medical Waste Stream System. In 2018 International Conference on Mathematics, Modeling, Simulation and Statistics Application (MMSSA 2018). Atlantis Press
  • R Izquierdo, L., S Izquierdo, S., & H Sandholm, W. (2019). Agent-Based Evolutionary Game Dynamics. Independent.
  • Raglin, A., & Metu, S. (2019, May). Agent based simulation of decision making with uncertainty. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications (Vol. 11006, p. 110060M). International Society for Optics and Photonics.
  • Rai, S., Carter, T., & Sharma, B. (2019). Using NetLogo to simulate building occupancy of a university building. ASEE 2019 Annual Conference.
  • Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: a practical introduction. Princeton university press.
  • Raimbault, J., & Pumain, D. (2019). Methods for exploring simulation models. Geographical Modeling: Cities and Territories, 2, 125-150.
  • Ramazanov, R. (2019). Agent-based modeling the distribution of authorities between the levels of the state. Artificial societies, 14(3).
  • Ramazanov, R. (2019). Simulation analysis of China fiscal models. Artificial Societies, 14(4).
  • Ramos, A., Calado, M., & Antunes, L. (2019, June). A Gift-Exchange Model for the Maintenance of Group Cohesion in a Telecommunications Scenario. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 189-196). Springer, Cham.
  • Rao, V. A. (2019). Distributed data-gathering protocols in AD-HOC wireless networks (Doctoral dissertation, IIT Delhi).
  • Rengifo, M. G. H., & Díaz-Ambrona, C. G. H. (2019). Comportamiento demográfico: Dinámico–Probabilístico de los pueblos indígenas en aislamiento de la amazonía ecuatoriana. Revista Científica Axioma, (20), 25-34.
  • Reuillon, R., Leclaire, M., Raimbault, J., Arduin, H., Chapron, P., Chérel, G., ... & Perret, J. (2019, September). Fostering the use of methods for geosimulation models sensitivity analysis and validation
  • Rey-Coyrehourcq, S., Banos, A., & Raimbault, J. (2019, October). Le calcul intensif en géographie: une tradition bien ancrée. In JCAD Journées Calcul et Données 2019.
  • Reynolds, E. R., Himmelwright, R., Sanginiti, C., & Pfaffmann, J. O. (2019). An agent-based model of the Notch signaling pathway elucidates three levels of complexity in the determination of developmental patterning. BMC systems biology, 13(1), 1-16.
  • Roach, A. R., Dennison, E. M., Hyrich, K. L., & MacGregor, A. J. (2019). O23 Using big data in the design and validation of a simulation of the healthcare system for patients with inflammatory rheumatic disease: results from the SiMSK study. Rheumatology, 58(Supplement_3), kez105-022.
  • Roach, A. R., Dennison, E. M., Hyrich, K. L., & MacGregor, A. J. (2019). O24 The impact of early referral and lowering clinical thresholds of biologic access on the disease course and costs in RA: results from the SiMSK study. Rheumatology, 58(Supplement_3), kez105-023.
  • Rodemann, T., Eckhardt, T., Unger, R., & Schwan, T. (2019). Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems. Energies, 12(15), 2858.
  • Romanowska, I., Crabtree, S., Harris, K., & Davies, B. (2019). Agent-Based Modeling for Archaeologists: Part 1 of 3. Advances in Archaeological Practice, 7(2), 178-184. [HTML]
  • Rozo, K. R., Arellana, J., Santander-Mercado, A., & Jubiz-Diaz, M. (2019). Modelling building emergency evacuation plans considering the dynamic behaviour of pedestrians using agent-based simulation. Safety science, 113, 276-284.
  • Sahnoun, M. H., Baudry, D., Mustafee, N., Louis, A., Smart, P. A., Godsiff, P., & Mazari, B. (2019). Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system. Journal of Intelligent Manufacturing, 30(8), 2981-2997.
  • Salecker, J., Sciaini, M., Meyer, K. M., & Wiegand, K. (2019). The nlrx R package: A next‐generation framework for reproducible NetLogo model analyses. Methods in Ecology and Evolution.[PDF]
  • Salt, D., & Polhill, G. A NetLogo Extension to Secure Data Using GNUs Pretty Good Privacy Software Suite. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 299). Springer Nature.
  • Sanginiti, C., Pfaffmann, J. O., Reynolds, E. R., & Himmelwright, R. An agent-based model of the Notch signaling pathway elucidates three levels of complexity in the determination of developmental patterning.
  • Santos, F., Nunes, I., & Bazzan, A. L. (2020). Quantitatively assessing the benefits of model-driven development in agent-based modeling and simulation. Simulation Modelling Practice and Theory, 104, 102126.
  • Sapienza, A., & Falcone, R. (2019, June). Social Recommendations: Have We Done Something Wrong?. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 281-284). Springer, Cham.
  • Saputro, N. (2019, December). Game-Theoretic and Genetic-Based Approach for Cooperative Mission-Oriented Swarms of Drones. In 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE) (pp. 163-168). IEEE.
  • Sarmento, R. P. (2019). Inventory Management-A Case Study with NetLogo. arXiv preprint arXiv:1905.08041
  • Saxena, N. (2019). A Designer’s Reflections on Designing for ‘Productive Failure’.
  • Saxena, N. (2019). Pallas Advanced Learning Systems–A research-informed virtual learning kit. Journal of Applied Learning and Teaching, 2(1), 65-68.
  • Scheller, S. (2019). Steven F. Railsback and volker grimm, agent-based and individual-based modeling. a practical introduction. Œconomia. History, Methodology, Philosophy, (9-2), 407-413.
  • Scheller, S. (2019). Steven F. Railsback and Volker Grimm, Agent-Based and Individual-Based Modeling. A Practical Introduction. Œconomia. History, Methodology, Philosophy, (9-2), 407-413.
  • Scherjon, F., Romanowska, I., & Lambers, K. (2019). Digitally Teaching Digital Skills: Lessons Drawn from a Small Private Online Course (SPOC) on ‘Modelling and Simulation in Archaeology’ at Leiden University. Journal of Computer Applications in Archaeology, 2(1), 79–88. doi: 10.5334/jcaa.26
  • Schmidt, A. H., & Zhang, K. (2019). Agent-Based Modelling: A New Tool for Legal Requirements Engineering: Introduction and Use Case (KEI). European Quarterly of Political Attitudes and Mentalities, 8(1), 1-21.
  • Schneider, R., & Kouros, J. (2019). Effiziente Programmierung sozialwissenschaftlicher Modelle. In Simulieren und Entscheiden (pp. 231-256). Springer VS, Wiesbaden.
  • Schwan, T., Unger, R., & Eckhardt, T. FMI-Based Co-Simulation of Multi-Agent Occupants Models, with Modelica Building and HVAC System Models.
  • Sciullo, A., Vallino, E., Iori, M., & Fontana, M. (2019). Paths and processes in complex electricity markets: The agent-based perspective. In Routledge Handbook of Energy Economics (pp. 522-533). Routledge.
  • Sengupta, P., Kim, B., & Shanahan, M. C. (2019). Playfully coding science: Views from preservice science teacher education. In Critical, Transdisciplinary and Embodied Approaches in STEM Education (pp. 177-195). Springer, Cham.
  • Serrano, E., & Satoh, K. (2019, November). An agent-based model for exploring pension law and social security policies. In JSAI International Symposium on Artificial Intelligence (pp. 50-63). Springer, Cham.
  • Sharma, A. K., & Asirwatham, L. (2019). Learning by Computing: A First Year Honors Chemistry Curriculum. In Using Computational Methods To Teach Chemical Principles (pp. 127-138). American Chemical Society.
  • Shen, Y., Guo, Y., & Chen, W. (2019). Safety analysis of China’s marine energy channel based on Multi-Agent simulation. Energy Procedia, 158, 3259-3264.
  • Sherin, B. (2019). Machine Learning and the Perils of Prolific Pattern Finding. Constructivist Foundations, 14(3), 285-287.
  • Shirazi, E. & Jadid, S.(2019) A multiagent design of self-healing in eletric power distribution systems. Electric Power Systems Research. [PDF]
  • Shirkhodaie, G., & Rahman, A. (2019). Investigating the Effects of the Spread of Contagious Disease and Immunization of Population on Social Welfare Using Agent Based Modeling. Social Welfare Quarterly, 18(70), 181-208.
  • Shvarts, A., & Abrahamson, D. (2019). Dual-eye-tracking Vygotsky: A microgenetic account of a teaching/learning collaboration in an embodied-interaction technological tutorial for mathematics. Learning, Culture, and Social Interaction, 22, 100316. https://doi.org/10.1016/j.lcsi.2019.05.003
  • Silva, J., Varela, N., & Lezama, O. B. P. (2019, September). Optimizing Street Mobility Through a NetLogo Simulation Environment. In International Conference On Computational Vision and Bio Inspired Computing (pp. 48-55). Springer, Cham.
  • Slate, J. E., Adler, R. F., & Hibdon, J. E. (2019). Scott T. Mayle, Hanna Kim, and Sudha Srinivas Northeastern Illinois University. Integrating Digital Technology in Education: School-University-Community Collaboration, 55.
  • Smart, H. (2019). Operationalizing a conceptual model of colorism in local policing. Social justice research, 32(1), 72-115.
  • Smarzhevskiy, I. (2019). Behaviour in Hierarchy NetLogo Model. Description, ODD Documentation, Result. Description, ODD Documentation, Result (September 9, 2019).
  • Smarzhevskiy, I. A. (2019, November). The Distribution Model of Social and Psychological Properties on a Dynamic Set of Agents. In The International Scientific and Practical Forum “Industry. Science. Competence. Integration” (pp. 376-382). Springer, Cham.
  • Spitznagel, B., Weigal, J., & Rodriguez, J. (2019). Visualizing Viscous Flow and Diffusion in the Circulatory System. The Physics Teacher, 57(8), 529-532.
  • Sukarno, S. A. (2019). Méthodes d'approximation au problème de routage de véhicule pour une gestion de flotte de drones (Doctoral dissertation, Valenciennes, Université Polytechnique Hauts-de-France).
  • Sulis, E., Amantea, I. A., & Fornero, G. (2019, December). Risk-aware business process modeling: a comparison of discrete event and agent-based approaches. In 2019 Winter Simulation Conference (WSC) (pp. 3152-3159). IEEE.
  • Sultanov, M., & Crape, B. L. (2019). PIN135 MODELING HUMAN PAPILLOMAVIRUS TRANSMISSION FOR VACCINE EVALUATIONS: A PRELIMINARY AGENT-BASED MODEL. Value in Health, 22, S661.
  • Sun, B. (2019). Technology Innovation Diffusion Mechanisms of Agricultural Machinery in an Innovation Ecosystem. Revista de la Facultad de Agronomia de la Universidad del Zulia, 36(5).
  • Sun, J., Zheng, M., Skitmore, M., Xia, B., & Wang, X. (2019). Industry effect of job hopping: an agent-based simulation of Chinese construction workers. Frontiers of Engineering Management, 6(2), 249-261.
  • Swanson H., Anton G., Bain C., Horn M., Wilensky U. (2019) Introducing and Assessing Computational Thinking in the Secondary Science Classroom. In: Kong SC., Abelson H. (eds) Computational Thinking Education. Springer, Singapore
  • Swanson, H., & Wilensky, U. (2019, August). Engaging students in theory building in the science classroom. Poster to be presented at the biennial conference of the European Association for Research on Learning and Instruction, Aachen, Germany.
  • Taillandier, P., Gaudou, B., Grignard, A., Huynh, Q. N., Marilleau, N., Caillou, P., ... & Drogoul, A. (2019). Building, composing and experimenting complex spatial models with the GAMA platform. GeoInformatica, 23(2), 299-322.
  • Taillandier, P., Grignard, A., Marilleau, N., Philippon, D., Huynh, Q. N., Gaudou, B., & Drogoul, A. (2019). Participatory modeling and simulation with the gama platform. Journal of Artificial Societies and Social Simulation, 22(2).
  • Tan, Z., Othman, W. A. F. W., Wahab, A. A. A., & Alhady, S. S. N. (2019). CROWD DYNAMICS ANALISYS: SIMULATING HETEREOGENEOUS CROWDS WITH PANIC EFFECT STOCHASTICS BEHAVIOUR. Journal of Fundamental and Applied Sciences, 11(2), 838-856.
  • Tashakor, G., & Suppi, R. (2019, July). Simulation and computational analysis of multiscale graph agent-based tumor model. In 2019 International Conference on High Performance Computing & Simulation (HPCS) (pp. 298-304). IEEE.
  • Tashakor, G., & Suppi, R. (2019). Agent-based model for tumour-analysis using Python+ Mesa. arXiv preprint arXiv:1909.01885.
  • Taves, A. (2019). Modeling Theories and Modeling Phenomena: A Humanist’s Initiation. In Human Simulation: Perspectives, Insights, and Applications (pp. 83-94). Springer, Cham.
  • Thiel, S. (2019). An Agent-Based Ecological Model of West Nile Virus for Classroom Use.
  • Todorova, V. (2019). Playing with Information Source. In At the Intersection of Language, Logic, and Information: ESSLLI 2018 Student Session, Sofia, Bulgaria, August 6–17, 2018, Selected Papers 30 (pp. 171-184). Springer Berlin Heidelberg.
  • Tofah, D. K. W. (2019). Production and Logistics Systems Improvements-Biim Ultrasound AS (Master's thesis, UiT Norges arktiske universitet).
  • Triastanto, A. N. D., & Utama, N. P. (2019, September). Model Study of Traffic Congestion Impacted by Incidents. In 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (pp. 1-6). IEEE.
  • Tubadji, A., Angelis, V., & Nijkamp, P. (2019). Micro-cultural preferences and macro-percolation of new ideas: A NetLogo simulation. Journal of the Knowledge Economy, 10(1), 168-185
  • Urbanová, P. (2019). Agent based modeling of fish shoal behaviour.
  • Vahdati, A. R. (2019). Agents. jl: agent-based modeling framework in Julia. Journal of Open Source Software, 4(42), 1611.
  • Valdez, A. C., & Ziefle, M. (2019). Predicting acceptance of novel technology from social network data-an agent-based simulation-approach. In International Conference on Competitive Manufacturing (COMA), CIRP. Stellenbosch, South Africa.
  • Valente, J. A. (2019). The Role of Debugging in Knowledge Construction. Constructivist Foundations, 14(3).
  • Varughese, J. C., Moser, D., Thenius, R., Wotawa, F., & Schmickl, T. (2019). swarmfstaxis: Borrowing a swarm communication mechanism from fireflies and slime mold. In Complex Adaptive Systems (pp. 213-222). Springer, Cham.
  • Verwaart, T., van Wassenaer, L., & Hofstede, G. J. Agent-Based Simulation of Policies to Reconnect a City and the Countryside. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 101). Springer Nature.
  • Vogelstein, L., & Brady, C. (2019). Taking the Patch Perspective: A Comparative Analysis of a Patch Based Participatory Simulation.
  • Wagner, M., & de Vries, W. T. (2019). Comparative review of methods supporting decision-making in urban development and land management. Land, 8(8), 123.
  • Waldherr, A., & Wettstein, M. (2019). Bridging the gaps: using agent-based modeling to reconcile data and theory in computational communication science. International Journal of Communication, 13, 3976-3999.
  • Walker, B., & Johnson, T.V.(2019) NetLogo and GIS: A Powerful Combination EPiC Series in Computing, vol 58, 257-264. [PDF]
  • Wang, H. H., Grant, W. E., Elliott, N. C., Brewer, M. J., Koralewski, T. E., Westbrook, J. K., ... & Sword, G. A. (2019). Integrated modelling of the life cycle and aeroecology of wind-borne pests in temporally-variable spatially-heterogeneous environment. Ecological modelling, 399, 23-38.
  • Warnke, T., & Uhrmacher, A. M. (2019, October). Reproducible parallel simulation experiments via pure functional programming. In 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT) (pp. 1-8). IEEE.
  • Warren, A., & Sattenspiel, L. (2020). Artificial Long House Valley v1. 0.0. CoMSES Computational Model Library.
  • Weintrop, D., & Wilensky, U. (2019). Transitioning from introductory block-based and text-based environments to professional programming languages in high school computer science classrooms. Computers & Education, 142.
  • Weintrop, D., Bau, D., & Wilensky U. (2019). The Cloud is the Limit: A case study of programming on the web, with the web. International Jounal of Child-Computer Interaction. [PDF]
  • Wells, L., Bednarz, T., & Au, A. (2019). Applying reinforcement learning techniques to operations-level wargaming scenarios. In 23rd International Congress on Modelling and Simulation-Supporting evidence-based decision making: the role of modelling and simulation (pp. 74-74).
  • Wen, G., Huang, N., & Zhu, J. (2019, November). A node-centric network congestion estimation method considering average spatio-temporal scale. In Journal of Physics: Conference Series (Vol. 1345, No. 4, p. 042062). IOP Publishing.[HTML]
  • Wijermans, N., Verhagen, H., & Lytter, A. An Online Implementation of a Virtual Agent-Based Experiment Tool—An Exploration. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 291). Springer Nature.
  • Witwytzkyj, J., Valle Filho, A., & Santana, A. L. M. (2019, November). Modelagem Bifocal Aplicada à Engenharia Mecânica: Desenvolvimento de um modelo computacional de condução térmica para uso educacional no ensino superior. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação (Vol. 8, No. 1, p. 1084).
  • Wozniak, M. Conceptual Framework for Modeling Complex Urban Systems—From Theoretical Assumptions to Empirical Basis. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 509). Springer Nature.
  • Wyeld, T., Shen, H., & Bednarz, T. (2019, November). Containerisation as a method for supporting multiple VR visualisation platforms from a single data source. In The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry (pp. 1-3).
  • Xanthopoulou, T. D., Prinz, A., & Shults, F. L. (2019, September). Generating Executable Code from High-Level Social or Socio-Ecological Model Descriptions. In International Conference on System Analysis and Modeling (pp. 150-162). Springer, Cham.
  • Xi, J. Y. S., & Chan, W. K. V. (2019, December). Simulation of knife attack and gun attack on university campus using agent-based model and GIS. In 2019 Winter Simulation Conference (WSC) (pp. 263-272). IEEE.
  • Xia, H., Zhang, S., Li, Y., Pan, Z., Peng, X., & Cheng, X. (2019) "An Attack-Resistant Trust Inference Model for Securing Routing in Vehicular Ad Hoc Networks," in IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 7108-7120, July 2019. doi: 10.1109/TVT.2019.2919681
  • Xiang, L., & Mitchell, A. (2019). Investigating bark beetle outbreaks. Science Scope, 42(6), 65-76.
  • Yang, B. (2019). Machine learning-based evolution model and the simulation of a profit model of agricultural products logistics financing. Neural Computing and Applications, 31(9), 4733-4759.
  • Yang, B., & Chen, Y. A. (2019). Evolution model and simulation of logistics outsourcing for manufacturing enterprises based on multi-agent modeling. Cluster Computing, 22(3), 6807-6815.
  • Yang, T., Gao, W., & Zhang, F. (2019, September). Summary of Research on Power Boosting Technology of Distributed Mobile Energy Storage Charging Piles. In 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC) (pp. 1770-1776). IEEE.
  • Ye, X., Chen, B., Li, P., Jing, L., & Zeng, G. (2019). A simulation-based multi-agent particle swarm optimization approach for supporting dynamic decision making in marine oil spill responses. Ocean & Coastal Management, 172, 128-136.
  • Yee, G. Q. M. (2019). Self-assembly for supply chains.
  • Yongchen, G., & Yang, S. (2019). Multi-Agent Modeling and Simulation on China's Marine Energy Channel Security. Journal of System Simulation, 31(4), 655.
  • Young, E. (2019). Prioritevac, An Adaptive Model for Evacuation: Agent Based Simulation of the Station Nightclub Fire (Doctoral dissertation, University of Delaware).
  • Yousefi, M., & Yousefi, M. (2019). Human resource allocation in an emergency department: A metamodel-based simulation optimization. Kybernetes.
  • Yu, B., Guo, Z., Peng, Z., Wang, H., Ma, X., & Wang, Y. (2019). Agent-based simulation optimization model for road surface maintenance scheme. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04018065.
  • Yue, T., Long, R., Chen, H., Liu, J., Liu, H., & Gu, Y. (2019). Energy-Saving Behavior of Urban Residents in China: A Multi-Agent Simulation. Journal of Cleaner Production, 119623.[HTML]
  • Yuhong, Z. H. A. O., & Jie, C. H. E. N. (2019, October). A P2P trust model based on trust factor and feedback aggregation. In 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE) (pp. 214-219). IEEE.
  • Zhang, E. Z., & Zhang, X. (2019, July). Road traffic congestion detecting by VANETs. In Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019).
  • Zhang, N., & Zheng, X. (2019). Agent-based simulation of consumer purchase behaviour based on quality, price and promotion. Enterprise Information Systems, 13(10), 1427-1441.
  • Zhao, G., Zhao, R., & Yang, X. (2019). Fostering Fifth-Grade Students’ Ability to Design Comparative Experiments Using an Online Model. Journal of Education and Development, 3(1), 1.
  • Zhou, Y., & Wu, H. (2019). Dynamic analysis and simulation study of knowledge flow under the perspective of industrial transfer. Expert Systems, 36(5), e12345.
  • Zhu, H. (2019, May). A Platoon-based Rolling Optimization Algorithm at Isolated Intersections in A Connected and Autonomous Vehicle Environment. In Proceedings of the 2019 5th International Conference on Computing and Data Engineering (pp. 41-46).
  • Zia, K., Saini, D. K., & Muhammad, A. (2019). Efficient evacuation in a multi-exit environment: an agent-based decision support model. International Journal of Information and Decision Sciences, 11(4), 355-375.
  • Zoto, E., Kianpour, M., Kowalski, S. J., & Lopez-Rojas, E. A. (2019). A socio-technical systems approach to design and support systems thinking in cybersecurity and risk management education. Complex Systems Informatics and Modeling Quarterly, (18), 65-75.
  • Zou, J., Sun, H., Fan, B., & Zhao, Y. (2019, October). A General Simulation Framework for Crowd Network Simulations. In Proceedings of the 4th International Conference on Crowd Science and Engineering (pp. 12-19).
  • Zu, C., Zeng, H. & Zhou, X.(2019) Computational Simulation of Team Creativity: The Benefit of Member Flow. Frontiers in Psychology. [PDF]
  • Zulkarnay, I. (2019). Simulation modeling in the study of the optimal placement of systemically important universities across the country. Artificial Societies, 14(4).

2018

  • Adelt, F., Weyer, J., Hoffmann, S., & Ihrig, A. (2018). Simulations of the governance of complex systems (SimCo): Basic concepts and experiments on urban transportation. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3654
  • Agarwal, A., Hartman, T., & Goel, A. K. (2018). From Middle School to Graduate School: Combining Conceptual and Simulation Modeling for Making Science Learning Easier. In CogSci.
  • Almagor, J., Benenson, I., & Czamanski, D. (2018). The Evolution of the Land Development Industry: An Agent-Based Simulation Model. In Trends in Spatial Analysis and Modelling (pp. 93-120). Springer, Cham.[PDF]
  • Alvarado, A., Mata, A., Segovia, M., & Vargas, E. (2018). Emergencia De Ideas Matemáticas En Secundaria Con Simulaciones Participativas En Netlogo. REVISTA ELECTRÓNICA AMIUTEM, 2(1), 88-94.
  • Alves, F., Pereira, A. I., Barbosa, J., & Leitão, P. (2018, June). Scheduling of home health care services based on multi-agent systems. In International conference on practical applications of agents and multi-agent systems (pp. 12-23). Springer, Cham.
  • Alves, F., Varela, M. L. R., Rocha, A. M. A., Pereira, A. I., Barbosa, J., & Leitão, P. (2018, December). Hybrid system for simultaneous job shop scheduling and layout optimization based on multi-agents and genetic algorithm. In International Conference on Hybrid Intelligent Systems (pp. 387-397). Springer, Cham.
  • Amin, E., Abouelela, M., & Soliman, A. (2018). The role of heterogeneity and the dynamics of voluntary contributions to public goods: An experimental and agent-based simulation analysis. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3585
  • Anderson, A. (2018). Costs and Benefits of Copulatory Silk Wrapping in the Nursery Web Spider, Pisaurina mira Walckenaer, 1837 (Araneae, Pisauridae). The University of Nebraska - Lincoln, ProQuest Dissertations Publishing. [PDF]
  • Anderson, T., & Dragićević, S. (2018). Deconstructing Geospatial Agent-Based Model: Sensitivity Analysis of Forest Insect Infestation Model. In Agent-Based Models and Complexity Science in the Age of Geospatial Big Data (pp. 31-44). Springer, Cham.[PDF]
  • Anderson, W., Kobold, K., & Yakimenko, O. (2018). Employing Systems Engineering Tools to Analyze Green Microgrids for Remote Islands.World Academy of Science, Engineering and Technology, International Journal of Energy and Power Engineering, 5(6).[PDF]
  • Andre, R. (2018). Simulating the Emergence of Social Complexity Using Agent-Based Modelling. In MEi: CogSci Conference 2018 (p. 64).
  • Antoniou, V., & Schlieder, C. (2018). Addressing Uneven Participation Patterns in VGI Through Gamification Mechanisms. In Geogames and Geoplay (pp. 91-110). Springer, Cham.[PDF]
  • Aurambout, J. P., & Endress, A. G. (2018). A model to simulate the spread and management cost of kudzu (Pueraria montana var. lobata) at landscape scale. Ecological Informatics, 43, 146-156.[PDF]
  • Avvenuti, M., Cimino, M. G. C., Cola, G., & Vaglini, G. (2018, December). Detection and mapping of a toxic cloud using uavs and emergent techniques. In International Conference on Mining Intelligence and Knowledge Exploration (pp. 215-224). Springer, Cham.
  • Baeza, A., & Janssen, M. A. (2018). Modeling the decline of labor-sharing in the semi-desert region of Chile. Regional Environmental Change, 18(4), 1161-1172.[PDF]
  • Bassett, K. (2018). Agent-Based Modeling of Pollen Competition.
  • Beauchemin, C. A., Liao, L. E., & Blahut, K. (2018). Tutorial on agent-based models in NetLogo applied to immunology and virology. arXiv preprint arXiv:1808.09499.
  • Berea, A. (2018). Constructed Language Versus Bio-chemical Communication: An Agent-Based Model and Applications. In Emergence of Communication in Socio-Biological Networks (pp. 31-49). Springer, Cham.[PDF]
  • Berea, A. (2018). Emergence of Communication in Socio-Biological Networks. Springer International Publishing.[PDF]
  • Bourgais, M., Taillandier, P., Vercouter, L., & Adam, C. (2018). Emotion modeling in social simulation: A survey. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3681
  • Boyd, R., Roy, S., Sibly, R., Thorpe, R., & Hyder, K. (2018). A general approach to incorporating spatial and temporal variation in individual-based models of fish populations with application to Atlantic mackerel. Ecological Modelling, 382, 9-17.[PDF]
  • Bozuyla, M., Tola, A. T., & Murat, Y. S. (2018). A Novel Safe Merging Algorithm for Connected Vehicles Using NetLogo. Elektronika ir Elektrotechnika, 24(3), 3-7.
  • Brady, C., Petrosino, A., Stroup, W., & Wilensky, U. (2018). Group-based cloud computing: Technological supports for social constructionism. In Dagiene, V. & Jasute, E. (Eds.) Proceedings of the Constructionism 2018 conference. Vilnius, Lithuania.
  • Brilliantova, A., Pletenev, A., Doronina, L., & Hosseini, H.(2018). An agent-based model of an endangered population of the Arctic fox from Mednyi Island. Moscow University, University of Munster, Rochester Institute of Technology [PDF]
  • Broniatowski, D. A., & Moses, J. (2018). The Flexibility of Generic Architectures: Lessons from the Human Nervous System. In Disciplinary Convergence in Systems Engineering Research (pp. 585-598). Springer, Cham.[PDF]
  • Burrows, A., French, D. (2018). Evidence of Science and Engineering Practices in Preservice Secondary Science Teachers’ Instructional Planning Journal of Science Education and Technology, (p. 1-14). [PDF]
  • Carbo, J., Sanchez-Pi, N., & Molina, J. M. (2018). Agent-based simulation with NetLogo to evaluate ambient intelligence scenarios. Journal of Simulation, 12(1), 42-52.
  • Carillo, M., Cordasco, G., Serrapica, F., Scarano, V., Spagnuolo, C., & Szufel, P. (2018). Distributed simulation optimization and parameter exploration framework for the cloud. Simulation Modelling Practice and Theory, 83, 108-123.[PDF]
  • Castillo, E. A., & Trinh, M. P. (2018). In search of missing time: A review of the study of time in leadership research. The Leadership Quarterly, 29(1), 165-178.[PDF]
  • Chareunsy, A. K. (2018). Diffusion of development initiatives in a southern Lao community: An agent based evaluation. Journal of Asian Economics, 54, 53-68.[PDF]
  • Chaudhari, K. S., Kandasamy, N. K., Krishnan, A., Ukil, A., & Gooi, H. B. (2018). Agent Based Aggregated Behavior Modelling For Electric Vehicle Charging Load. IEEE Transactions on Industrial Informatics.[PDF]
  • Chen, W., Liu, H., & Xu, D. (2018). Dynamic pricing strategies for perishable product in a competitive multi-agent retailers market. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3710
  • Chennoufi, M., Bendella, F., & Bouzid, M. (2018). Multi-agent simulation collision avoidance of complex system: application to evacuation crowd behavior. International Journal of Ambient Computing and Intelligence (IJACI), 9(1), 43-59.
  • Chi, Y. (2018). Self-Organized Bike Redistribution in Urban City. American Journal of Operations Research, 8(5), 386-394.
  • Chumachenko, D., Dobriak, V., Mazorchuk, M., Meniailov, I., & Bazilevych, K. (2018, February). On agent-based approach to influenza and acute respiratory virus infection simulation. In Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), 2018 14th International Conference on (pp. 192-195). IEEE.[PDF]
  • Clapp, J. (2018). The Promise of Systems Science in Health Behavior Research: The Example of Studying Drinking Events. Health Behavior Research 1(2) [PDF]
  • Condro A., Pawitan H., Risdyanto I. (2018). Predicting drought propagation within peat layers using a three dimensionally explicit voxel based model (pp. 149). IOP Conference: earth Envionmental Science.[PDF]
  • Cortier, O., Boutouil, M., & Maquaire, O. (2018, September). Quantifying Benefits of Permeable Pavement on Surface Runoff, An Agent-Based-Model with NetLogo. In International Conference on Urban Drainage Modelling (pp. 729-733). Springer, Cham.
  • Crittenden, J., Fujimoto, R., Lu, Z., Pecher, P. (2018). Granular Cloning: Intra-Object Parallelism in Ensemble Studies Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Pages 165-176.[PDF]
  • Cucart-Mora, C., Lozano, S., & de Pablo, J. F. L. (2018). Bio-cultural interactions and demography during the Middle to Upper Palaeolithic transition in Iberia: An agent-based modelling approach. Journal of Archaeological Science, 89, 14-24.[PDF]
  • Dabholkar, S., Anton, G., & Wilensky, U. (2018). Developing mathetic content knowledge using an emergent systems microworld. Proceedings of Constructionism.
  • Daeichian, A., & Haghani, A. (2018). Fuzzy Q-Learning-Based Multi-agent System for Intelligent Traffic Control by a Game Theory Approach. Arabian Journal for Science and Engineering, 1-7.[PDF]
  • Daňa, J., & Ráček, J. (2018). Modeling and Simulating Cooperation in Organizations. IT for Practice 2018, 79.
  • Davis, B. (2018). Complexity as a Discourse on School Mathematics Reform. In Transdisciplinarity in Mathematics Education (pp. 75-88). Springer, Cham.[PDF]
  • de Aguiar, P. V., de Sá, C. C., de Lima, H. G. G., Amaral, A. R., & Parpinelli, R. S. (2018, October). Application of the NetLogo Tool in Ischemic Stroke Simulation. In Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (pp. 596-607). SBC.
  • Delcea, C., Cotfas, L. A., & Paun, R. (2018, September). Agent-based optimization of the emergency exits and desks placement in classrooms. In International Conference on Computational Collective Intelligence (pp. 340-348). Springer, Cham.
  • Delcea, C., Cotfas, L. A., & Paun, R. (2018, September). Airplane boarding strategies using agent-based modeling and grey analysis. In International Conference on Computational Collective Intelligence (pp. 329-339). Springer, Cham.
  • Delcea, C., Cotfas, L. A., & Paun, R. (2018). Agent-based evaluation of the airplane boarding strategies’ efficiency and sustainability. Sustainability, 10(6), 1879.
  • Delcea, C., Cotfas, L. A., Chiriță, N., & Nica, I. (2018). A two-door airplane boarding approach when using apron buses. Sustainability, 10(10), 3619.
  • Deshmukh, V. (2018). Modeling Human Migration Dynamics in Netlogo.
  • DeSoto, K. A. (2018). Modeling Evacuation of Population Centers Using NetLogo
  • Di Pietrantonio, J. (2018). A Computational Model of Team-Based Dynamics in the Workplace: Assessing the Impact of Incentive-Based Motivation on Productivity (Doctoral dissertation, Duquesne University).[PDF]
  • Djerroud, H., & Cherif, A. A. (2018, November). Visualization tool for jade platform (jex). In Proceedings of the Future Technologies Conference (pp. 481-489). Springer, Cham.
  • Dobbie, S., Schreckenberg, K., Dyke, J. G., Schaafsma, M., & Balbi, S. (2018). Agent-based modelling to assess community food security and sustainable livelihoods. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3639
  • Dorsey, J. W., & Hardy, L. C. (2018). Sustainability factors in dynamical systems modeling: Simulating the non-linear aspects of multiple equilibria. Ecological Modelling, 368, 69-77.[PDF]
  • Dos Santos, H. D. R., Galafassi, C., de Souza, D. C., & Russini, A. (2018). MICROSSIMULAÇÃO DO TRAJETO DE UMA MÁQUINA AGRÍCOLA UTILIZANDO O SOFTWARE NETLOGO. Anais do Salão Internacional de Ensino, Pesquisa e Extensão, 9(3).
  • Drezewski, R. (2018). The Agent-Based Model and Simulation of Sexual Selection and Pair Formation Mechanisms. Entropy 20(5), p. 342. [PDF]
  • Du, x., Chen, Y., Bouferguene, A., Al-Hussein, M.(2018). Multi-Agent based simulation of elderly egress process and fall accident in senior partment buildings. 2018 Winter Simulation Conference [PDF]
  • Dubovi, I., Dagan, E., Mazbar, O. S., Nassar, L., & Levy, S. T. (2018). Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study. Nurse education today, 61, 175-181.[PDF]
  • Easter, C. (2018). An agent-based simulation for studying the effect of various parameters on the evolution of teaching.
  • Edali, M. & Yücel, G. (2018). Automated analysis of regularities between model parameters and output using support vector regression in conjunction with decision trees. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3876
  • El Hachami, K., & Tkiouat, M. (2018, April). An approach for modeling the economy as a complex system using agent-based theory. In Intelligent Systems and Computer Vision (ISCV), 2018 International Conference on (pp. 1-6). IEEE.[PDF]
  • Ene, N., Fernandez, M., Pinaud, B. (2018). A Graph Transformation Approach to the Modelling of Capital Markets. [PDF]
  • Enokela, J. A. (2018). An Analysis of the Consequences of the Herdsmen-Farmers Crisis in North Central Nigeria using Agent–Based Modelling. The Pacific Journal of Science and Technology, 19(2), 326-336.
  • Feldman, T. (2018). Unwinding ZIRP: A simulation analysis. Finance Research Letters, 24, 278-288.[PDF]
  • Flores-Parra, J. M., Castañón-Puga, M., Gaxiola-Pacheco, C., Palafox-Maestre, L. E., Rosales, R., & Tirado-Ramos, A. (2018). A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo. In Computer Science and Engineering—Theory and Applications (pp. 127-149). Springer, Cham.
  • Furtado, B. (2018). Policy space: agent based modeling. Rio de Janerio: Ipea.[PDF]
  • Galán, S. F., & Mengshoel, O. J. (2018). Neighborhood beautification: Graph layout through message passing. Journal of Visual Languages & Computing, 44, 72-88.[PDF]
  • Gao, X., Li, K., & Chen, B. (2018). Invulnerability Measure of a Military Heterogeneous Network Based on Network Structure Entropy. IEEE Access, 6, 6700-6708.[PDF]
  • García-Magariño, I., Gray, G., Lacuesta, R., & Lloret, J. (2018). Survivability strategies for emerging wireless networks with data mining techniques: a case study with NetLogo and RapidMiner.IEEE Access.[PDF]
  • Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., Narasimhan, K. P., & Wilkinson, H. (2018). Computational modelling of public policy: Reflections on practice. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3669
  • Ginovart, M. (2018). How a multi-agent programmable modelling environment like NetLogo can help to deal with communities or assemblages of bacteria on surfaces?. Exploring Microorganisms: Recent Advances in Applied Microbiology, 256.
  • Gómez-Cruz, N. A., Loaiza Saa, I., & Ortega Hurtado, F. F. (2018). Agent-based simulation in management and organizational studies: a survey. European Journal of Management and Business Economics, 26(3), 313-328.[PDF]
  • Gonzales, G. V., dos Santos, E. D., Adamatti, D. F., & Neto, A. J. S. (2018). A Netlogo and Matlab Hybrid Approach for Constructal Design of the Double-T Shaped Cavity by Means of Simulated Annealing. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, 6(1).
  • GOROSHNIKOVA, T., & SMAKHTIN, E. (2018). EMOTIONS IN DECISION-MAKING WITHIN SIMULATION MODELING. In System analysis in economics-2018 (pp. 135-138).
  • Greco, A., & Pluchino, A. (2018). On the use of immune algorithms to determine seismic collapse conditions for frame structures. Computer Software and Media Applications.
  • Gunaratne, C., & Garibay, I. (2018). NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces. arXiv preprint arXiv:1808.03292.
  • Gündel, M., Hoyt, C. T., & Hofmann-Apitius, M. (2018). BEL2ABM: agent-based simulation of static models in Biological Expression Language. Bioinformatics, 34(13), 2316-2318.
  • Guo, Y. and Wilensky (2018a). NetLogo MTG 1 Equal Opportunities HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Guo, Y. and Wilensky (2018b). NetLogo MTG 2 Random Assignment HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Guo, Y. and Wilensky (2018c). NetLogo MTG 3 Feedback Loop HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Haddad, T. A. (2018). An IoT-Based Adaptive Traffic Light Control Algorithm for Isolated Intersection. In Advances in Computing Systems and Applications: Proceedings of the 4th Conference on Computing Systems and Applications (p. 107). Springer Nature.
  • Haeme, J., McCaw, K., Nguyen, T., & May, T. (2018). Project Whirligig: Modeling the Swarming Behavior of Whirligig Beetles.
  • Han, S., Huang, H., Luo, Z., & Foropon, C. (2018). Harnessing the power of crowdsourcing and Internet of Things in disaster response. Annals of Operations Research, 1-16.[PDF]
  • Haydari, S. (2018). Copyright Law and Knowledge Creation: A Study of Copyright Term Length Impact on Knowledge Creation and Learning. Northeastern University, ProQuest Dissertations Publishing[PDF]
  • Head, B., & Wilensky, U. (2018). Agent cognition through micro-simulations: Adaptive and tunable intelligence with NetLogo LevelSpace. In: Morales A., Gershenson C., Braha D., Minai A., Bar-Yam Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham.[PDF]
  • Heath, K. N., Ryder, E. F., & Gegear, R. J. (2018). Investigating the effect of memory loss on pollinator-plant interactions through agent-based modeling. Gordon Research Conference: Unifying Ecology Across Scales, Biddeford, ME, United States.
  • Hébert, G. A., Perez, L., & Harati, S. (2018). An Agent-Based Model to Identify Migration Pathways of Refugees: The Case of Syria. In Agent-Based Models and Complexity Science in the Age of Geospatial Big Data (pp. 45-58). Springer, Cham.[PDF]
  • Hokamp, S., Gulyas, L., Koehler, M., & Wijesinghe, S. (2018). Agent-Based Modeling of Tax Evasion. Wiley.
  • Hollar, D. W. (2018). Simulations, Applications, and the Challenge for Public Health. In Trajectory Analysis in Health Care (pp. 231-246). Springer, Cham.
  • Huang, W. (2018). Exploring households’ weatherization adoptions: an agent-based approach (Doctoral dissertation, Iowa State University).[PDF]
  • Huber, L., Bahro, N., Leitinger, G., Tappeiner, U., & Strasser, U. (2018, April). Aqua. MORE: Socio-hydrological Modelling of Water Resources in an Alpine Catchment. In EGU General Assembly Conference Abstracts (p. 6426).
  • Humann, J., Jin, Y., & Madni, A. M. (2018). Scalability in Self-Organizing Systems: An Experimental Case Study on Foraging Systems. In Disciplinary Convergence in Systems Engineering Research (pp. 543-557). Springer, Cham.[PDF]
  • Hunter, E., Mac Namee, B., & Kelleher, J. D. (2018). Using a socioeconomic segregation burn-in model to initialise an agent-based model for infectious diseases. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3870
  • Izquierdo, S. S. & Izquierdo, L. R. (2018). Mamdani fuzzy systems for modelling and simulation: A critical assessment. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3660
  • Jaxa-Rozen, M., & Kwakkel, J. H. (2018). PyNetLogo: Linking NetLogo with Python. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3668
  • Jianyu, Z., Baizhou, L., Xi, X., Guangdong, W., & Tienan, W. (2018). Research on the characteristics of evolution in knowledge flow networks of strategic alliance under different resource allocation. Expert Systems with Applications, 98, 242-256.[PDF]
  • JIN, Z., & ZOU, H. (2018). Research on joint distribution based on Web semantics and Agent technology. Modern Electronics Technique, 2018, 14.
  • Jing, L., Chen, B., Zhang, B., & Ye, X. (2018). Modeling marine oily wastewater treatment by a probabilistic agent-based approach. Marine pollution bulletin, 127, 217-224.[PDF]
  • Karanci, A., Velásquez-Montoya, L., Paniagua-Arroyave, J. F., Adams, P. N., & Overton, M. F. (2018). Beach Management Practices and Occupation Dynamics: An Agent-Based Modeling Study for the Coastal Town of Nags Head, NC, USA. In Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 373-395). Springer, Cham.[PDF]
  • Kaziyeva, D., Wallentin, G., Loidl, M., Mohr, S., & Neuwirth, C. (2018). Reviewing software for agent-based bicycle flow models. GI_Forum, 6, 291-296.
  • Klein, I., Levy, N., & Ben-Elia, E. (2018). An agent-based model of the emergence of cooperation and a fair and stable system optimum using ATIS on a simple road network. Transportation research part C: emerging technologies, 86, 183-201.[PDF]
  • Koch, A. (2018). Dynamic Relationships Between Human Decision Making and Socio-natural Systems. In Trends in Spatial Analysis and Modelling (pp. 121-141). Springer, Cham.[PDF]
  • Köhler, J., de Haan, F., Holtz, G., Kubeczko, K., Moallemi, E., Papachristos, G., & Chappin, E. (2018). Modelling sustainability transitions: An assessment of approaches and challenges. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3629
  • Koli, V.N., Mirza, F., Baig, M.M., & Ullah, E.(2018). An Agent-Based Modelling Approach for Scheduling and Management of Elective Surgeries. SM Journal of Health and Medical Informatics[PDF]
  • Kotnik, K. (2018). Exploring Marine Population Dynamics with Agent Based Models.
  • Kurahashi, S. (2018, June). Agent-Based Gaming Approach for Electricity Markets. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 311-320). Springer, Cham.[HTML]
  • Laatabi, A., Marilleau, N., Nguyen-Huu, T., Hbid, H., & Ait Babram, M. (2018). ODD+2D: An ODD based protocol for mapping data to empirical ABMs. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3646
  • Lawall, M. L., & Graham, S. (2018). NETLOGO SIMULATIONS AND THE USE OF TRANSPORT AMPHORAS IN ANTIQUITY. Maritime Networks in the Ancient Mediterranean World, 163.
  • Lawlor, F., Collier, R., Nallur, V. (2018).Towards a Programmable Framework for Agent Game Playing. Adaptive Learning Agents Workshop.[HTML]
  • Lawson, T., Rogerson, R., & Barnacle, M. (2018). A comparison between the cost effectiveness of CCTV and improved street lighting as a means of crime reduction. Computers, Environment and Urban Systems, 68, 17-25.
  • Lee, T. E. (2018). The thin blue line between protesters and their counter-protesters. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3676
  • Lemos, C. M. (2018). ABM of Civil Violence: ODD Description. In Agent-Based Modeling of Social Conflict (pp. 51-63). Springer, Cham.[HTML]
  • Lemos, C. M. (2018). Model Exploration and Computer Experiments. In Agent-Based Modeling of Social Conflict (pp. 65-111). Springer, Cham.[HTML]
  • Liew, C. W., Phuong, T., Jones, C. B., Evans, S., Hoot, J., Weedling, K., ... & Kurt, R. A. (2018). A computational approach to unraveling TLR signaling in murine mammary carcinoma. Computers in biology and medicine, 93, 56-65.[PDF]
  • Liukkonen, L., Ayllón, D., Kunnasranta, M., Niemi, M., Nabe-Nielsen, J., Grimm, V., & Nyman, A. M. (2018). Modelling movements of Saimaa ringed seals using an individual-based approach. Ecological Modelling, 368, 321-335.[PDF]
  • Lopez-Parez, A., Ruiz-Martin, C., Wainer, G. (2018). Formal Abstract Modeling of Dynamic Multiplex Networks. Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pp.61-72. Rome, Italy.[PDF]
  • Luna-Ramirez, W. A., & Fasli, M. (2018). Bridging the gap between abm and mas: A disaster-rescue simulation using jason and netlogo. Computers, 7(2), 24.
  • Maciel, M.V. (2018). Emergência de distribuições de posicionamentos ideológicos: uma abordagem computacional. Diss. Universidade de São Paulo.
  • Mailleret, L., Davtian, D., & Grognard, F. (2018). An individual based model to optimize natural enemies deployment in augmentative biological control.
  • Makarov, S., & Belianov, A. (2018). Reasons to create a Python framework for agent-based simulation models development. Herald of CEMI, (1).
  • Malishev, M., Bull, C. M., & Kearney, M. R. (2018). An individual‐based model of ectotherm movement integrating metabolic and microclimatic constraints. Methods in Ecology and Evolution, 9(3), 472-489.[PDF]
  • Mao, W., Wu, H., Pan, L., & Zhou, J. (2018, September). Multi-layer Simulation Methods of Temperature Controlled Loads. In 2018 China International Conference on Electricity Distribution (CICED) (pp. 2002-2006). IEEE.
  • Martin, K. (2018, October). Multitouch NetLogo for museum interactive game. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 5-8).
  • Mayangsari, L., & Pasaribu, N. (2018). Understanding Unlawful Behavior in Music Industry Using Agent-Based Modelling and Simulation: A Complementary of Misconception in Value Co-Creation. World Academy of Science, Engineering and Technology, International Journal of Industrial and Manufacturing Engineering, 5(4).[HTML]
  • Menárguez, F. J. M., Hortal, J. C., Salazar, S. S., & Alarcón, A. A. (2018). Anatomizing NetLogo. Some advices on how to consider a programmable environment for designing inhabited landscapes. In EURAU18 Alicante: Retroactive Research: Congress Proceedings (pp. 423-428). Universitat d´ Alacant/Universidad de Alicante.
  • Modu, B., Polovina, N., Lan, Y., & Konur, S. (2018). Machine learning analysis and agent-based modelling of malaria transmission. In Fuzzy Systems and Data Mining IV (pp. 465-472). IOS Press.
  • Moglia, M., Podkalicka, A., & McGregor, J. (2018). An agent-based model of residential energy efficiency adoption. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3729
  • Muelder, H. & Filatova, T. (2018). One theory - many formalizations: Testing different code implementations of the theory of planned behavior in energy agent-based models. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3855
  • Mueller, C., Klein, U., & Hof, A. (2018). An easy-to-use spatial simulation for urban planning in smaller municipalities. Computers, Environment and Urban Systems.[PDF]
  • Muhammad, A., Kashif, Z., & Saini, D. (2018). Agent-based Simulation of Socially-inspired Model of Resistance against Unpopular Norms Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) - Volume 1, pages 133-139.[PDF]
  • Nabe-Nielsen, J., van Beest, FM., Grimm, V., Sibly, PM.(2018). TRACE document: Disturbances and marine populations.[PDF]
  • Nie, J., Hu, H., Wang, X., & Xu, C. (2018). Simulation Study on Network Stability of Short Cycle Product Supply Chain Based on Netlogo. In CICTP 2018: Intelligence, Connectivity, and Mobility (pp. 409-418). Reston, VA: American Society of Civil Engineers.
  • Olugboji, O., Camorlinga, S. G., de Faria, R. L., & Kaushal, A. (2018). Understanding the Emergency Department Ecosystem Using Agent-Based Modeling: A Study of the Seven Oaks General Hospital Emergency Department. In Putting Systems and Complexity Sciences Into Practice (pp. 199-214). Springer, Cham.
  • Opiyo, N. (2018, September). How Subsidies Impact on Temporal Diffusion of PV-Based Minigrids. In 35th European Photovoltaic Solar Energy Conference.
  • Opiyo, N. (2018, September). Modelling Different PV-Based Minigrids Architectures. In 35th European Photovoltaic Solar Energy Conference.
  • Opiyo, N. (2018). Impacts of socio-economic policies on temporal diffusion of PV-based communal grids in a rural developing community. In Proceedings of the 35th EU PVSEC 2018 (pp. 2182-2187).
  • Opiyo, N. (2018). Modelling different PV-based communal grids architectures for rural developing communities. In Proceedings of the 35th EU PVSEC 2018 (pp. 1859-1864).
  • Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High Performance Computer Simulations
  • Ornelas, N. O. An Ecosystem: Computational Thinking, Project-Based Learning [PDF]Logo
  • Paris, T., Ciarletta, L., & Chevrier, V. (2018, October). Co-simulation à base d'outils multi-agents: un cas d'étude avec NetLogo.
  • Pei, C., Weintrop, D., & Wilensky, W. (in press). Cultivating computational thinking practices and mathematical habits of mind in Lattice Land. Mathematical Thinking and Learning.
  • Pele, M., Deneubourg, J. L., & Sueur, C. (2018). Decision-making processes underlying pedestrian behaviours at signalised crossings: Part 2. Do pedestrians show cultural herding behaviour?. arXiv preprint arXiv:1805.11834.[PDF]
  • Peng, Y., Li, Q. X., & Bao, H. J. (2018). Conflict Analysis of Concentrated Rural Settlement Development During Post-disaster Reconstruction in China: A Multi-agent Simulation. In Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate (pp. 491-502). Springer, Singapore.[PDF]
  • Petrosino, A. J., Sherard, M. K., Harron, J. R., & Stroup, W. (2018). Using Collaborative Agent-Based Computer Modeling to Explore Tri-Trophic Cascades with Elementary School Science Students. Creative Education, 09(04), 615–624. https://doi.org/10.4236/ce.2018.94043
  • Petrosino, A., Sherard, M., Harron, J., & Kohl, M. (2018, March). Using Collaborative Agent-based Modeling to Explore Complex Phenomena in Pre-and In-service Teacher Education. In Society for Information Technology & Teacher Education International Conference (pp. 1669-1671). Association for the Advancement of Computing in Education (AACE).
  • Polater, A. (2018). Managing airports in non-aviation related disasters: A systematic literature review. International Journal of Disaster Risk Reduction.[PDF]
  • Ponziani, F. A., Tinaburri, A., & Ricci, V. (2018). A multi agent approach to analyse shift in people behavior under critical conditions. International Journal of Safety and Security Engineering, 8(1), 1-9.
  • PONZIANI, F., TINABURRI, A., & RICCI, V. (2018). A Multi Agent Approach To Analyse Shift In People Behaviour Under Critical Conditions. International Journal of Safety and Security Engineering, 8(1), 1-9.[PDF]
  • Pour, F. S. A., Tatar, U., & Gheorghe, A. (2018, April). Agent-based model of sand supply governance employing blockchain technology. In Proceedings of the Annual Simulation Symposium (p. 14). Society for Computer Simulation International.[PDF]
  • Proctor, C., Blikstein, P.(2018). Unfold.studio: Supporting critical literacies of text and code Stanford Graduate School of Education, (p. 1-35).[PDF]
  • Proietti, C. & Franco, A. Social norms and the dominance of low-doers. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3524
  • Quan-en, M. A., & Juan, Z. H. A. N. G. (2018). Research on Propagation Principle of Wechat Official Account in Complex Network by Modeling Based on SIR and Simulation. Information Science.
  • Raglin, A., Metu, S., & Howard, C. (2018, April). Understanding theoretical human information interaction, the development of a standard model using an agent based modeling framework. In Next-Generation Analyst VI (Vol. 10653, p. 1065302). International Society for Optics and Photonics.
  • Raimbault, J. An Urban Morphogenesis Model Capturing Interactions between Networks and Territories.[PDF]
  • Rajib, Md. Design Considerations for Intermittently Connected Energy Harvesting Wireless Sensor Networks. ProQuest.[PDF]
  • Ramírez-Ávila, G. M., Kurths, J., & Deneubourg, J. L. (2018). Fireflies: a paradigm in synchronization. In Chaotic, Fractional, and Complex Dynamics: New Insights and Perspectives (pp. 35-64). Springer, Cham.[PDF]
  • Rashid, K. I., Nan, D., Tahir, M., & Ahmed, A. An Adaptive Cruise Control Model based on PDLCA for Efficient Lane Selection and Collision Avoidance. International Journal of Advanced Computer Science and Applications (9).[PDF]
  • Raya, K., Gaxiola, C. G., & Castanon, M. (2018). Agent-based model for self management of network flows using negotiation. IEEE Latin America Transactions, 16(1), 210-215.
  • Reinhardt, O., Hilton, J., Warnke, T., Bijak, J., & Uhrmacher, A. M. (2018). Streamlining simulation experiments with agent-based models in demography. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3784
  • Rosales, C., Whipple, J. M., & Blackhurst, J. (2018). The Impact of Out-of-Stocks and Supain Design on Manufacturers: Insights from an Agent-Based Model. Transportation Journal, 57(2), 137-162.[PDF]
  • Rossetti, G., Milli, L., Rinzivillo, S., Sîrbu, A., Pedreschi, D., & Giannotti, F. (2018). NDlib: a python library to model and analyze diffusion processes over complex networks. International Journal of Data Science and Analytics, 5(1), 61-79.[PDF]
  • Salman, M. A., & Al Essa, H. A. (2018). A Distributed Approach for Disk Defragmentation. Journal of University of Babylon for Pure and Applied Sciences, 26(3), 1-5.
  • Santos, F., Nunes, I., & Bazzan, A. L. (2018). Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain. Simulation Modelling Practice and Theory, 83, 162-187.[PDF]
  • Saputra, G. W., Irawan, B., & Kusuma, P. D. (2018). Pemodelan Dan Simulasi Penyebaran Penyakit Tuberkulosis Berbasis Sistem Agen. eProceedings of Engineering, 5(3).
  • Schools, S. (2018). A Genetic Algorithm for Sorting Lists Implemented in the Programming Language Net Logo (Doctoral dissertation, Morgan State University)
  • Secchi, D. & Cowley, S. J. (2018). Modeling organizational cognition: The case of impact factor. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3628
  • SHEN, Y. W., & LU, Z. J. (2018). Research on the Evolution of Entrepreneurial Ecosystem Based on Innovation Network. In Economic Forum.
  • Simpkins, C. E., Dennis, T. E., Etherington, T. R., & Perry, G. L. (2018). Assessing the performance of common landscape connectivity metrics using a virtual ecologist approach. Ecological Modelling, 367, 13-23.[PDF]
  • Snitker, G. (2018). Identifying natural and anthropogenic drivers of prehistoric fire regimes through simulated charcoal records. Journal of Archaeological Science, 95, 1-15.[PDF]
  • Soheilypour, M., & Mofrad, M. R. (2018). Agent‐based modeling in molecular systems biology. BioEssays, 40(7), 1800020.
  • Stamatovic, B. (2018, February). Implementation of CA algorithm for labeling of 26-connected components in 3D binary lattices. In 2018 23rd International Scientific-Professional Conference on Information Technology (IT) (pp. 1-3). IEEE.
  • Sturley, C., Newing, A., & Heppenstall, A. (2018). Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours. The Ionternatinal Review of Retail, Distribution and Consumer Research, 28(1), 27-46.[HTML]
  • Sukarno, S. A., Atitallah, R. B., & Djemai, M. (2018, October). Approximation Algorithm for 3-Dimensional Vehicle Routing Problem for Fleet of Multi-Agents. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT) (pp. 1-6). IEEE
  • Sullivan, A., An, L., & York, A. (2018). Which perspective of institutional change best fits empirical data? An agent-based model comparison of rational choice and cultural diffusion in invasive plant management. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3611
  • Suyamto, D., Prasetyo, L., & Setiawan, Y. (2018, August). A voxel-based model of LiDAR point cloud for estimating forest canopy closure. In Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018) (Vol. 10773, p. 107730Q). International Society for Optics and Photonics.
  • Syuhada, K. E. Pengaruh Formasi Penyimpanan Metode Volume Based Terhadap Unjuk Kerja Strategi Routing Order Picking Menggunakan Strategi Midpoint dan Largesgap. Jurnal TIN Universitas Tanjungpura, 2(2).
  • Taherian, M., Mousavi, S. M., & Chamani, H. (2018). An agent-based simulation with NetLogo platform to evaluate forward osmosis process (PRO Mode). Chinese journal of chemical engineering, 26(12), 2487-2494.
  • Thakur, S., Bhautik, P., Sangore, V., Singh, K. (2018).A Review on Treatment of Sewage Water & Biogas Purification by Algae. Internatinal Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 1, Page No pp.639-642, January 2018, [PDF]
  • Thiriot, S. (2018). Word-of-mouth dynamics with information seeking: Information is not (only) epidemics. Physica A: Statistical Mechanics and its Applications, 492, 418-430.[PDF]
  • Trivedi, A., & Pandey, M. (2018). Agent-based modelling and simulation of religious crowd gatherings in India. In Advanced Computational and Communication Paradigms (pp. 465-472). Springer, Singapore.
  • Tufféry, C., Fernandes, P., Delvigne, V., & Morala, A. (2018). Combinaison d’un SMA et d’un SIG pour aider à la prospection pétroarchéologique. Exploration d’une approche multi-agents dans la modélisation des parcours naturels du silex. Archéologies numériques, 2(1).
  • Tyson, M. (2018). Managing Distributed Information: Implications for Energy Infrastructure Co-production (Doctoral dissertation, Arizona State University).[PDF]
  • Vanhée, L. & Dignum, F. (2018). Explaining the emerging influence of culture, from individual influences to collective phenomena. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3881
  • Vidaković, M., Ivanović, M., Stantić, D., & Vidaković, J. (2018, June). How Research Achievements Can Influence Delivering of a Course-Siebog Agent Middleware. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 110-120). Springer, Cham.[HTML]
  • Voinov, A., Jenni, K., Gray, S., Kolagani, N., Glynn, P. D., Bommel, P., ... & Smajgl, A. (2018). Tools and methods in participatory modeling: Selecting the right tool for the job. Environmental Modelling & Software, 109, 232-255.
  • Wagh, A., & Wilensky, U. (2018). EvoBuild: A quickstart toolkit for programming agent-based models of evolutionary processes. Journal of Science Education and Technology, 27(2), 131-146.
  • Waight, N., & Abd-El-Khalick, F. (2018). Technology, Culture, and Values: Implications for Enactment of Technological Tools in Precollege Science Classrooms. In Cognition, Metacognition, and Culture in STEM Education (pp. 139-165). Springer, Cham.[PDF]
  • Walbert, H. J., Caton, J. L., & Norgaard, J. R. (2018). Countries as agents in a global-scale computational model. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3717
  • Wang, X. M., He, C. C., & Li, X. K. (2018). Interaction Optimization Among Multi-agent of Green Dwelling Market. Journal of Civil Engineering and Management, 35(2), 1-7.
  • Wang, Y., Wen, S., Farnon Ellwood, M. D., Miller, A. D., & Chu, C. (2018). Temporal effects of disturbance on community composition in simulated stage‐structured plant communities. Ecology and evolution, 8(1), 120-127.[PDF]
  • Wang, Z., Zhang, H., Hu, M., Qiu, Q., & Liu, H. (2018). Analysis of safety characteristics of flight situation in complex low-altitude airspace. Advances in Mechanical Engineering, 10(5), 1687814018774656.[PDF]
  • Weintrop, D., & Wilensky, U. (2018). How block-based, text-based, and hybrid block/text modalities shape novice programming practices. International Journal of Child-Computer Interaction.[PDF]
  • Weisbuch, G. (2018). Lattice dynamics of inequality. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3635
  • West, T. A., Grogan, K. A., Swisher, M. E., Caviglia-Harris, J. L., Sills, E., Harris, D., ... & Putz, F. E. (2018). A hybrid optimization-agent-based model of REDD+ payments to households on an old deforestation frontier in the Brazilian Amazon. Environmental Modelling & Software, 100, 159-174.[PDF]
  • Wilkerson, M. H., Shareff, R., Laina, V., & Gravel, B. (2018). Epistemic gameplay and discovery in computational model-based inquiry activities. Instructional Science, 1-26.[PDF]
  • Xu, X., Sahnoun, M., Abdelaziz, F., Baudry, D., Louis, A.(2018). Multi-objective Flexible Job Shop Scheduling Problem: Simulation Approach [PDF]
  • YACHOU, N., & ABOULAICH, R. (2018). Agent Based Modeling and Simulation for Home Financing. Application in Netlogo Platform. Journal of Applied Economic Sciences, 13(3).
  • Yahyaoui, F., & Tkiouat, M. (2018). Agent-based co-modeling of information society and wealth distribution. International Journal of Advanced Computer Science and Applications, 9(11), 201-206.
  • Yan, J., Liu, R., & Zhang, G. (2018). Task structure, individual bounded rationality and crowdsourcing performance: An agent-based simulation approach. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3854
  • Yarbrough, B., & Wagner, N. (2018, April). Assessing security risk for wireless sensor networks under cyber attack. In Proceedings of the Annual Simulation Symposium (p. 1). Society for Computer Simulation International.[PDF]
  • Yaşar, O. (2018). A new perspective on computational thinking. Communications of the ACM, 61(7), 33-39.
  • YIN, G., LI, G., ZHU, T., & CHEN, J. (2018). Study on Multi-Agent System of Energy Managements for 4WD Electric Vehicles. China Mechanical Engineering, 29(15), 1765.
  • Yoo, E. (2018). Dynamics of Information Distribution on Social Media Platforms during Disasters (Doctoral dissertation, Arizona State University).[PDF]
  • Yoon, S. A., Goh, S. E., & Park, M. (2018). Teaching and Learning About Complex Systems in K–12 Science Education: A Review of Empirical Studies 1995–2015. Review of Educational Research, 88(2), 285-325.[PDF]
  • Yousefi, M., Yousefi, M., Ferreira, R. P. M., Kim, J. H., & Fogliatto, F. S. (2018). Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artificial intelligence in medicine.[PDF]
  • Yunming, W., Si, C., Chengsheng, P., & Bo, C. (2018). Measure of invulnerability for command and control network based on mission link. Information Sciences, 426, 148-159.[PDF]
  • Zeppini, P. & Frenken, K. (2018). Networks, percolation, and consumer demand. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3658
  • Zhang, X., He, Junhui.(2018). Nature-Inspired Computational Model of Population Desegregation Under Group Leaders Influence. Proceedings of the Fifth International Forum on Decision Sciences.[PDF]
  • Zhao, C., Li, S., Wang, W., Li, X., & Du, Y. (2018). Advanced parking space management strategy design: an agent-based simulation optimization approach. Transportation Research Record, 2672(8), 901-910.
  • ZHENG, Y., ZHANG, G., MA, R., & SHU, H. (2018). Research on Influence of Mobile Social Network on College Students' Campus Life: from Perspective of Multi-Agent Evolution Simulation. China Educational Technology & Equipment, 04.
  • ZHOU, X. N., WANG, J. S., & Yin, Z. H. U. (2018). Simulation Research of Vehicle Lane-changing Behavior Evolutionary Game Model Based on NetLogo. DEStech Transactions on Engineering and Technology Research, (ecar).
  • Zhu, M., Panorkou, N., Lal, P., Etikyala, S., Germia, E., Iranah, P., ... & Basu, D. (2018, March). Integrating interactive computer simulations into K-12 earth and environmental science. In 2018 IEEE Integrated STEM Education Conference (ISEC) (pp. 220-223). IEEE.
  • Zia, K., Saini, D. K., Muhammad, A., & Ferscha, A. (2018). Customer Participation in the Internet of Things: A Bayesian Game Model. IEEE Transactions on Computational Social Systems.[PDF]
  • Zinelli Jr, M. (2018, October). Multi-agent Simulation of a Real Evacuation Scenario: Kiss Nightclub and the Panic Factor. In Multi-Agent Systems and Agreement Technologies: 15th European Conference, EUMAS 2017, and 5th International Conference, AT 2017, Evry, France, December 14-15, 2017, Revised Selected Papers (Vol. 10767, p. 268). Springer.
  • Zoto, E., Kowalski, S., Lopez-Rojas, E. A., & Kianpour, M. Using a socio-technical systems approach to design and support systems thinking in cyber security education.[PDF]

2017

  • à Campo, S. Agent-Based Modelling for Online Community Designers. Paper presented at the Conference on Humana Factors in Computing Systems.[PDF]
  • A spatial explicit agent based model approach to evaluate the performance of different monitoring options for mortality estimates in the scope of onshore windfarm impact assessments
  • Abbott, R., & Hadžikadić, M. (2017).Complex Adaptive Systems, Systems Thinking, and Agent-Based Modeling. In Advanced Technologies, Systems, and Applications (pp. 1-8). Springer International Publishing. [PDF]
  • ACOSTA, C., BORGESIUS, F., & VAN HATTUM, J. E. S. S. I. E. Facilitating Collective Action for an Integrated Community Energy System.[PDF]
  • Alzaeemi, S. A. S., Sathasivam, S., & Adebayo, S. A. (2017). Analysis of Performance of Various Activation Functions for doing the logic programming in Hopfield Network. International Journal of Computational Bioinformatics and In Silico Modeling, 6(2), 911-921. [ PDF]
  • Alzahrani, E., Richmond, P., & Simons, A. J. (2017, August). A formula-driven scalable benchmark model for ABM, applied to FLAME GPU. In European Conference on Parallel Processing (pp. 703-714). Springer, Cham.[ PDF]
  • Ampatzidis, G., & Ergazaki, M. (2017). Toward an “Anti-Balance of Nature” Learning Environment for Non-Biology Major Students: Learning Objectives and Design Criteria. Natural Sciences Education, 46(1).[HTML]
  • Anderson, J. H., Downs, J. A., Loraamm, R., & Reader, S. (2017). Agent-based simulation of Muscovy duck movements using observed habitat transition and distance frequencies. Computers, Environment and Urban Systems, 61, 49-55. [PDF]
  • Auerbach, S., & Dix, R. (2017). Competition and spatial efficiency.[PDF]
  • Aydin, M. E., & Fellows, R. (2017). A reinforcement learning algorithm for building collaboration in multi-agent systems. arXiv preprint arXiv:1711.10574.[PDF]
  • Badham, J., Jansen, C., Shardlow, N., & French, T. (2017). Calibrating with multiple criteria: A demonstration of dominance. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3212
  • Balev, S., Dutot, A., & Olivier, D. (2017). Networking, Networks and Dynamic Graphs. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 85-116).[HTML]
  • Ballet, P., Rivière, J., Pothet, A., Theron, M., Pichavant, K., Abautret, F., ... & Rodin, V. (2017).Modelling and Simulating Complex Systems in Biology: Introducing NetBioDyn–A Pedagogical and Intuitive Agent-Based Software. In Multi-Agent-Based Simulations Applied to Biological and Environmental Systems (pp. 128-158). IGI Global. [ HTML]
  • Banati, H., Bhattacharyya, S., Mani, A., & Köppen, M. (Eds.). (2017). Hybrid Intelligence for Social Networks. Springer International Publishing.[PDF]
  • Banos, A., Corson, N., Daudé, É., Gaudou, B., & Coyrehourcq, S. R. (2017). Macro Models, Micro Models and Network-based Coupling. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 63-84).[ HTML]
  • Barker, A. K., Alagoz, O., & Safdar, N. (2017). Interventions to reduce the incidence of hospital-onset Clostridium difficile infection: An agent-based modeling approach to evaluate clinical effectiveness in adult acute care hospitals. Clinical Infectious Diseases.[PDF]
  • Barrientos, A. H. (2017). The Evolutionary Dynamics of the Mixe Language. In Sociolinguistics-Interdisciplinary Perspectives. InTech.[PDF]
  • Barrientos, A. H., & Andrade, Y. D. (2017). Modelling and Simulation of Complex Adaptive System: The Diffusion of Socio-Environmental Innovation in the RENDRUS Network. In Cvetkovic, D (Ed.)Computer Simulation. InTech. [HTML]
  • Bastien-Olvera, B., Bautista-Gonzalez, E., & Gay-Garcia, C. An agent-based model of food-borne diseases under climate change scenarios in Mexico City.[PDF]
  • Belete, G.F., Voinov, A., Morales, J. (2017). Environmental Modelling & Software. Volume 94, (pp 112–126). [HTML]
  • Bent, J. (2017). Autonomous UAV Path Planning for Wildfire Data Collection. Computer Science Thesis. [PDF]
  • Beutler, J. D. Online Waiting Time Information.
  • Bezzout, H., Hsaini, S., Azzouzi, S., & El Faylali, H. (2017). Simulation of electromagnetic waves propagation in free space using Netlogo multi-agent approach. In Proceedings of the 2nd international Conference on Big Data, Cloud and Applications (p. 112). ACM.[PDF]
  • Binmad, R., & Li, M. (2017). Improving the Efficiency of an Online Marketplace by Incorporating Forgiveness Mechanism. ACM Transactions on Internet Technology (TOIT), 17(1), 9. [PDF]
  • Birks, D., & Davies, T. (2017). STREET NETWORK STRUCTURE AND CRIME RISK: AN AGENT‐BASED INVESTIGATION OF THE ENCOUNTER AND ENCLOSURE HYPOTHESES. Criminology, 55(4), 900-937.[PDF]
  • Bogatzky, N. (2017). A "gung-ho" Approach Towards Sophic Economy. Economic Alternatives, (1), 160-186. [PDF]
  • Brown, A. J. (2017). DEVELOPMENT OF A SUPPLIER SEGMENTATION METHOD FOR INCREASED RESILIENCE AND ROBUSTNESS: A STUDY USING AGENT BASED MODELING AND SIMULATION. Theses and Dissertations--Mechanical Engineering. 100.[PDF]
  • Brown, A., & Badurdeen, F. (2017). Supplier Segmentation Method for Selection of Resilience-Enabling Procurement Strategies. In IIE Annual Conference. Proceedings (pp. 656-661). Institute of Industrial and Systems Engineers (IISE).[PDF]
  • Bruya, B. (2017). Ethnocentrism and Multiculturalism in Contemporary Philosophy. Philosophy East and West, 67(4), 991-1018.[PDF]
  • Bui, H., Pence, J., Mohaghegh, Z., Reihani, S., & Kee, E. (2017). Spatio-temporal socio-technical risk analysis methodology: an application in emergency response. In American nuclear society (ANS) international topical meeting on probabilistic safety assessment and analysis (PSA). American Nuclear Society Pittsburgh, PA.[PDF]
  • Burrows, A. C. (2017). Teaching Teachers to Think Like Engineers Using NetLogo. Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio.
  • Buurma, J., Hennen, W., & Verwaart, T. (2017). How social unrest started innovations in a food supply chain. Journal of Artificial Societies and Social Simulation, 20(1). doi.org/10.18564/jasss.3350
  • Caillou, P., Coyrehourq, S. R., Marilleau, N., & Banos, A. (2017). Exploring Complex Models in NetLogo. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 173-208).[HTML]
  • Calvaresi, D., Marinoni, M., Lustrissimini, L., Appoggetti, K., Sernani, P., Dragoni, A. F., ... & Buttazzo, G. Local Scheduling in Multi-Agent Systems: getting ready for safety-critical scenarios. In Proceedings of 15th European Conference on Multi-Agent Systems. Springer (Dec 2017).[ PDF]
  • Carely, K., Dobson, G. (2017). Cyber-FIT: An Agent-Based Modelling Approach to Simulating Cyber Warfare. Lecture Notes in Computer Science book series, 10354. Springer, Cham[PDF]
  • Carver, S., & Quincey, D. (2017). A Conceptual Design of Spatio-Temporal Agent-Based Model for Volcanic Evacuation. Systems, 5(4), 53.[PDF]
  • Cervantes, D., Flores, D. L., Gutiérrez, E., & Chacón, M. A. (2017). Ce, Tb-Doped Y2SiO5 Phosphor Luminescence Emissions Modeling and Simulation. In Properties and Characterization of Modern Materials (pp. 145-156). Springer Singapore. [PDF]
  • Chavira, M. A. L., & Marcelín-Jiménez, R. (2017). Distributed rewiring model for complex networking: The effect of local rewiring rules on final structural properties. PloS one, 12(11), e0187538.[PDF]
  • Colosimo, A. (2018). Multi-agent Simulations of Population Behavior: A Promising Tool for Systems Biology. In Systems Biology (pp. 307-326). Humana Press, New York, NY.[PDF]
  • Darr, Y. R., & Niazi, M. A. (2017). Towards Self-organized Large-Scale Shape Formation: A Cognitive Agent-Based Computing Approach. arXiv preprint arXiv:1711.06426.[PDF]
  • David, N., Fachada, N., & Rosa, A. C. (2017). Verifying and validating simulations. In Simulating Social Complexity (pp. 173-204). Springer, Cham.[HTML]
  • Davidsson, P., & Verhagen, H. (2017). Types of simulation. In Simulating Social Complexity (pp. 23-37). Springer, Cham.[PDF]
  • de la Fuente, D., Gómez, A., Ponte, B., & Costas, J.Agent-Based Prototyping for Business Management: An Example Based on the Newsvendor Problem.[PDF]
  • de Lima Corrêa, L., Inostroza-Ponta, M., & Dorn, M. (2017, June). An evolutionary multi-agent algorithm to explore the high degree of selectivity in three-dimensional protein structures. In Evolutionary Computation (CEC), 2017 IEEE Congress on (pp. 1111-1118). IEEE. [PDF]
  • Deividi Moreira, Fernando Santos, Matheus Barbieri, Ingrid Nunes, Ana L. C BazzanABStractme: Modularized Environment Modeling in Agent-based Simulations[PDF]
  • dos Santos, T. R. E., & Nakane, M. I. (2017). Dynamic Bank Runs: an agent-based approach (No. 465).[PDF]
  • Dubovi, I., Dagan, E., Nasar, L., Mazbar, O. S., & Levy, S. T. (2017). Follow the Glucose Molecule: Learning Pharmacology by Exploring Multi-Scale Agent-Based Computer Models of Cellular Biochemical Processes and their Interactions Between Organs. [HTML]
  • Fagiolo, G. & Roventini, A. (2017). Macroeconomic policy in DSGE and agent-based models redux: New developments and challenges ahead. Journal of Artificial Societies and Social Simulation, 20(1). doi.org/10.18564/jasss.3280
  • Falcone, R., & Sapienza, A. (2017, November). Using Sources Trustworthiness in Weather Scenarios: The Special Role of the Authority. In Conference of the Italian Association for Artificial Intelligence (pp. 3-16). Springer, Cham.[PDF]
  • Fan, S., Chen, X., & Sun, Q. (2017). Emergent Research of Employee Safety Awareness Based on Multi-agent Model. International Conference on Applied Human Factors and Ergonomics, 17(5), 320-327). [PDF]
  • Feliciani, T., Flache, A., & Tolsma, J. (2017). How, when and where can spatial segregation induce opinion polarization? Two competing models. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3419
  • Fernando, T., & Rupasinghe, T. D. Simulation of Data Plans for Operate Revenues In Telco Industry.[PDF]
  • Frank, K., Xu, R., & Penuel, W. R. (2017). The Micro-Dynamics of Network Leverage: Implications for Change Agents External to an Organization.[PDF]
  • Galland, S., Rodriguez, S., & Gaud, N. (2017). Run-time environment for the SARL agent-programming language: the example of the Janus platform. Future Generation Computer Systems.[PDF]
  • García-Magariño, I., Lombas, A. S., Plaza, I., & Medrano, C. (2017). ABS-SOCI: An Agent-Based Simulator of Student Sociograms. Applied Sciences, 7(11), 1126.[PDF]
  • Gaudou, B., Lang, C., Marilleau, N., Savin, G., Coyrehourcq, S. R., & Nicod, J. M. (2017). NetLogo, an Open Simulation Environment. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 1-36).[HTML]
  • Gotts, N. M. & Polhill, J. G. (2017). Experiments with a model of domestic energy demand. Journal of Artificial Societies and Social Simulation, 20(3). doi.org/10.18564/jasss.3467
  • Grgurina, N., Barendsen, E., Suhre, C., van Veen, K., & Zwaneveld, B. (2017, November). Investigating Informatics Teachers’ Initial Pedagogical Content Knowledge on Modeling and Simulation. In International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 65-76). Springer, Cham.[PDF]
  • Guijun, L., Yongsheng, W., Daohan, H., & Hongtao, Y. (2017, July). A Multi-Agent Model for Urban Water-Energy-Food Sustainable Development Simulation. In Proceedings of the 2nd International Conference on Crowd Science and Engineering (pp. 105-110). ACM.[PDF]
  • Gwiazda, A., Sękala, A., & Banaś, W. (2017). Modeling of a production system using the multi-agent approach. IOP Conference Series: Materials Science and Engineering, 227(1). [PDF]
  • Han, B., Zhang, P., Kuang, H., & Wan, M. Screening of Port Enterprise Value Chain Routines Based on Evolution Equilibrium. Wireless Personal Communications, 1-18.[PDF]
  • Hartbauer, Manfred (2017). Simplified bionic solutions:a simple bio-inspired vehicle collision detection system. Bioinspiration & Biominietics, 12(2). [HTML]
  • Hauke, J., Lorscheid, I., & Meyer, M. (2017). Recent development of social simulation as reflected in JASSS between 2008 and 2014: A citation and co-citation analysis. Journal of Artificial Societies and Social Simulation, 20(1). doi.org/10.18564/jasss.3238
  • Hausmann, S. L., Tietjen, B., & Rillig, M. C. (2017). Solving the puzzle of yeast survival in ephemeral nectar systems: exponential growth is not enough. FEMS microbiology ecology, 93(12), fix150.[PDF]
  • Heredia, M., de Decker, M., Villalta, V., & Vargas, M. (2017). Stochastic model on the mobility of petroleum in the soils of the Ecuadorian Amazon. In MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition. Multidisciplinary Digital Publishing Institute [ HTML ]
  • Herzog C., Pierson JM., Lefèvre L. (2017). Modelling Technology Transfer in Green IT with Multi-agent System. In: Benlamri R., Sparer M. (eds) Leadership, Innovation and Entrepreneurship as Driving Forces of the Global Economy. Springer Proceedings in Business and Economics. Springer, Cham. [PDF]
  • Hickmott, D., Prieto-Rodriguez, E., & Holmes, K. (2017). A Scoping Review of Studies on Computational Thinking in K–12 Mathematics Classrooms. Digital Experiences in Mathematics Education, 1-22.[PDF]
  • Hoekstra, A., Steinbuch, M., & Verbong, G. (2017). Creating agent-based energy transition management models that can uncover profitable pathways to climate change mitigation. Complexity, 2017.[PDF]
  • Huang, W., Krejci, C. C., Dorneich, M. C., & Passe, U. (2017, October). Weatherization adoption in a multilayer social network: An agent-based approach. In Proceedings of the 2017 International Conference of The Computational Social Science Society of the Americas (p. 19). ACM.[PDF]
  • Hussain, I., Khan, M. A., Baqueri, S. F. A., Shah, S. A. R., Bashir, M. K., Khan, M. M., & Khan, I. A. (2017). An Organizational-Based Model and Agent-Based Simulation for Co-Traveling at an Aggregate Level. Applied Sciences, 7(12), 1221.[PDF]
  • Imkeller, M., Sonnleitner, M., Gassner, J., Nies, Y., Bauer, L., Baumann, S., et al. Participatory Simulation for Games with a Purpose–A Case Study. GI_Forum 2017, 1, 397-404.[PDF]
  • Ivanova, Y. (2017). Modelling the impact of cyber attacks on the traffic control centre of an urban automobile transport system by means of enhanced cybersecurity. In MATEC Web of Conferences (Vol. 133, p. 07001). EDP Sciences.[PDF]
  • Janssen, M. A. (2017). The practice of archiving model code of agent-based models. Journal of Artificial Societies and Social Simulation, 20(1). doi.org/10.18564/jasss.3317
  • Jimenez-Romero, C., & Johnson, J. (2017). SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo. Neural Computing and Applications, 28(1), 755-764.[HTML]
  • Johnson, N. F., Manrique, P., Zheng, M., Cao, Z., Botero, J., Huang, S., ... & Restrepo, E. M. (2017). Population polarization dynamics and next-generation social media algorithms. arXiv preprint arXiv:1712.06009.[PDF]
  • Kahn, K. (2017). A half-century perspective on Computational Thinking. Tecnologias, Sociedade e Conhecimento, 4(1), 23-42.[PDF]
  • Khalil, K. M., Abdel-Aziz, M., Nazmy, T. T., & Salem, A. B. M. Multi-Agent Model for Job Scheduling in Cloud Computing.International Journal of Computers 11, p. 11-17.[PDF]
  • Khasawneh, M. T., Shearer, N., Rabadi, G., & Bowling, S. (2017).The information age combat model: A vision for a discrete event simulation approach. International Journal of Simulation and Process Modelling, 12(5), 429. doi:10.1504/ijspm.2017.087604
  • Kittany, J. (2017). Perception of multi-varied sound patterns of sonified representations of complex systems by people who are blind. Journal of Alternative Medicine Research, 9(2), 201.[PDF]
  • Kowalska-Pyzalska, A. (2017, June). Willingess to pay for green energy: An agent-based model in NetLogo platform. In 2017 14th International Conference on the European Energy Market (EEM) (pp. 1-6). IEEE.
  • Krobath, I., Römer, H., & Hartbauer, M. (2017). Plasticity of signaling and mate choice in a trilling species of the Mecopoda complex (Orthoptera: Tettigoniidae). Behavioral ecology and sociobiology, 71(11), 164.[PDF]
  • Lamarque, R. (2017). From variation to the emergence of linguistic regularities. Current Trends in Linguistics, Hamburg, Germany. [ HTML]
  • Langbeheim, E., & Levy, S. T. "You Evaporated?" vs. "It Escaped": A Comparison of Students' Reasoning about Vaporization when Using Participatory and Non-Participatory Simulations. [PDF]
  • Lapates, J. M., & Espina, M. (2017).Social Network Behaviours to Explain the Spread of Online Game. e-Proceedings of the 5th Global Summit on Education 2017. [ PDF]
  • Lawson, T., Rogerson, R., & Barnacle, M. (2017). A comparison between the cost effectiveness of CCTV and improved street lighting as a means of crime reduction. Computers, Environment and Urban Systems. [ PDF]
  • Le Page, C., Abrami, G., Becu, N., Bommel, P., Bonte, B., Bousquet, F., & Taillandier, P. (2017). Lessons from implementing in parallel with 3 platforms the same didactic agent-based model. [HTML]
  • Li, H. B., Miura, R., & Kojima, F. (2017). A study on Quick Device Discovery for Fully Distributed D2D Networks. IEICE Transactions on Communications, 2017NRP0010. [ HTML]
  • Li, J., & He, J. (2017).Evolutionary Game and Simulation on the Internet of Things in Supply Chain. In Proceedings of the Fourth International Forum on Decision Sciences (pp. 271-279). Springer, Singapore. [ HTML]
  • Likotiko, E., Nyambo, D., Mwangoka, J.(2017)"Multi-Agent based IOT Smat Waste Monitoring and Collection Architecture. International Journal of Computer Science, Engineering and Information Technology 7(5). [PDF]
  • Lippert, K. (2017). Towards the Evolution of Information in Digital Ecosystems. University of South Alabama, ProQuest Dissertations Publishing. [PDF]
  • Litterio, A. M., Nantes, E. A., Larrosa, J. M., & Gómez, L. J. (2017). Marketing and social networks: a criterion for detecting opinion leaders. European Journal of Management and Business Economics, 26(3), 347-366. [PDF]
  • Liu, D., Zheng, X., Zhang, C., & Wang, H. (2017). A new temporal–spatial dynamics method of simulating land-use change. Ecological Modelling, 350, 1-10. [PDF]
  • Liu, H., Ludu, A., Klein, J., Spector, M., & Ikle, M. (2017) Innovative Model, Tools, and Learning Environments to Promote Active Learning for Undergraduates in Computational Science & Engineering. Journal of Computational Science Education, 8(3). [PDF]
  • Liu, X. (2017). Evolution and simulation analysis of co-opetition behavior of E-business internet platform based on evolutionary game theory. Cluster Computing, 1-10. [PDF]
  • Liu, Z., Rexachs, D., Epelde, F., & Luque, E. (2017).A simulation and optimization based method for calibrating agent-based emergency department models under data scarcity. Computers & Industrial Engineering, 103, 300-309. [PDF]
  • Lopez-Cruz, O., & Garnica, N. J. (2017, October). Engineering Organizational Absorptive Capacity for Effective Knowledge Transfer. In International Conference on Software Process Improvement (pp. 186-197). Springer, Cham. [HTML]
  • Lotzmann, U. & Neumann, M. (2017). Simulation for interpretation: A methodology for growing virtual cultures. Journal of Artificial Societies and Social Simulation, 20(3). doi.org/10.18564/jasss.3451
  • Ma, S., Jiang, Z., & Liu, W. (2017).Multi-variation propagation prediction based on multi-agent system for complex mechanical product design. Concurrent Engineering: Research and Applications, 1-15. [ PDF]
  • Malanson, G. P., Resler, L. M., & Tomback, D. F. (2017).Ecotone response to climatic variability depends on stress gradient interactions. Climate Change Responses, 4(1), 1. [HTML]
  • Malik, A., & Abdalla, R. (2017).Agent-based modelling for urban sprawl in the region of Waterloo, Ontario, Canada. Modeling Earth Systems and Environment, 3(1), 7. [PDF]
  • Mantese, G. C., & Amaral, D. C. (2017).Comparison of industrial symbiosis indicators through agent-based modeling. Journal of Cleaner Production, 140, 1652-1671. [PDF]
  • Markauskaite, L., Kelly, N., & Jacobson, M. J. (2017). Model-based knowing: How do students ground their understanding about climate systems in agent-based computer models? Research in Science Education, 1-25.[PDF]
  • Martin, P., Newton, A. C., Cantarello, E., & Evans, P. M. (2017).Analysis of ecological thresholds in a temperate forest undergoing dieback. PloS one, 12(12), e0189578.[PDF]
  • McConnell, B. J., Smout, S. C., & Wu, M. (2017). Modelling harbour seal movements. Scottish Marine and Freshwater Science (8), 20. PDF]
  • McEntire, K. (2017) Getting off the ground: climbing salamanders and Individual-Based Models.[HTML]
  • Mills, D. A. (2017). Agent Based Modeling: Fine-Scale Spatio-Temporal Analysis of Pertussis. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 37.[PDF]
  • Minjing, P., Xinglin, L., Ximing, L., Mingliang, Z., Xianyong, Z., Xiangming, D., & Mingfen, W. Recognizing intentions of E-commerce consumers based on ant colony optimization simulation. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-11. [HTML]
  • Mittal, A. & Krejci, C. (2017). Integrating Consumer Preferences in Renewable Energy Expansion Planning Using Agent-based Modeling.[PDF]
  • Mittal, A., Huang, W., & Krejci, C. C. (2017, October). Consumer-adoption modeling of distributed solar using an agent-based approach. In Proceedings of the 2017 International Conference of The Computational Social Science Society of the Americas (p. 22). ACM.[PDF]
  • Moreira, D., Santos, F., Barbieri, M., Nunes, I., & Bazzan, A. L. (2017).ABStractme: Modularized Environment Modeling in Agent-based Simulations. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (pp. 1802-1804). Sao Paul, Brazil. [ PDF]
  • Munjal, P., Kumar, S., Kumar, L., & Banati, A. (2017). Opinion Dynamics Through Natural Phenomenon of Grain Growth and Population Migration. In Hybrid Intelligence for Social Networks (pp. 161-175). Springer, Cham.[ PDF]
  • Muqaddas, A., & Niazi, M. A. (2017). Modeling the Multiple Sclerosis Brain Disease Using Agents: What Works and What Doesn't?. arXiv preprint arXiv:1712.00190.[PDF]
  • Muscalagiu, C.G., Muscalagiu, I., Muscalagiu,D.M. (2017).The curving calculation of a mechanical device attached to a multi-storey car park. IOP Conf. Series: Materials Science and Enginneering (163). [ HTML]
  • Nabinejad, Shima, Schuttrumpf, HolgerAn Agent Based Model for Land Use Policies In Coastal Areas[PDF]
  • Nam Bui, K. H. & Jung, J. J. (2017). Cooperative game-theoretic approach to traffic flow optimization for multiple intersections. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng. [PDF]
  • Naqvi, A. (2017). Deep impact: Geo-simulations as a policy toolkit for natural disasters. World Development, 99, 395-418.
  • Nguyen Dang, An.(2017) Migrating Behavior Search’s User Interface from Swing to JavaFX. Poster presented at Celebration of Learning. Augustana College, Illinois.[ PDF]
  • Obaidat, M. S., Ören, T., & Merkuryev, Y. (Eds.). (2017). Simulation and Modeling Methodologies, Technologies and Applications: International Conference, SIMULTECH 2016 Lisbon, Portugal, July 29-31, 2016, Revised Selected Papers (Vol. 676). Springer.[ PDF]
  • Oldham, M. (2017) Introducing a multi-asset stock market to test the power of investor networks. Journal of Artificial Societies and Social Simulation, 20(4). doi.org/10.18564/jasss.3497
  • Ozaeta, L., & Graña, M. (2017). Finding Communities in Recommendation Systems by Multi-agent Spatial Dynamics. In International Conference on Hybrid Artificial Intelligence Systems (pp. 577-587). Springer, Cham.[ HTML]
  • Park, M., Liu, X., & Waight, N. (2017).Development of the Connected Chemistry as Formative Assessment Pedagogy for High School Chemistry Teaching. Journal of Chemical Education. [PDF]
  • Patrzyk, P. M. & Takáč, M. (2017). Cooperation via intimidation: An emergent system of mutual threats can maintain social order. Journal of Artificial Societies and Social Simulation, 20(4). doi.org/10.18564/jasss.3336
  • Perry, G. L., & O’Sullivan, D. (2017). Identifying Narrative Descriptions in Agent-Based Models Representing Past Human-Environment Interactions. Journal of Archaeological Method and Theory, 1-23.[PDF]
  • Petit, F. M. (2017). Scientific Modelling and Emergence (Bachelor's thesis).[PDF]
  • Plewe, D. A., & Lee, H. (2017).Simulating the Outcomes of Contracts: A Visual Interface Supporting Start-Up Financing. In Advances in Human Factors, Business Management, Training and Education (pp. 823-831). Springer International Publishing. [HTML]
  • Polhill, G., & Salt, D. (2017). The Importance of Ontological Structure: Why Validation by ‘Fit-to-Data’Is Insufficient. In Simulating Social Complexity (pp. 141-172). Springer, Cham.[PDF]
  • Primiero, G., Raimondi, F., Bottone, M., & Tagliabue, J. (2017).Trust and distrust in contradictory information transmission. Applied Network Science, 2(1). [ HTML]
  • Procházka J., Štekerová K. (2017) OpenCL for Large-Scale Agent-Based Simulations. In: Nguyen N., Papadopoulos G., Jędrzejowicz P., Trawiński B., Vossen G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science, vol 10448. Springer, Cham[ HTML]
  • Puga-Gonzalez, I. & Sueur, C. (2017). Friendships and social networks in an individual-based model of primate social behavior. Journal of Artificial Societies and Social Simulation, 20(3). doi.org/10.18564/jasss.3450
  • Puig Camps, B. (2017). Analysis and enhancement of an individual based model strategy to study tuberculosis at a city level. (Bachelor's thesis, Universitat Politècnica de Catalunya).[PDF]
  • Pumain, D., & Reuillon, R. (2017). An Innovative and Open Toolbox. In Urban Dynamics and Simulation Models. Springer International Publishing.[PDF]
  • Putra, H. C., Andrews, C. J., & Senick, J. A. (2017). An agent-based model of building occupant behavior during load shedding. In Building Simulation (pp. 1-15). Tsinghua University Press.[PDF]
  • Qi, Y., Wang, X., & Chen, C. (2017, December). Research on Arrival Integration Method for Point Merge System in Tactical Operation. In International Conference on Combinatorial Optimization and Applications (pp. 417-425). Springer, Cham.[PDF]
  • Qiu, R., Xu, W., Zhang, J., & Staenz, K. (2017). Modelling and Simulating Urban Residential Land Development in Jiading New City, Shanghai. Applied Spatial Analysis and Policy, 1-25.[PDF]
  • Railsback, S., Ayllón, D., Berger, U., Grimm, V., Lytinen, S., Sheppard, C., & Thiele, J. (2017). Improving Execution Speed of Models Implemented in NetLogo. Journal of Artificial Societies and Social Simulation, 20(1), 1-3. [ HTML]
  • Ramírez, S. Q., Castañeda, W. L. R., & Velásquez, J. R. (2017). Representation of unlearning in the innovation systems: A proposal from agent-based modeling. Estudios gerenciales, 33(145), 366-376.[PDF]
  • Rangoni, R. & Jager, W. (2017). Social dynamics of littering and adaptive cleaning strategies explored using agent-based modelling. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3269
  • Rani, G. A., Krishna, V. R., & Murthy, G. V. (2017). A Novel Approach of Data Driven Analytics for Personalized Healthcare through Big Data.[PDF]
  • Raviraj, P. (2017). IoT Based Intelligent Traffic Information System with the integration of SCOOT Control and Secured Automotive Communication System: A brief Analysis.International Journal of Computer and Mathematical Sciences 6(9), p. 326-332.[ PDF]
  • Riou, J., Boëlle, P. Y., Christie, J. D., & Thabut, G. (2017). High emergency organ allocation rule in lung transplantation: a simulation study. ERJ open research, 3(4), 00020-2017.[ PDF]
  • Robinson, Luke D. (2017) Unsupervised Machine Learning in Agent-Based Modeling. Poster presented at Celebration of Learning. Augustana College, Illinois.[ PDF]
  • Rovbo, M. A., & Ovsyannikova, E. E. (2017). Simulating robot groups with elements of a social structure using KVORUM. Procedia computer science, 119, 147-156.[ PDF]
  • Ryder, E. F., Boyd, J. R., La, S., Sullender, M. E., Marsden, T. B., Jennings, R., Reider, D., & White, B. T. (2017). Teaching students to build biological simulations using agent-based modeling. AAAS Annual Meeting, Boston, MA, United States.
  • Saft, D., & Nissen, V. (2017). A Toolbox to Analyze Emergence in Multiagent Simulations. In Multi-agent Systems. InTech. [PDF]
  • Samson, B. P. V., Marcaida, C. N. P., Gervasio, E. A. B., Militar, R. D., & Ibanez, J. (2017). Analyzing Congestion Dynamics in Mass Rapid Transit using Agent-Based Modeling. PCSC’17, Cebu City, Philippines.[ PDF]
  • Sanchez-Cartas, J. M., & Leon, G. (2017). A Game-Theory-Based Price-Optimization Algorithm for the Simulation of Markets Using Agent-Based Modelling. World Academy of Science, Engineering and Technology, International Journal of Economics and Management Engineering, 4(6).
  • Scalco, K. C., Talanquer, V., Kiill, K. B., & Cordeiro, M. R. (2017). Making Sense of Phenomena from Sequential Images versus Illustrated Text. Journal of Chemical Education, 95(3), 347-354.[PDF]
  • Schuchard, K., Sicking, U., & Donaldson, S. (2017). Modeling Nitrate Uptake in Freshwater Phytoplankton. Proceedings of 2017 NCUR. Chicago.[PDF]
  • Schulze, J., Müller, B., Groeneveld, J., & Grimm, V. (2017). Agent-based modelling of social-ecological systems: Achievements, challenges, and a way forward. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3423
  • Scotti, M., Hartvig, M., Winemiller, K. O., Li, Y., Jauker, F., Jordán, F., & Dormann, C. F. (2017). 15 Trait-Based and Process-Oriented Modeling in Ecological Network Dynamics. Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems, 228.[HTML]
  • Seri, R., & Secchi, D. (2017). How Many Times Should One Run a Computational Simulation? In Simulating Social Complexity (pp. 229-251). Springer, Cham.[ PDF]
  • Shevchuk, G. K., Zvereva, O. M., & Medvedev, M. A. (2017, November). Imbalance detection in a manufacturing system: An agent-based model usage. In AIP Conference Proceedings (Vol. 1906, No. 1, p. 070013). AIP Publishing.[HTML]
  • Shim, J., & Bliemel, M. (2017). Ignition of new product diffusion in entrepreneurship: An agent-based approach. Entrepreneurship Research Journal.[PDF]
  • Singleton, A. D., Spielman, S., & Folch, D. (2017). Urban analytics. Sage.[HTML]
  • SJOBERG, C., KARDUNI, A., BEORKREM, C., & ELLINGER, J. Animal: An Agent-Based Model of Circulation logic for Dynamo.[ PDF]
  • Sobkowicz, P. (2017). Utility, impact, fashion and lobbying: An agent-based model of the funding and epistemic landscape of research. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3399
  • Stefanelli, A. & Seidl, R. (2017). Opinion communication on contested topics: How empirics and arguments can improve social simulation. Journal of Artificial Societies and Social Simulation, 20(4). doi.org/10.18564/jasss.3492
  • Stöckelhuber, K.W., Wießner, S., Das, A., & Heinrich, G. (2017).Filler flocculation in polymers - a simplified model derived from thermodynamics and game theory. Soft Matters, 12(20). [PDF]
  • Suescún, C. G. P., Aragón, C. J. E., Gómez, M. A. J., & Moreno, R. J. (2017, September). Obstacle Evasion Algorithm for Clustering Tasks with Mobile Robot. In Workshop on Engineering Applications (pp. 84-95). Springer, Cham.[PDF]
  • Sun, X., Zhao, X., & Robaldo, L. (2017). Ali Baba and the thief, convention emergence in games. Journal of Artificial Societies and Social Simulation, 20(3). doi.org/10.18564/jasss.3421
  • Swanson, H., Anton, G., Bain, C., Horn, M., Wilensky, U.(2017).Computational Thinking in the Science Classroom. Proceedings of the 1st International Conference on Computational Thinking Education.[PDF]
  • Taherian, M., & Mousavi, S. M. (2017).Modeling and simulation of forward osmosis process using agent-based model system. Computers & Chemical Engineering, 100, 104-118. [PDF]
  • Thomas, D., Lin, S. (2017).Inquiry-Based Science and Mathematics Using Dynamic Modeling. SCIREA Journal of Mathematics, 2(2). [PDF]
  • Troitzsch, K. G. (2017). Axiomatic Theory and Simulation: A Philosophy of Science Perspective on Schelling's Segregation Model. Journal of Artificial Societies and Social Simulation, 20(1). [HTML]
  • Tubadji, A., Angelis, V., Nijkamp, P. (2017).Micro-Cultural Preferences and Macro-Percolaton of New Ideas: Netlogo Simulation. Journal of Knowledge Economy, 1-18. [ HTML]
  • van der Veen, R. A. C., Kisjes, K. H., & Nikolic, I. (2017). Exploring policy impacts for servicising in product-based markets: A generic agent-based model. [PDF] Journal of Cleaner Production.
  • van der Wal, C. N., Formolo, D., Robinson, M. A., Minkov, M., & Bosse, T. (2017). Simulating crowd evacuation with socio-cultural, cognitive, and emotional elements. In Transactions on Computational Collective Intelligence XXVII (pp. 139-177). Springer, Cham.[PDF]
  • Varughese, J. C., Thenius, R., Schmickl, T., & Wotawa, F. (2017). Quantification and analysis of the resilience of two swarm intelligent algorithms. C. Benzmuller, C. Lisetti, M. Theobald (eds.) GCAI, 148-161.[PDF]
  • Varughese, J. C., Thenius, R., Wotawa, F., & Schmickl, T. (2017). swarmfstaxis: Borrowing a swarm communication mechanism from fireflies and slime mold. In Proceedings of the Twenty First Annual Meeting on Agent Based Modeling and Simulation. Springer.[PDF]
  • Vasiljevska, J., Douw, J., Mengolini, A., & Nikolic, I. (2017). An agent-based model of electricity consumer: Smart metering policy implications in Europe. Journal of Artificial Societies and Social Simulation, 20(1). doi.org/10.18564/jasss.3150
  • Velazquez, J. J., Su, E., Cahan, P., & Ebrahimkhani, M. R. (2017). Programming Morphogenesis through Systems and Synthetic Biology. Trends in biotechnology.[PDF]
  • Vermeer, W., Head, B., & Wilensky, U. (2017). “The Effects of Local Network Structure on Disease Spread in Coupled Networks.” In Complex Networks & Their Applications V, 487–98. Studies in Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_39.
  • Vila Guilera, J. (2017). Analysis and individual-based modelling of the tuberculosis epidemiology in Barcelona. The role of age, gender and origin (Bachelor's thesis, Universitat Politècnica de Catalunya).[PDF]
  • Wagh, A., & Wilensky, U. (2017). EvoBuild: A Quickstart Toolkit for Programming Agent-Based Models of Evolutionary Processes. Journal of Science Education and Technology, 1-16.[PDF]
  • Wagh, A., Cook‐Whitt, K., & Wilensky, U. (2017). Bridging inquiry‐based science and constructionism: Exploring the alignment between students tinkering with code of computational models and goals of inquiry. Journal of Research in Science Teaching. [PDF]
  • Wallentin, G., & Neuwirth, C. (2017).Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model. Ecological Modelling, 345, 165-175. [PDF]
  • Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education (TOCE), 18(1), 3.[PDF]
  • Weiss, M. B., Krishnamurthy, P., & Gomez, M. M. (2017, March). How can polycentric governance of spectrum work? In Dynamic Spectrum Access Networks (DySPAN), 2017 IEEE International Symposium on (pp. 1-10). IEEE.[PDF]
  • Winkler, R. P., & Metu, S. (2017). An Extensible NetLogo Model for Visualizing Message Routing Protocols (No. ARL-SR-0380). US Army Research Laboratory Aberdeen Proving Ground United States.[PDF]
  • Yahyaoui, F., Tkiouat, M. 2017. A multi-level agent-based model of reinsurance. Journal of Applied Economic Sciences, Volume XII, Summer 3(49): 746– 752.[PDF]
  • Yasar, O., Maliekal, J., Veronesi, P., & Little, L. (2017). The essence of scientific and engineering thinking and tools to promote it. In Proceedings of the American Society of Engineering Education Annual Conference.
  • Ye, P., & Wang, F. Y. (2017). Hybrid calibration of agent-based travel model using traffic counts and AVI data. In Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on (pp. 457-462). IEEE.[PDF]
  • Yeager, M. E. (2017). Structural Complexity, Seascape Patchiness, and Body Size Interactively Mediate Seagrass Habitat Value for a Fish Mesopredator (Doctoral dissertation, San Diego State University).[PDF]
  • Yu, B., Ren, S., Wu, E., Zhou, Y., & Wang, Y. (2017). Optimization of urban bus operation frequency under common route condition with rail transit. Frontiers of Engineering Management, 4(4), 451-462.
  • Yu, S., Hong, L., & Tian, W. (2017). An Integrated Spatial Analysis Computer Environment for Building Energy Efficiency at City Scale. UIA 2017 Seoul World Architects Congress.[PDF]
  • Zamzami, N., & Schiffauerova, A. (2017).The impact of individual collaborative activities on knowledge creation and transmission. Scientometrics, 111(3), 1385-1413. [PDF]
  • Zandi, M. (2017).Simulation of Ascorbic Acid Release from Alginate-Whey Protein Concentrates Microspheres at the Simulated Gastrointestinal Condition Using Netlogo Platform. Journal of Food Process Engineering, 40. [PDF]
  • Zeng, Y., Li, S., & Deng, L. (2017, October). The measurement, mathematical and logical modeling, and agent-based simulation of carbon intangible assets with embedded strategies. In Knowledge Engineering and Applications (ICKEA), 2017 2nd International Conference on (pp. 76-80). IEEE.[ PDF]
  • Zhang, L., & Zeng, Z. (2017).Cascading Failure in the Maximum Entropy Based Dense Weighted Directed Network: An Agent-based Computational Experiment. [ PDF]
  • Zhao, H., & Sun, Y. (2017). Communication effect of passengers on information diffusion in metro emergency. Wuhan University Journal of Natural Sciences, 22(6), 503-509.[ PDF]
  • Zhao, Y., Ortt, R. J., & Katzy, B. R. Agent Based Simulation of Technological Innovation using Hypercycle Model.Center for Technology Innovation and Management, the Netherlands.[ PDF]
  • Zia, K., Din, A., Shahzad, K., & Ferscha, A. (2017).A Cognitive Agent-based Model for Multi-Robot Coverage at a City Scale. Complex Adaptive Systems Modeling, 5(1), 1. [ HTML]
  • Zinterhof, P. (2017). Vectorization of Cellular Automaton-Based Labeling of 3-D Binary Lattices. In Sustained Simulation Performance 2017 (pp. 89-109). Springer, Cham.[PDF]
  • Zschache, J. (2017). The explanations of social conventions by melioration learning. Journal of Artificial Societies and Social Simulation, 20(3). doi.org/10.18564/jasss.3428

2016

  • Abrahamson, D. (Chair), D. Clements (Discussant), & K. Chase (Organizer) (2016). Discovery-based STEM learning 2.0: Are we there yet? Symposium presented at the annual meeting of the American Educational Research Association (Special Interest Group: Learning Sciences), Washington, DC, April 8 – 12.
  • Amarasinghe, U. G. L. S., & Rajapakse, C. (2016). Urban traffic simulation using agent-based modelling: A study in the Sri Lankan context. In Proceedings of the International Research Symposium on Pure and Applied Sciences. Sri Lanka. [ PDF ]
  • Ameerbakhsh, O., Maharaj, S., Hussain, A., Paine, T., & Taiksi, S. (2016, September). An exploratory case study of interactive simulation for teaching Ecology. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1-7). IEEE.
  • Araujo Granda, P., Gras, A., Ginovart, M. (2016). MbT-Tool: An open-access tool based on Thermodynamic Electron Equivalents Model to obtain microbial-metabolic reactions to be used in biotechnological process. Computational and Structural Biotechnology Journal, 14: 325-332. [ HTML]
  • Araujo Granda, P., Gras, A., Ginovart, M., Moulton, V. (2016). INDISIM-Paracoccus, an individual-based and thermodynamic model for a denitrifying bacterium.Journal of Theoretical Biology, 403: 45-58. [ HTML]
  • Aslan, U., & Wilensky, U. (2016). Old Tricks Revisited: Studying Probabilistic Reasoning through Incorporating Computer Modeling into Piagetian Research. In Jean Piaget Society 46th annual meeting (pp. 9-11).
  • Aslan, U., & Wilensky, U. (2016). Restructuration in Practice: Challenging a Pop-Culture Evolutionary Theory through Agent Based Modeling. Proceedings of the Constructionism 2016 Conference. Bangkok, Thailand.
  • Bahle G., Poxrucker A., Kampis G., Lukowicz P. (2016). An Adaptive and Dynamic Simulation Framework for Incremental, Collaborative Classifier Fusion. Communications in Computer and Information Science (CCIS, volume 674). [HTML]
  • Barrientos, A. H., & de la Mota, I. F. (2016). Modeling Sustainable Supply Chain Management as a Complex Adaptive System: The Emergence of Cooperation. In Krmac, E. (Ed.) In Sustainable Supply Chain Management. InTech. [ HTML]
  • Barroso, C. J. V., & Babanto, R. R. P. (2016). Unwanted Teenage Pregnancies: Sociological Model Based on Agents. Asia Pacific Journal of Social and Behavioral Sciences, 13.[ PDF ]
  • Beriro, D, Cave, M., Wragg, J., Hughes, A. (2016). Agent based modelling : initial assessment for use on soil bioaccessibility. British Geological Survey (pp 29). [ PDF]
  • Bezirgiannis, N., Prasetya, I. S. W. B., & Sakellariou, I. (2016). HLogo: A Parallel Haskell Variant of NetLogo. In proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. Lisbon, Portugal. (pp. 119-128). [ PDF]
  • Bollinger, L. A., van Blijswijk, M. J., Dijkema, G. P., & Nikolic, I. (2016). An Energy Systems Modelling Tool for the Social Simulation Community. Journal of Artificial Societies and Social Simulation, 19(1). [HTML]
  • Boumans, I.J.J.M., Hofstede, G.J., Bolhuis, E.J., de Boer, I.J.M., Bokkers, E.A.M. (2016). Agent-based modelling in applied ethology: An exploratory case study of behavioural dynamics in tail biting in pigs. Applied Animal Behaviour Science, Volume 183, (pp 10-18). [PDF]
  • Brady, C., Orton, K., Weintrop, D., Anton, G., Rodriguez, S. & Wilensky, U. (2016). All Roads Lead to Computing: Making, Participatory Simulations, and Social Computing as pathways to Computer Science. IEEE Transactions on Education, 60(99), 1-8.[PDF]
  • Brady, C., Weintrop, D., Anton, G., & Wilensky, U. (2016). Constructionist Learning at the Group Level with Programmable Badges. Proceedings of the Constructionism 2016 Conference. Bangkok, Thailand.[PDF]
  • Brewer, K., & Bareiss, C. (2016). Introduction to Computational Science. In Concise Guide to Computing Foundations (pp. 1-8). Springer, Cham.
  • Brewer, K., Bareiss, C. (2016). Procedures: Algorithms and Abstraction. Concise Guide to Computing Foundations (pp 45-57).[PDF]
  • Brughmans, T. & Poblome, J. (2016). MERCURY: An agent-based model of tableware trade in the Roman east. Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.2953
  • Calik, S. K., Kugu, E., Birtane, S., & Sahingoz, O. K. (2016). A Multi Agent Solution for UAV Path Planning Problem with NetLogo. International Journal of Applied Engineering Research, 11(15), 8397-8401. [HTML]
  • Canavesio, M.M., Quaglia, C., Martínez, E. Agent-based simulation of a project fractal company XXII Congreso Argentino de Ciencias de la Computación (CACIC 2016). [PDF]
  • Carbo, J., Sanchez-Pi, N., & Molina, J. M. (2016). Agent-based simulation with NetLogo to evaluate ambient intelligence scenarios. Journal of Simulation. [PDF]
  • Cardona, P.J., & Prats, C. (2016). The Small Breathing Amplitude at the Upper Lobes Favors the Attraction of Polymorphonuclear Neutrophils to Mycobacterium tuberculosis Lesions and Helps to Understand the Evolution toward Active Disease in An Individual-Based Model. Frontiers in Microbiology 7. https://doi.org/10.3389/fmicb.2016.00354
  • Cartel, J. E., & Clutario, W. A. (2016). Socio-Environmental Agent-Based Simulation on the Livability of Two Cities. Journal of Science, Engineering and Technology, 4, 42-48. [HTML]
  • Cegielski, Wendy H., Rogers, Daniel J. Rethinking the role of Agent-Based Modeling in archaeology, Journal of Anthropological Archaeology, Volume 41, 2016, Pages 283-298,ISSN 0278-4165,https://doi.org/10.1016/j.jaa.2016.01.009
  • Cervantes, D. Flores, D.L., Gutiérrez, E., Chacón, M.A.(2016). Ce,Tb-Doped Y2SiO5 Phosphor Luminescence Emissions Modeling and Simulation. Properties and Characterization of Modern Materials. Volume 33 of the series Advanced Structured Materials (pp 145-156).[PDF]
  • Chandrasekaran, S., & Hougen, D. F. (2016). Trade-Offs in Cooperative Goal Seek using Nano-Devices. June, 13, 15.[HTML]
  • Chen, F., Meng, Q., & Li, F. (2016). Simulation of technology sourcing overseas post-merger behaviors in a global game model. Journal of Artificial Science and Social Simulation, 19(4). doi.org/10.18564/jasss.3122
  • Chen, Z. (2016). An Agent-Based Model for Information Diffusion Over Online Social Networks Kent State University.[PDF]
  • Christos, K., Dimitrios, B., & Dimitrios, A. (2016). Agent-based simulation for modeling supply chains: A comparative case study. International Journal of New Technology and Research, 2(10).
  • Cogliano, J. F., & Jiang, X. (2016). Agent-based computational economics: simulation tools for heterodox research. In F. S. Lee & B. Cronin (Eds.), Handbook of research methods and applications in heterodox economics (pp. 253-271). Edward Elgar Publishing Inc. 10.4337/9781782548461
  • Conales, C. P., Janamjam, C. T., & Polinar, J. U. (2016). A Path to Equality on Wealth Distribution: Basis for PhilippinesPolicy Reforms. Asia Pacific Journal of Social and Behavioral Sciences, 13.[PDF]
  • Egar, M. M., Arbutante, D. C. C., & Cauilan, J. J. (2016). An Agent-Based Model on the Potential of a Dipterocarp Forest Fire. Asia Pacific Journal of Social and Behavioral Sciences, 13.[PDF]
  • Ergazaki, M., & Ampatzidis, G. (2016). Can the idea of'Balance of Nature'be effectively challenged within a model-based learning environment? Insights from the second cycle of developmental research. [PDF]
  • Espina, M. O., & Lapates, J. M. (2016). Social Network Behaviours to Explain the Spread of Online Game. Asia Pacific Journal of Social and Behavioral Sciences, 13.[PDF]
  • Everton, R. R., de Castro, P.A.L., Sichman, J.S. (2016). Enhancing Classification Accuracy Through Feature Selection Methods. XIII Encontro Nacional de Inteligˆencia Artificial e Computacional. [PDF]
  • Farrenkopf, T., Guckert, M., Urquhart, N., & Wells, S. (2016). Ontology based business simulations. Journal of Artificial Science and Social Simulation, 19(4). doi.org/10.18564/jasss.3266
  • Feliciani, C., & Nishinari, K. (2016). An improved Cellular Automata model to simulate the behavior of high density crowd and validation by experimental data. Physica A: Statistical Mechanics and its Applications. [HTML]
  • Friege, J., Holtz, G., & Chappin, É. J. L. (2016). Exploring homeowners' insulation activity. Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.2941
  • García-Valdecasas, José Ignacio (2016). Simulación Basada en Agentes. Introducción a NetLogo. Cuadernos Metodológicos del Centro de Investigaciones Sociológicas, Madrid, Spain
  • Ge, J. & Polhill, G. (2016). Exploring the combined effect of factors influencing commuting patterns and CO2 emissions in Aberdeen using an agent-based model. Journal of Artificial Science and Social Simulation, 19(3). doi.org/10.18564/jasss.3078
  • Greco, A., Cannizzaro, F., Pluchino, A. (2016). Seismic collapse prediction of frame structures by means of genetic algorithms. Engineering Structures Volume 143, Pages 152–168. [HTML]
  • Greeven, S., Kraan, O., Chappin, É. J. L., & Kwakkel, J. H. (2016). The emergence of climate change mitigation action by society: An agent-based scenario discovery study. Journal of Artificial Science and Social Simulation, 19(3). doi.org/10.18564/jasss.3134
  • Guo, Y., & Wilensky, U. Small Bugs, Big Ideas: Teaching Complex Systems Principles Through Agent-Based Models of Social Insects. Proceedings of Artificial Life Conference 2016 (p. 664).Chicago.[HTML]
  • Hamhalter, A., & Švarný, P. (2016). Simulation of Self-reconfigurable Material Systems. In Applied Mechanics and Materials (Vol. 825, pp. 119-122). Trans Tech Publications Ltd.
  • Hasson, S. T., & Hasan, Z. Y. (2016). Simulating Road Modeling Approach’s in Vanet Environment Using Net Logo. Research Journal of Applied Sciences, 11(10), 1130-1136. [HTML]
  • Hjorth, A., Brady, C., Head, B. & Wilensky, U. (2016). Turtles All the Way Down: Presenting LevelSpace, a NetLogo Extension for Reasoning About Complex Connectedness. Constructionism 2016, Bangkok, Thailand.
  • Hjorth, A., Weintrop., D., Brady C. & Wilensky., U. (2016). LevelSpace: Constructing Models and Explanations Across Levels. Constructionism 2016, Bangkok, Thailand.
  • Hodzic, M., Selman, S., Hadzikadic, M. (2016). Complex Ecological System Modeling. Periodicals of Engineering and Natural Sciences Vol 4, No 1. [PDF]
  • Inovejas, C. J., Mirasol, J. M., Recente, J. M., & Frias, M. (2016). Modeling Civil Unrest in the Philippines. Asia Pacific Journal of Social and Behavioral Sciences, 13.[PDF]
  • JIANG, L., & Yueliang, S. U. (2016). Analysis of the Influence of the Penalty on the Cooperative Behavior Based on Netlogo Computer Simulation. Management Science and Engineering, 10(3), 1-6. [HTML]
  • Jimenez-Romero, C., & Johnson, J. (2016). SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo. Neural Computing and Applications, 1-10. [ HTML]
  • Jing, W., & Ling, C. (2016). Study on the relationship between the team commitment, knowledge sharing and performance. In Logistics, Informatics and Service Sciences (LISS), 2016 International Conference on (pp. 1-4). IEEE. [ pDF]
  • Kowalska-Styczeń, A. & Sznajd-Weron, K. (2016). From consumer decision to market share - unanimity of majority? Journal of Artificial Science and Social Simulation, 19(4). doi.org/10.18564/jasss.3156
  • Kponyo, J. J., K. S. Nwizege, K. A. Opare, A. R. Ahmed, H. Hamdoun, L. O. Akazua, S. Alshehri, and H. Frank. (2016). A Distributed Intelligent Traffic System Using Ant Colony Optimization: A NetLogo Modeling Approach. In Systems Informatics, Modelling and Simulation (SIMS), International Conference on (pp. 11-17). IEEE. [HTML]
  • Krishnan, R. (2016). Biomass residues for power generation: A simulation study of their usage at Liberia’s plantations. Diss. University of Michigan, 2016.[PDF]
  • Kurahashi-Nakamura, T., Mäs, M., & Lorenz, J. (2016). Robust clustering in generalized bounded confidence models. Journal of Artificial Societies and Social Simulation, 19(4). doi.org/10.18564/jasss.3220
  • Lai, Polly & Jacobson, Michael & Markauskaite, Lina. (2016). Agent-Based Models Versus Video-Based Visualizations to Learn Nanoscience Concepts: An Embodied Cognition Perspective.
  • Lamy, F., Quinn, B., Dwyer, R., Thomson, N., Moore, D., & Dietze, P. (2016). TreatMethHarm: An agent-based simulation of how people who use methamphetamine access treatment. Journal of Artificial Science and Social Simulation, 19(2). doi.org/10.18564/jasss.3069
  • Levin, J. A., & Ching, C. C. (2016). A multi-mediator framework for understanding teaching and learning in higher education classrooms.
  • Ma, Y., Shen, Z., & Nguyen, D. T. (2016). Agent-based simulation to inform planning strategies for welfare facilities for the elderly: Day care center development in a Japanese city. Journal of Artificial Science and Social Simulation, 19(4). doi.org/10.18564/jasss.3090
  • Maroulis, S. (2016). Interpreting school choice treatment effects: Results and implications from computational experiments. Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.3002
  • Martínez, D. L., & Halme, A. (2016). MarSim, a Simulation of the MarsuBots Fleet Using NetLogo. In Distributed Autonomous Robotic Systems (pp. 79-87). Springer Japan.[HTML]
  • Mehic, S., Tadano, K., Vicario, E. (2016). Combining Simulation and Mean Field Analysis in Quantitative Evaluation of Crowd Evacuation Scenarios EPEW 2016: Computer Performance Engineering (pp 174-186). [HTML]
  • Monett, D., Navarro-Barrientos, J. E. (2016). Simulating the fractional reserve banking using agent-based modelling with NetLogo Federated Conference on Computer Science and Information Systems (FedCSIS). [PDF]
  • Opiyo, N.N. (2016). Modelling temporal diffusion of PV mircogeneration systems in a rural developing community Energy and Resources Research Institute (University of Leeds). [PDF]
  • Orly, L., Nuha, C., Vadim, T. (2016). Use of a sonification system for science learning by people who are blind. Journal of Assistive Technologies, Vol. 10 Issue: 4, pp.187-198. [HTML]
  • Othmane, A.B., Tettamanzi, A., Villata, S. & Nhan, L.T. (2016). A Multi-context BDI Recommender System: From Theory to Simulation Institute of Electrical and Electronics Engineers Conference (13-16 Oct. 2016). Omaha, NE, USA.[PDF]
  • Ozaeta, L., Graña, M. (2016). Agent-Based Spatial Dynamic Modeling of Opinion Propagation Exploring Delaying Conditions to Achieve Homogeneity.International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. ICEUTE 2016, SOCO 2016, CISIS 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham[HTML]
  • Pardo, M., Wilfredo, F.C. (2016). Agent-based Modeling and Simulation to Adoption Process of Information Technologies in Health Systems Institute of Electrical and Electronics Engineers Latin American Transactions.[PDF]
  • Pashakalaei, A. G. (2016). Optimal Energy Managment System for a Net Zero Building Using Multi-Agent Systems Approach The University of Texas at El Paso. Ann Arbor.[HTML]
  • Plewe, D.A., Lee, H. (2016). Simulating the Outcomes of Contracts: A Visual Interface Supporting Start-Up Financing Advances in Human Factors, Business Management, Training and Education. Advances in Intelligent Systems and Computing, vol 498.[HTML]
  • Prats Soler, C., Montañola-Sales, C., Gilabert-Navarro, J. F., Valls, J., Casanovas, J. C. G., Vilaplana i Massaguer, C., ... & López, D. (2016). Individual-based modeling of tuberculosis in a user-friendly interface. Frontiers in Microbiology, 6(1654).[PDF]
  • Qi, H., Zhang, M., Chen, H., & Liu, F. (2016). Simulation of Chinese Coal Mine Safety Supervision System Performance Based on Netlogo Platform. Journal of Computational and Theoretical Nanoscience, 13(8), 5072-5080.[HTML]
  • Qiuyun, M., Yang, S. (2016). Design and Simulation Analysis of Multi-Agent Online Dissemination Model on the Basis of "The Spiral of Silence" Theory Journal of Computer and Information Science, 1913-8989.[PDF]
  • Rajanikanth, K.N., Meenakshi, D., Kumar, S.B., Nitin, K. (2016). Incorporating adaptivity using learning in avionics self adaptive software: A case study. Institute of Electrical and Electronics Engineers Conference (21-24 Sept. 2016).[PDF]
  • Razzaq, S., Riaz, F., Mehmood, T., & Ratyal, N. I. (2016, April). Multi-factors based road accident prevention system. In 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) (pp. 190-195). IEEE.
  • Rouchier, J. & Tanimura, E. (2016). Learning with communication barriers due to overconfidence: What a "model-to-model analysis" can add to the understanding of a problem. Journal of Artificial Science and Social Simulation, 19(2). doi.org/10.18564/jasss.3039
  • Rubio, M. T., & Tulang, A. B. (2016). Socio-Environmental Learning Model. Asia Pacific Journal of Social and Behavioral Sciences, 13. [PDF]
  • Sanchez-Segura, M.I., Dugarte-Peña, G.L., Medina-Dominguez, F., Ruiz-Robles, A. (2016). A model of biomimetic process assets to simulate their impact on strategic goals. Information Systems Frontiers (pp 1-18).[HTML]
  • Santos, J.L., Sampaio, R.R. (2016). Informal social networks and knowledge diffusion: a modeling proposal applied to a software development environment. Perspectivas em Ciência da Informação, 1981-5344.[PDF]
  • Scott, N., Livingston, M., Hart, A., Wilson, J., Moore, D., & Dietze, P. (2016). SimDrink: An Agent-Based NetLogo Model of Young, Heavy Drinkers for Conducting Alcohol Policy Experiments. Journal of Artificial Societies and Social Simulation, 19(1).[HTML]
  • Sengupta, P., & Wilensky, U. (2016). Understanding Electric Current Using Agent-Based Models: Connecting the Micro-level with Flow Rate. In: Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016).
  • Shamsuddin, A Z M, Ahsan, T. and Momen, S. (2016). Trophallaxis and energy optimization in swarms of robots. 19th International Conference on Computer and Information Technology (ICCIT 2016, IEEE). [HTML]
  • Sheppard, C. J. R., A. Harris, and A. R. Gopal. (2016a). Cost-effective siting of electric vehicle charging infrastructure with agent-based modeling. IEEE Transactions on Transportation Electrification, 2(2), 174-189.
  • Sheppard, C. J., Gopal, A. R., Harris, A., & Jacobson, A. (2016b). Cost-effective electric vehicle charging infrastructure siting for Delhi. Environmental Research Letters, 11(6), 64010-64021.
  • Song, S. X., Liang, X. Y., Mei, Y. J., Wen, X., Wang, Y. N., & Mao, N. Z. (2016). Modeling and simulating land abandonment behavior of farmer households based on the CBDI. J. Nat. Resour, 31, 1926-1937.
  • Song, Y. M., & Kim, S. A. (2016). A Study on the Application of the Complex Systems Theory to Understand Urban Phenomena-Focusing on Citizen's Awareness Change through Information Transmission. Journal of the Architectural Institute of Korea Planning & Design, 32(1), 51-58.
  • Štekerová, K., Danielisová, A. (2016). Economic Sustainability in Relation to Demographic Decline of Celtic Agglomerations in Central Europe: Multiple-Scenario Approach. Computational Social Sciences (pp 335-357). [PDF]
  • Suwarno, A., van Noordwijk, M., Weikard, H-P, & Suyamto, D. (2016). Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES). Mitigation and Adaptation Strategies for Global Change. doi:10.1007/s11027-016-9721-0. [HTML]
  • ten Broeke, G., van Voorn, G., & Ligtenberg, A. (2016). Which sensitivity analysis method should I use for my agent-based model? Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.2857
  • Thomas, S. A., Lloyd, D. J., & Skeldon, A. C. (2016). Equation-free analysis of agent-based models and systematic parameter determination: A NetLogo Implementation. [ HTML
  • Thompson, J., Savino, G., & Stevenson, M. (2016). A model of behavioural adaptation as a contributor to the safety-in-numbers effect for cyclists. Transportation Research Part A: Policy and Practice, 85, 65-75. https://doi.org/10.1016/j.tra.2015.12.004
  • Thuy An Vo, T., van der Waerden, P. J. H. J., & Wets, G. (2016). Micro-simulation of car drivers’ movements at parking lots. Procedia Engineering, 142, 100-107. [HTML]
  • Troitzsch, K. G. (2016). Extortion Rackets: An Event-Oriented Model of Interventions. In Social Dimensions of Organised Crime (pp. 117-131). Springer International Publishing.[PDF]
  • Übler, H. & Hartmann, S. (2016). Simulating trends in artificial influence networks. Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.2978
  • Vanloo, G., Ng, C., Osborne, K., Wong, K., Ramsey, B., Wang, D., Bao, Z. (2016). Using High Performance Computing to Model Cellular Embryogenesis. Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale Article No. 12 [PDF]
  • Vo, T. T. A., van der Waerden, P., & Wets, G. (2016). Micro-simulation of car drivers’ movements at parking lots. Procedia Engineering, 142, 100-107.
  • Wagner N., Sahin C., Hanson D., Pena J., Vuksani E., and Tello B. (2016). Quantitative Analysis of the Mission Impact for Host-Level Cyber Defensive Mitigations. to appear in Proceedings of the 2016 ACM Spring Simulation Multi-Conference - Annual Simulation Symposium, April, 2016.
  • WaiShiang, C., YeeWai, S. Nizam, S. (2016). Agent Oriented Requirement Engineering for Lake. Mathematical Modelling: Preliminary Study. Journal of Telecommunication Electronic and Computer Engineering, 8(2), (pp 5-10). [PDF]
  • Wang, S., & Hu, K. (2016, July). Towards dynamic epistemic learning of actions in autonomic multi-agent systems. In 2016 IEEE International Conference on Autonomic Computing (ICAC) (pp. 237-238). IEEE.
  • Wang, Y., Chen, H., Song, S., MEI, Y., Wen, X. (2016). Simulation of households' planting behavior based on a CR-BDI model: Case study of Jiangxingzhuang Village of Mizhi County in Shaanxi Province. Department of Urban and Resource Sciences, Northwest University, Xi'an 710127, China. [ PDF]
  • Warnke, T., Reinhardt, O., & Uhrmacher, A.M. (2016). Population-Based CTMCS and Agent-Based Models. In Proceedings of the 2016 Winter Simulation Conference. Washington, D.C. [ PDF]
  • Weintrop, D., Hjorth, A., & Wilensky, U. (2016). NetLogo Web: Bringing Turtles to the Cloud. Workshop at Constructionism 2016. Bangkok, Thailand.
  • White, D. G., & Levin, J. A. (2016). Navigating the turbulent waters of school reform guided by complexity theory. Complicity: An International Journal of Complexity and Education, 13(1). [HTML]
  • Will, T. E. (2016) A Flocking Model of Human Organizing: Leadership Implications. Presented at the Annual Meeting of the Academy of Management, Anaheim, CA.
  • Will, T. E. (2016) Flock Leadership: Building Collective Capacity by Managing Group Norms. Presented at the International Leadership Association’s Annual Global Conference, Atlanta, GA.
  • Will, T. E. (2016). Flock Leadership: Understanding and Influencing Emergent Collective Behavior. The Leadership Quarterly, 27: 261-279.[PDF]
  • Wirth, E., Szabó, Gy., and Czinkóczky, A. (2016). MEASURE OF LANDSCAPE HETEROGENEITY BY AGENT-BASED METHODOLOGY. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 145-151, doi:10.5194/isprs-annals-III-8-145-2016, 2016. [PDF]
  • Wurzer, G., Pont, U., Lorenz, W.E., Mahdavi, A. (2016). COUPLING BUILDING MORPHOLOGY OPTIMIZATION AND ENERGY EFFICIENCY – A PROOF OF CONCEPT. Department of Building Physics and Building Ecology, TU Wien, Wien, Austria [ PDF]
  • Xiaohong, S. H. A. N., Panpan, J. I. A., & Xiaoyan, L. I. U. (2016). WeChat Information Dissemination Mechanism and Simulation Research Based on Field Theory. Journal of System Simulation, 28(11), 2867.
  • Zellner, M., Massey, D., Minor, E., & Gonzalez-Meler, M. (2016). Exploring the effects of green infrastructure placement on neighborhood-level flooding via spatially explicit simulations. Computers, Environment and Urban Systems, 59, 116-128.
  • Zhang, R., & Yang, C. (2016). The Simulation Research of Potential Loop in Redistribution of Multipoint Two-way Routing Protocol Based on the Butterfly Effect. Proceedings of the 3rd International Conference on Engineering Technology and Application. Thailand. [ PDF]
  • ZHANG, Z. (2016). A Simulation Study on Cooperation Behavior Using NetLogo Software Considering Resource Re-Allocation. Canadian Social Science, 12(4), 20-26. [HTML]
  • Zhu, C., & Yangzhou, C., Guiping, D. (2016). Controller-Based Management of Connected Vehicles in the Urban Expressway Merging Zone Information Technology and Intelligent Transportation Systems (pp 65-75).[ HTML]

2015

  • Wurzer, G., Kowarik, K., & Reschreiter, H. (Eds.). (2015). Agent-based modeling and simulation in archaeology. New York: Springer International Publishing.
  • Yongchen, G., Yang, S., & Jing, M. (2015). A Study on the Multi-Agent Simulation of BBS Public Opinion Evolution Based on the Theory of Opinion Leaders. Journal of Intelligence, 2, 003.[HTML]
  • Abayneh Abebe, Y., Vojinovic, Z., Nikolic, I., Hammond, M., Sanchez, A., & Pelling, M. (2015, April). Holistic flood risk assessment using agent-based modelling: the case of Sint Maarten Island. In EGU General Assembly Conference Abstracts (p. 11584).
  • Agolli, E., & Hakrama, I. (2015). The effective usage of Durres Seaport Container Terminal capacity using an Agent-based modeling approach. Information Systems and Technology Innovations: inducting Modern Business Solution.
  • Al-Sakran, H. O. (2015). Intelligent traffic information system based on integration of Internet of Things and Agent technology. International Journal of Advanced Computer Science and Applications (IJACSA), 6(2), 37-43.
  • Alharbi, H., & Hussain, A. (2015, March). An Agent-Based Approach for Modelling Peer to Peer Networks. In 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim) (pp. 532-537). IEEE.
  • Almagooshi, S. (2015). Simulation modelling in healthcare: Challenges and trends. Procedia Manufacturing, 3, 301-307.
  • Almarza Díaz, David. (2015). Evaluación del programa de simulación NetLogo como herramienta motivadora y eficaz para trabajar destrezas científicas. [Trabajo Fin de Máster].
  • Altawee, M. (2015). Book Review: Agent-based computational economics Using NetLogo.
  • Amblard, F., Daudé, E., Gaudou, B., Grignard, A., Hutzler, G., Lang, C., ... & Taillandier, P. (2015). Introduction à NetLogo. Simulation spatiale à base d'agents avec NetLogo, partie 1, 73-112. [HTML]
  • Angus, S. D. & Hassani-Mahmooei, B. (2015). "Anarchy" reigns: A quantitative analysis of agent-based modelling publication practices in JASSS, 2001-2012. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2952
  • Ayaragarnchanakul, E. (2015). An agent-based model of polycentric city formation: application to the Bangkok Metropolitan Region (Doctoral dissertation, Faculty of Economics, Thammasat University).
  • Badham, J. (2015). Review of An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NETLogo.
  • Balaraman, V., Athle, D., & Singh, M. (2015).Do Daily Routines Affect Convenience Store Footfalls? - Some Experiments with Agent Based Simulation. To appear in SummerSim 15.
  • Balestrini-Robinson, S., Horne, G., Ng, K., Huopio, S., & Schubert, J. Team 1: Cyber Defence in Support of NATO. Scythe, 2.
  • Banitz, T., Gras, A., & Ginovart, M. (2015).Individual-based modeling of soil organic matter in NetLogo: Transparent, user-friendly, and open. Environmental Modeling & Software 71, 39-45. [HTML]
  • Banos, A., Lang, C., & Marilleau, N. (2015). Agent-Based Spatial Simulation with NetLogo (Vol. 1). Elsevier. [HTML]
  • Barnett, R. (2015). A conversation with: Rod Barnett. Kerb: Journal of Landscape Architecture, (23), 30-33.
  • Basu, S., Sengupta, P., & Biswas, G. (2015).A scaffolding framework to support learning of emergent phenomena using multi-agent based simulation environments. Research in Science Education, 45(2), 293-324.
  • Baudry, D., Mustafee, N., Louis, A., Smart, P. A., Godsiff, P., & Mazari, B. (2015). Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system. Journal of Intelligent Manufacturing, 1-17. [ PDF]
  • Bennet, N. (2015). NetLogo Tutorial Series: Set Theory Concepts and Applications. licensed under a Creative Commons Atribution NonCommercial-ShareAlike 4.0 International License.
  • Bergant, D., Bergant, M. D., RNetLogo, I., & LazyData, T. R. U. E. (2015). Package ‘nlexperiment’. [PDF]
  • Berland, M., & Wilensky, U. (2015).Comparing Virtual and Physical Robotics Environments for Teaching Complex Systems and Computational Literacies. Journal of Science Education and Technology.
  • Bhatia, A., Sharma, C., & Goyal, R. (2015). Development of an agent based model illustrating the usage of deferred acceptance algorithm in the admission process (No. e825v1). PeerJ PrePrints.
  • Bhatia, A., Singh, A., & Goyal, R. (2015).A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems Computers 4, 2-23. [HTML]
  • Bianchi, F., & Squazzoni, F. (2015). Agent-based models in sociology. Wiley Interdisciplinary Reviews: Computational Statistics, 7(4), 284–306. https://doi.org/10.1002/wics.1356
  • Blondin, J., Halpern, C., & Malkus, T. (2015). Predator-prey game to maintain stable fish population for Ecotoxicological studies.
  • Blum, C., Lozano, J. A., Davidson, P.P. (2015).An Artificial Bioindicator System for Network Intrusion Detection. Artificial Life 21(2), 93-118. [HTML]
  • Boero, R. (2015). Behavioral Computational Social Science. WileyBlackwell.
  • Brady, C., Holbert, N., Soylu, F., Novak, M., & Wilensky, U. (2015).Sandboxes for Model-Based Inquiry. Journal of Science Education and Technology, 24(2). 265-286.
  • Brady, C., Weintrop, D., Gracey, K., Anton, G., & Wilensky, U. (2015).The CCL-Parallax Programmable Badge: Learning with Low-Cost, Communicative Wearable Computers. In Proceedings of the 16th Annual Conference on Information Technology Education (pp. 139–144). New York, NY, USA: ACM.
  • Buda, A., & Jarynowski, A. (2015). Agent-based modeling, complex networks and system dynamics–practical aproaches. [HTML]
  • Camus, B., Bourjot, C., & Chevrier, V. (2015). Considering a multi-level model as a society of interacting models: Application to a collective motion example. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2645
  • Cascalho, J., & Mabunda, P. (2015). Agent-Based Modelling for a Resource Management Problem in a Role-Playing Game. In Progress in Artificial Intelligence (pp. 696-701). Springer International Publishing. [HTML]
  • Challenge, N. M. S., Djidjev, C., Djidjev, H., & Djidjev, S. (2015). Spread of Viruses on a Computer Network.
  • Chandra Putra, H., Zhang, H., & Andrews, C. (2015). Modeling real estate market responses to climate change in the coastal zone. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2577
  • Chen, S. H., Chie, B. T., & Zhang, T. (2015). Network-based trust games: An agent-based model. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2767
  • Chhatwal, J., & He, T. (2015). Economic evaluations with agent-based modelling: an introduction. Pharmacoeconomics, 33(5), 423-433.
  • Chunying, Y. A. N. G. (2015). Generating Mechanism Model of and Simulation Analysis on the Group Event on Internet. Sci-Tech Information Development & Economy, 2015, 20.
  • Ciutacu, I., & Micu, L. A. (2015). THE FIRM, PART OF THE ECONOMIC SYSTEM: REASONS FOR EXITING A MARKET-AN AGENT-BASED MODELING APPROACH. Revista Economica, 67.
  • Collins, A. J., Frydenlund, E., Elzie, T., & Robinson, R. M. (2015, April). Agent-based pedestrian evacuation modeling: a one-size fits all approach?. In Proceedings of the Symposium on Agent-Directed Simulation (pp. 9-17). Society for Computer Simulation International. [HTML]
  • Collins, A., Petty, M., Vernon-Bido, D., & Sherfey, S. (2015). A call to arms: Standards for agent-based modeling and simulation. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2838
  • Constantine, M. (2015). A Model of Speciation on a Newly Formed Insular Environment Using a Netlogo Model. 대한지리학회 학술대회논문집, 227-227.
  • Corson, N., & Olivier, D. (2015). Dynamical Systems with NetLogo. In Agent-Based Spatial Simulation with NetLogo (pp. 183-221). Elsevier.
  • Cowie, G., Hurd, M., Sevostianov, V., Cundiff, M. E., & Guerin, M. S. (2015). PAVL: Personal Assistance for the Visually Limited. [HTML]
  • D’Alessandro, S., Johnson, L., Gray, D., & Carter, L. (2015). Consumer satisfaction versus churn in the case of upgrades of 3G to 4G cell networks. Marketing Letters, 26(4), 489-500.HTML]
  • Das, U., Dzikowski, J., & Hood, C. S. (2015, September). Evaluating Cognitive Radio Networks an agent based modeling approach. In 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) (pp. 305-308). IEEE.
  • del Sol, M., Hill, M., James, K., Ward, R., & Prescott, P. (2015). Using a Concentrated Heat System to Shock the P53 Protein to Direct Cancer Cells into Apoptosis. [HTML]
  • Dickerson, M. (2015). Agent-based modeling and NetLogo in the introductory computer science curriculum: tutorial presentation. Journal of Computing Sciences in Colleges, 30(5), 174-177. [HTML]
  • Dubrow, A. (2015).Back to school with new cyberlearning tools. National Science Foundation (NSF). [HTML]
  • Dykstra, P., Jager, W., Elsenbroich, C., Verbrugge, R., & de Lavaletter, G. R. (2015). An agent-based dialogical model with fuzzy attitudes. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2813
  • Dzikowski, J., & Hood, C. (2015). Modeling cognitive radio networks in NetLogo. In Proceedings of the Conference on Summer Computer Simulation (pp. 1-11). Society for Computer Simulation International. [HTML]
  • Fachada, N., Lopes, V.V., Martins, R.C., & Rosa, A.C. (2015).Towards a standard model for research in agent-based modeling and simulation. Warburg's lens: A mathematical oncology pre-print discussion forum. [HTML]
  • Faroqi, H., & Mesgari, M. S. (2015). Agent-based crowd simulation considering emotion contagion for emergency evacuation problem. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), 193.
  • Ferrando, P., & Fortiana, J. 33. FINANCIAL TIME SERIES OBTAINED BY AGENT-BASED SIMULATION. In Current Topics on Risk Analysis: ICRA6 and RISK 2015 Conference (p. 281).
  • Fitzpatrick, B., Martinez, J., Polidan, E., & Angelis, E. (2015). The big impact of small groups on college drinking. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2760
  • Flores, D. L., & Gómez, C. M. (2015). Computational modeling of the MAPK pathway using NetLogo. LATIN AMERICAN JOURNAL OF APPLIED ENGINEERING, 1(1).[HTML]
  • Fonseca, F., Ramos, R. A. R., & da Silva, A. N. R. (2015). An agent-based model to assess the attractiveness of industrial estates. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2893
  • Furtado, B. A., Sakowski, P. A. M., & Tovolli, M. H. (2015). Modeling Complex Systems for Public Policies. Institute for Applied Economic Research.
  • Gammack, D. (2015). Using NetLogo as a tool to encourage scientific thinking across disciplines. Journal of Teaching and Learning with Technology, 4(1), 22-39.
  • Gilligan, J.M., Brady, C., Camp, J.V., Nay, J.J., & Sengupta, P. (2015). Participatory simulations of urban flooding for learning and decision support. Proceedings of the 2015 Winter Simulation Conference, 3174-3175, IEEE. 10.1109/WSC.2015.7408456
  • Gkiolmas, A., Chalkidis, A., Papaconstantinou, M., Iqbal, Z., & Skordoulis, C. (2015). An alternative use of the NetLogo modeling environment, where the student thinks and acts like an Agent, in order to teach concepts of Ecology. arXiv preprint arXiv:1501.05779.[HTML]
  • Goel, A. K., & Joyner, D. A. (2015). Impact of a Creativity Support Tool on Student Learning about Scientific Discovery Processes. In Proceedings of the Sixth International Conference on Computational Creativity June (p. 284).[HTML]
  • Golling, M., Koch, R., Hillmann, P., Eiseler, V., Stiemert, L., & Rekker, A. (2015, November). On the evaluation of military simulations: towards a taxonomy of assessment criteria. In 2015 Military Communications and Information Systems Conference (MilCIS) (pp. 1-7). IEEE.
  • Gong, T., Pei, L., Gao, S., Han, F., Zhao, S., & Cai, Z. (2015, July). Visual netlogo-based simulation of anti-SARS immune system and low-to-high resolution reconstruction of sequence medical ct images anti-sars CT. In 2015 International Workshop on Artificial Immune Systems (AIS) (pp. 1-8). IEEE.
  • Gordo, E., Khalaf, N., Strangeowl, T., Dolino, R., & Bennett, N. (2015). FACTORS AFFECTING SOLAR POWER PRODUCTION EFFICIENCY. [HTML]
  • Greasley, A., & Owen, C. (2015, July). Implementing an Agent-based Model with a Spatial Visual Display in Discrete-event Simulation Software. In SIMULTECH (pp. 125-129).
  • Grgurina, N., Barendsen, E., van Veen, K., Suhre, C., & Zwaneveld, B. (2015, November). Exploring Students' Computational Thinking Skills in Modeling and Simulation Projects: a Pilot Study. In Proceedings of the Workshop in Primary and Secondary Computing Education on ZZZ (pp. 65-68). ACM. [HTML]
  • Grow, A., Flache, A., & Wittek, R. (2015). An agent-based model of status construction in task focused groups. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2740
  • Gu, X., Blackmore, K., Cornforth, D., & Nesbitt, K. (2015). Modelling academics as agents: An implementation of an agent-based strategic publication model. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2725
  • Han, Z., Zhang, K., Yin, H., & Zhu, Y. (2015, May). An urban traffic simulation system based on multi-agent modeling. In Control and Decision Conference (CCDC), 2015 27th Chinese (pp. 6378-6383). IEEE.[HTML]
  • Haydari, S. & Smead, R. (2015). Does longer copyright protection help or hurt scientific knowledge creation? Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2720
  • Head, B., Hjorth, A., Brady, C., & Wilensky, U. (2015, December). Evolving agent cognition with netlogo levelspace. In L.Yilmaz, W.K.V. Chan, I. Moon, T.M.K. Roeder, C. Macal, & M.D. Rossetti (Eds.). Proceedings of the 2015 Winter Simulation Conference (pp. 3122-3123). IEEE Press.[HTML]
  • Hjorth, A., Brady, C., Head, B. and Wilensky, U. (2015).LevelSpaceGUI - Scaffolding Novice Modelers’ Inter-Model Explorations. In proceedings for Interaction Design & Children 2015. Boston, MA.
  • Hjorth, A., Brady, C., Head, B., Wilensky, U. (2015).Thinking Within and Between Levels: Exploring Reasoning with Multi-Level Linked Models. In T. Koschmann, P. Häkkinen, & P. Tchounikine (Eds.), "Exploring the material conditions of learning: opportunities and challenges for CSCL," the Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference Gothenburg, Sweden: ISLS.
  • Hmelo‐Silver, C. E., Liu, L., Gray, S., & Jordan, R. (2015). Using representational tools to learn about complex systems: A tale of two classrooms. Journal of Research in Science Teaching, 52(1), 6-35.[HTML]
  • Holbert, N., Brady, C., Soylu, F., Novak, M., & Wilensky, U. (2015).The Model Gallery: Supporting Idea Diffusion in Computational Modeling Activities. Poster presented at the AERA Annual Meeting, Chicago, IL: April, 2015.
  • Huang, L., Wang, S., Hsu, C. H., Zhang, J., & Yang, F. (2015). Using reputation measurement to defend mobile social networks against malicious feedback ratings. The Journal of Supercomputing, 71(6), 2190-2203.
  • Izquierdo, L. R., Olaru, D., Izquierdo, S. S., Purchase, S., & Soutar, G. N. (2015). Fuzzy Logic for Social Simulation Using NetLogo. Journal of Artificial Societies and Social Simulation, 18 (4) 1. [ HTML]
  • Jacobson, M. J., Kim, B., Pathak, S., & Zhang, B. (2015). To guide or not to guide: issues in the sequencing of pedagogical structure in computational model-based learning. Interactive Learning Environments, 23(6), 715-730.
  • Jeewan, A., & Hussain, R. (2015). Minimal Energy Consumption by WSN Nodes during Communication using LEACH and NetLogo in Intelligent Greenhouse. International Journal of Advanced Research in Computer Science 6.6 [HTML]
  • Jerry, K., Yujun, K., Kwasi, O., Enzhan, Z., & Parfait, T. (2015). NetLogo implementation of an ant colony optimisation solution to the traffic problem. Intelligent Transport Systems, IET, 9(9), 862-869. [HTML]
  • Jiang, G., Liu, X., & Wang, Y. (2015). An Agent-based Simulation System for Evolution of Interplay between E-Commerce Vendor and Consumers. International Journal of Hybrid Information Technology, 8(4), 81-88. [HTML]
  • Jiang, G., Wang, Y., & Zhang, N. (2015). Evolution of the Interplay Between E-Commerce Vendor and Consumers. In LISS 2014 (pp. 1243-1247). Springer Berlin Heidelberg.[HTML]
  • Jimenez-Romero, C., Sousa-Rodrigues, D., & Johnson, J. H. (2015). Designing behaviour in bio-inspired robots using associative topologies of spiking-neural-networks. arXiv preprint arXiv:1509.07035.
  • Jimenez-Romero, C., Sousa-Rodrigues, D., & Johnson, J.H. (2015). A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones. Proceedings of the UK Workshop on Computational Intelligence (UKCI 2015) Conference, at Exeter. [HTML]
  • JIN, Z., WU, Y., & YUE, D. (2015). Simulation for distributed load control based on Netlogo and Matlab. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 06.
  • Jun, Y. C. (2015). A qualitative case study of computer programming and unfolding creative processes: focusing on NetLogo-based computational thinking. The Journal of Korean Association of Computer Education, 18(3), 1-14.
  • Just, W., Callender, H. L., & Lamar, D. M. (2015, October). Exploring transmission of infectious diseases on networks with NetLogo. In International Symposium on Biomathematics.
  • Kahl, C. H. & Hansen, H. (2015). Simulating creativity from a systems perspective: CRESY. Journal of Artificial Societies and Social Simulation, 18(1). doi.org/10.18564/jasss.2640
  • Kahn, K. (2015). An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. Physics today, 68(8), 55.[HTML]
  • Kim, J. (2015). An Agent Based Modeling Approach to Exploring Technology Adoption Using MATLAB. Center for Complexity Education, 10.
  • Knoth, L., Arif, A., & Schmitt, J. (2015). Salzburg Residential Mobility Model. GI_Forum,, 2015, 126-135.
  • Kout, A., Labed, S., & Chikhi, S. (2015). Netlogo, agent-based tool for modeling and simulation of routing problem in ad-hoc networks. Proceedings of the Second International Conference on Advances in Information Processing and Communication Technology.
  • Koutiva, I., & Makropoulos, C. (2015, April). Integrating the simulation of domestic water demand behaviour to an urban water model using agent based modelling. In EGU General Assembly Conference Abstracts (p. 6937).
  • Kravari, K. & Bassiliades, N. (2015). A survey of agent platforms. Journal of Artificial Societies and Social Simulation, 18(1). doi.org/10.18564/jasss.2661
  • Krejci, C. & Beamon, B. (2015). Impacts of farmer coordination decisions on food supply chain structure. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2727
  • Landriscina, F. (2015). Simulation and Learning. Springer-Verlag.
  • Le Page, C., Abrami, G., Becu, N., Bommel, P., Bonté, B., Bousquet, F., Gaudou, B., Müller, J.P. and Taillandier, P., 2015, September. Multi-platform training sessions to teach agent-based simulation. In Conference on Complex Systems, Tempe, Etats-Unis (Vol. 28).
  • Le, Q. B. (2015). Manual for Computer Lab Practice on Multi-Agent System (MAS) for Simulating Coupled Community-Landscape System Dynamics.
  • Lee, J. S., Filatova, T., Ligmann-Zielinska, A., Hassani-Mahmooei, B., Stonedahl, F., Lorscheid, I., Voinov, A., Polhill, G., Sun, Z., & Parker, D. C. (2015). The complexities of agent-based modeling output analysis. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2897
  • Lemos, C., Lopes, R. J., & Coelho, H. (2015, November). Analysis of the decision rule in Epstein's Agent-Based model of civil violence. In 2015 Third World Conference on Complex Systems (WCCS) (pp. 1-6). IEEE.
  • LI, R., GAO, H. W., SONG, L., SUN, L. P., & WANG, L. (2015). Coordination Game with Non-exclusive Convention on the Ring Network and the Study. Journal of Qingdao University (Natural Science Edition), 1, 004. [HTML]
  • Liu, H., Silva, E. A., & Wang, Q. (2015). Simulating the Dynamics of Creative Industries’ Interactions with Urban Land Use by Agent-Based Modelling. In Creative Industries and Urban Spatial Structure (pp. 101-133). Springer, Cham.
  • Liu, Z., Cabrera, E., Taboada, M., Epelde, F., Rexachs, D., & Luque, E. (2015). Quantitative evaluation of decision effects in the management of emergency department problems. Procedia Computer Science, 51, 433-442.
  • Lovelace, R., Birkin, M., Ballas, D., & van Leeuwen, E. (2015). Evaluating the performance of iterative proportional fitting for spatial microsimulation: New tests for an established technique. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2768
  • Lungeanu, A., Sullivan, S., Wilensky, U., & Contractor, N.S. (2015). A computational model of team assembly in emerging scientific fields. In L. Yilmaz, W.K.V. Chan, I. Moon, T.M.K. Roeder, C. Macal, & M.D. Rossetti (Eds.). Proceedings of the 2015 Winter Simulation Conference.
  • MA, Y., & LI, Y. (2015). Conceptual Research on Decision Making Meetings for Urban Water Management. International Review for Spatial Planning and Sustainable Development, 3(3), 16-24.[HTML]
  • Malchow, M., van Schaik, L., & Tietjen, B. (2015, April). Modelling the influence of plants on the spatial heterogeneity of soil water. In EGU General Assembly Conference Abstracts (p. 13583).
  • Malik, A., Crooks, A., Root, H., & Swartz, M. (2015). Exploring creativity and urban development with agent-based modeling. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2722
  • Malone, K., Schuchardt, A., & Schunn, C. (2015). Scalable approaches to modeling and engineering in high school biology. In National Association of Research in Science Teaching.
  • Manzanarez-Ozuna, E., Flores, D., Gómez-Gutiérrez, C. M., Abaroa, A., Castro, C., & Castañeda-Martínez, R. (2015). Computational modeling of the MAPK pathway using NetLogo. Lat. Am. J. Appl. Eng, 1, 11-17.
  • Manzo, G., & Baldassarri, D. (2015).Heuristics, Interactions, and Status Hierarchies An Agent-based Model of Deference Exchange. Sociological Methods Research 44(2), 329-387. [HTML]
  • Marsden, T., Blakely, R. I., Gegear, R. J., & Ryder, E. F. (2015). From individuals to populations: Using an agent-based modelling approach to understand mechanisms of pollinator decline. Swarmfest 2015: Agent-based Simulation and the Study of Complexity, University of South Carolina, Columbia, SC, United States.
  • Mayfield, J., & Mayfield, M. (2015). The diffusion process of strategic motivating language. In Academy of Management Proceedings (Vol. 2015, No. 1, p. 13723). Briarcliff Manor, NY 10510: Academy of Management.
  • Mayrhofer, C. (2015). Performance, Scale & Time in Agent-based Traffic Modelling with NetLogo. GI_Forum, 2015, 567-570.[HTML]
  • McPhee-Knowles, S. (2015). Growing food safety from the bottom up: An agent-based model of food safety inspections. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2717
  • Mezencev, K. N. (2015). Multi-agent simulation in netlogo software. Autom. Control Tech. Syst, 1, 10-20.
  • Micu, L. A., & Ciutacu, I. (2015). EU Vs. China: Is Agriculture the Way towards Sustainability? Case Study Using Agent-based Models. Procedia Economics and Finance, 27, 607-611. [HTML]
  • Mitchell, D., Keller, J. M., & Castillo, V. M. (2015, June). Saving Rivertown: Using Computer Simulations in an Earth Science Engineering Design Project for Pre-service Teachers. In 2015 ASEE Annual Conference & Exposition (pp. 26-1355).
  • Miyoshi, K., Jibiki, M., & Murase, T. (2015, July). Self-Organization of Shortest Spanning Tree and Optimal Sink Node Position for Various Shapes of Large-Scale Wireless Sensor Networks. In 2015 IEEE 39th Annual Computer Software and Applications Conference (Vol. 2, pp. 647-652). IEEE.
  • Momen, S. and Tabassum, K.T. (2015). Group Performance in a Swarm of Simulated Mobile Robots, ULAB Journal of Science and Engineering, vol 6, no. 1, pp: 25 - 31, ISSN: 2079-4398 (print), ISSN: 2414-102X (online)[HTML]
  • Moon, S., & Han, Y. (2015). An Agent Based Model for the Study on Undergraduate’s Choice of Seat and Seat Distribution.[PDF]
  • Munteanu, A. C. (2015). Knowledge Spillovers of FDI. Procedia Economics and Finance, 32, 1093-1099. [HTML]
  • Mustafee, N., Sahnoun, M. H., Smart, A., & Godsiff, P. (2015, June). An application of distributed simulation for hybrid modeling of offshore wind farms. In Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (pp. 171-172).
  • Nolting, B.C., Hinkelman, T.M., Brassil, C.E., & Tenhumberg, B. (2015).Composite random search strategies based on non-directional sensory cues. Ecological Complexity 22, 126-138. [HTML]
  • Olsen, J., & Jørgensen, T. R. (2015). Revisiting the cost-effectiveness of universal HPV-vaccination in Denmark accounting for all potentially vaccine preventable HPV-related diseases in males and females. Cost Effectiveness and Resource Allocation, 13(1), 1-10.
  • Olšvičová, K., Procházka, J., & Danielisová, A. (2015). Reconstruction of Prehistoric Settlement Network Using Agent-Based Model in NetLogo. In Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability-The PAAMS Collection (pp. 165-175). Springer International Publishing.[HTML]
  • Patarakin, E., Burov, V., & Parfenov, R. (2015, November). Identifying sets of key players and cliques in socio-educational co-creative projects. In Proceedings of the 2015 2nd International Conference on Electronic Governance and Open Society: Challenges in Eurasia (pp. 232-236).
  • Pereda, M., Poza, D., Santos, J. I., & Galán, J. M. (2015). Quality Uncertainty and Market Failure: An Interactive Model to Conduct Classroom Experiments. In International Joint Conference (pp. 549-557). Springer International Publishing.[HTML]
  • Polhill, G. (2015). Extracting OWL Ontologies from Agent-Based Models: A NetLogo Extension. Journal of Artificial Societies and Social Simulation, 18(2), 15. [HTML]
  • Pope, A. J., & Gimblett, R. (2015). Linking Bayesian and agent-based models to simulate complex social-ecological systems in semi-arid regions. Frontiers in Environmental Science, 3, 55.[HTML]
  • Priest B., Vuksani E., Wagner N., Tello B., Carter K., and Streilein W.(2015). Agent-Based Simulation in Support of Moving Target Cyber Defense Technology Development and Evaluation. in Proceedings of the 2015 ACM Spring Simulation Multi-Conference - Communications and Networking Simulation Symposium, Alexandria, VA, April, 2015. [HTML]
  • Procházka, J., & Olševičová, K. (2015). Monitoring Lane Formation of Pedestrians: Emergence and Entropy. In Intelligent Information and Database Systems (pp. 221-228). Springer International Publishing. [HTML]
  • Procházka, J., Cimler, R., & Olševičová, K. (2015). Pedestrian Modelling in NetLogo. In Emergent Trends in Robotics and Intelligent Systems (pp. 303-312). Springer International Publishing. [HTML]
  • Ramli, N. R., Razali, S., & Osman, M. (2015, August). An overview of simulation software for non-experts to perform multi-robot experiments. In Agents, Multi-Agent Systems and Robotics (ISAMSR), 2015 International Symposium on (pp. 77-82). IEEE. [HTML]
  • Richards, D., & Taylor, M. (2015). A Comparison of learning gains when using a 2D simulation tool versus a 3D virtual world: An experiment to find the right representation involving the Marginal Value Theorem. Computers & Education 86, 157-171. http://dx.doi.org/10.1016/j.compedu.2015.03.009
  • Ronquillo, A. O., Doloriel, D. M., & Doloriel, N. S. (2015). RATE OF NEWCASTLE DISEASE SPREAD AMONG CHICKENS: COMPARATIVE SIMULATION EXPERIMENT. SDSSU MULTIDISCIPLINARY RESEARCH JOURNAL, 3, 110-113.[PDF]
  • Rorabaugh, A. (2015). Modeling pre-European contact Coast Salish seasonal social networks and their impacts on unbiased cultural transmission. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2911
  • Rossiter, S. (2015). Simulation design: Trans-paradigm best-practice from software engineering. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2842
  • Rotenberry, J.T., Swanger, E., & Zuk, M. (2015).Alternative reproductive tactics arising from a continuous behavioral trait: callers versus satellites in field crickets. American Naturalist 185:469-490. DOI: 10.1086/680219. [HTML]
  • Ryder, E. F., Boyd, J. R., Marsden, T. B., La, S., Sullender, M. E., Reider, D., & White, B. T. (2015). Modeling from molecules to moose: Teaching students to develop agent-based simulations in biology. Swarmfest 2015: Agent-based Simulation and the Study of Complexity, University of South Carolina, Columbia, SC, United States.
  • Scherer, S., Wimmer, M., Lotzmann, U., Moss, S., & Pinotti, D. (2015). Evidence based and conceptual model driven approach for agent-based policy modelling. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2834
  • Sengupta, P., Dickes, A., Farris, A. V., Karan, A., Martin, D., & Wright, M. (2015). Programming in K-12 science classrooms. Communications of the ACM, 58(11), 33-35. [PDF]
  • Shook, E., Wren, C., Marean, C. W., Potts, A. J., Franklin, J., Engelbrecht, F., ... & Esler, K. J. (2015, July). Paleoscape model of coastal South Africa during modern human origins: progress in scaling and coupling climate, vegetation, and agent-based models on XSEDE. In Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure (p. 2). ACM. [PDF]
  • Shutters, S. T. & Hales, D. (2015). Altruism displays a harmonic signature in structured societies. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2780
  • Siddique, O. (2015). Steven F. Railsback and Volker Grimm. Agent-Based and Individual-Based Modelling: A Practical Introduction. Pakistan Development Review, 54(1), 76-77. [HTML]
  • Simpson, O., & Camorlinga, S. (2015, April). A methodology to create Complex Adaptive System models that support Cardiovascular Diseases simulation. In 2015 Annual IEEE Systems Conference (SysCon) Proceedings (pp. 224-229). IEEE.
  • Singh, M., & Balaraman, V. (2015).Exploring Norm Establishment and Spread in Different Organizational Structures Using an Extended Axelrod Model. Spring Sim 15, Proceedings of the 2015 Spring Sim Simulation Multiconference
  • Sirer, M. I., Maroulis, S., Guimera, R., Wilensky, U., & Amaral, L. A. N. (2015). The currents beneath the “rising tide” of school choice: An analysis of student enrollment flows in the Chicago public schools. Journal of Policy Analysis and Management, 34(2), 358-377.[PDF]
  • Takács, K. & Squazzoni, F. (2015). High standards enhance inequality in idealized labor markets. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2940
  • TANG, H., XIONG, S., & JIN, Z. (2015). Research for Simulation of Rescue Behaviors of Industrial Accidents Based on Communication of Virtual Human. Industrial Safety and Environmental Protection, 01.
  • Terna, P. (2015). Agent-based Models for Exploring Social Complexity, with an Application of Network Analysis to Agents. In SIMULTECH (pp. IS-11).
  • Thompson, J., Newman, S., & Stevenson, M. (2015). A model for exploring the relationship between payment structures, fatigue, crash risk, and regulatory response in a heavy-vehicle transport system. Transportation Research Part A: Policy and Practice, 82, 204-215. https://doi.org/10.1016/j.tra.2015.09.016
  • Tovar, R. J. C., Fernández, O., & Medina, L. J. C. (2015). Dialéctica entre teorías y ciencias de la complejidad. Un Acercamiento a Través del Análisis Crítico. Entelequia: revista interdisciplinar, (18), 137-142.
  • Trab, S., Bajic, E., Zouinkhi, A., Abdelkrim, M. N., Chekir, H., & Ltaief, R. H. (2015). Product Allocation Planning with Safety Compatibility Constraints in IoT-based Warehouse. Procedia Computer Science, 73, 290-297. [HTML]
  • Troitzsch, K. G. (2015). What One Can Learn from Extracting OWL Ontologies from a NetLogo Model That Was Not Designed for Such an Exercise. Journal of Artificial Societies and Social Simulation, 18(2), 14. [HTML]
  • Uemura, M., Matsushita, H., & Kraetzschmar, G. K. (2015, November). Path Planning with Slime Molds: A Biology-Inspired Approach. In Neural Information Processing (pp. 308-315). Springer International Publishing.[HTML]
  • Varela, C. A. R., Velandia, F. B., Rey, M. A. M., Romero, N. G., & Neira, N. O. (2015). Foraging Multi-Agent System Simulation Based on Attachment Theory. In ISCS 2014: Interdisciplinary Symposium on Complex Systems (pp. 359-364). Springer International Publishing. [HTML]
  • Wang, H., Mostafizi, A., Cramer, L. A., Cox, D., & Park, H. (2015). An agent-based model of a multimodal near-field tsunami evacuation: Decision-making and life safety. Transportation Research Part C: Emerging Technologies.[HTML]
  • Wang, Y. N., & Chen, H. (2015). Scenario Simulation of Land Use Based on Net Logo Model—A Case Study for Matiwa Village of Mizhi County of Shaanxi Province. Journal of Anhui Agricultural Sciences, 25, 111. [HTML]
  • Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2015).Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127–147.
  • Weintrop, D., Head, B., & Wilensky, U. (2015).Plotting Programming Trajectories with the NetLogo Data Explorer. In Proceedings of Information Visualization, 2015. Chicago, IL. IEEE.
  • Weiss, J., & Steel, J. (2015). Continuing with agent based models to aid decision making in weed management. A case study of buffel grass; a weed dispersed by vehicular wind turbulence.
  • Wilensky, U. & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social and engineered complex systems with NetLogo. Cambridge, MA: MIT Press.
  • Wilkerson-Jerde, M. H. & Wilensky, U. (2015).Patterns, probabilities, and people: Making sense of quantitative change in complex systems. Journal of the Learning Sciences, 24(2), 204-251. doi: 10.1080/10508406.2014.976647
  • Wilkerson-Jerde, M. H., Wagh, A. & Wilensky, U. (2015).Balancing curricular and pedagogical needs in computational construction kits: Lessons from the DeltaTick project. Science Education, 99(3), 465-499. [HTML]
  • Wirth, E. (2015). Pi from agent border crossings by NetLogo package. Wolfram Library Archive.
  • Wrasse, K., Hayka, H., & Stark, R. (2015). Simulation of product-service-systems piloting with agent-based models (outlined revision). Procedia CIRP, 30, 108-113.
  • Wright, M. & Sengupta, P. (2015). Modeling oligarchs' campaign donations and ideological preferences with simulated agent-based spatial elections. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2736
  • Zandi, M. (2015). Simulation of Ascorbic Acid Release from Alginate‐Whey Protein Concentrates Microspheres at the Simulated Gastrointestinal Condition Using Netlogo Platform. Journal of Food Process Engineering. [HTML]
  • Zandi, M., & Mohebbi, M. (2015). An agent‐based simulation of a release process for encapsulated flavour using the NetLogo platform. Flavour and Fragrance Journal, 30(3), 224-229. [HTML]

2014

  • Alden, K., Timmis, J., & Coles, M. (2014). Easing Parameter Sensitivity Analysis of Netlogo Simulations using SPARTAN. ALIFE 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems. [HTML]
  • Ampatzidis, G., & Ergazaki, M. (2014). Towards a learning environment for challenging the idea of the balanced nature: Insights from the first cycle of research. In C. P. Constantinou, N. Papadouris & A. Hadjigeorgiou (Eds.), E-Book. Proceedings of the ESERA 2013 Conference: Science Education Research For Evidence-based Teaching and Coherence in Learning. Part 3 (pp. 44-54). Nicosia, Cyprus: European Science Education Research Association. [HTML]
  • Balaraman, V., & Singh, M. (2014). Exploring Norm Establishment in Organizations Using an Extended Axelrod Model with Two New Metanorms. SummerSim '14, Proceedings of the 2014 Summer Simulation Multiconference, Article No. 39.
  • Becher, M. A., Grimm, V., Thorbek, P., Horn, J., Kennedy, P. J., & Osborne, J. L. (2014). BEEHAVE: a systems model of honeybee colony dynamics and foraging to explore multifactorial causes of colony failure. Journal of applied ecology, 51(2), 470-482.[HTML]
  • Biotechnology and Biological Sciences Research Council. (2014). Virtual bees help to unravel complex causes of colony decline. ScienceDaily. [HTML]
  • Brady, C., Holbert, N. Soylu, F., Novak, M., Wilensky, U. (2014). Sandboxes for model-based inquiry. Science Teaching and Learning with Models,” Journal of Science Education and Technology (JOST) [Special Issue].
  • Buttò, M., Pereira, C., & Taylor, M. (2014). Sunshine or shield? Secret voting procedures and legislative accountability. Journal of Artificial Science and Social Simulation, 17(4). doi.org/10.18564/jasss.2620
  • Chiacchio, Ferdinando, Pennisi, Marzio, Russo, Giulia, Motta, Santo, Pappalardo, Francesco, Agent-Based Modeling of the Immune System: NetLogo, a Promising Framework, BioMed Research International, 2014, 907171, 6 pages, 2014.
  • Dickerson, M. (2014). Multi-agent simulation, netlogo, and the recruitment of computer science majors. Journal of Computing Sciences in Colleges, 30(1), 131-139. [HTML]
  • Epstein, J.M. (2014). Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science. New Jersey: Princeton University Press. [HTML]
  • Galic, N., R. Ashauer, H. Baveco, A.-M. Nyman, A. Barsi, P. Thorbek, E. Bruns, & P. J. Van den Brink. (2014). Modeling the contribution of toxicokinetic and toxicodynamic processes to the recovery of Gammarus pulex populations after exposure to pesticides. Environmental Toxicology and Chemistry 33:1476-1488.
  • Gkiolmas A., Papaconstantinou M., Chalkidis A., & Skordoulis C. (2014). "Learning about Populations in Ecosystems by “Building Them From Inside” with NetLogo: A Constructionist Approach for Teaching Population Ecology’s Principles. Proceedings of the Constructionism. Vienna, Austria [PDF]
  • Gooding, T. (2014). Modelling society's evolutionary forces. Journal of Artificial Science and Social Simulation, 17(3). doi.org/10.18564/jasss.2497
  • Guo, Y., & Wilensky, U. (2014). Beesmart: a microworld for swarming behavior and for learning complex systems concepts. Proceedings of the Constructionism 2014 Conference. Vienna, Austria. August 2014. [PDF]
  • Head, B., Liang, C., & Wilensky, U. (2014). Flying like a School of Fish: Discovering Flocking Formations in an Agent-Based Model with Analogical Reasoning. In Proceedings of the Michigan Complexity Mini-Conference. University of Michigan, Ann Arbor, Michigan.
  • Head, B., Orton, K., & Wilensky, U. (2014). An Agent-Based Approach to Modeling Membrane Formation. In Proceedings of the Michigan Complexity Mini-Conference. University of Michigan, Ann Arbor, Michigan.
  • Heijnen, P., Chappin, É. J. L., & Nikolic, I. (2014). Infrastructure network design with a multi-model approach: Comparing geometric graph theory with an agent-based implementation of an ant colony optimization. Journal of Artificial Science and Social Simulation, 17(4). doi.org/10.18564/jasss.2533
  • Hjorth, A. & Wilensky, U. (2014). Re-grow Your City – a NetLogo curriculum unit on Regional Development. In J. L. Polman, E. A. Kyza, D. K. O'Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O'Connor, T. Lee & L. D'Amico (Eds.), Proceedings of "Learning and Becoming in Practice," the 11th International Conference of the Learning Sciences (ICLS) 2014 (Vol. 3, pp. 1553-1555). Boulder, CO: International Society of the Learning Sciences.
  • Hjorth, A., & Wilensky, U. (2014). Redesigning Your City - A Constructionist Environment for Urban Planning Education. Proceedings of Constructionism 2014, Vienna, Aug 19-23.
  • Hjorth, A., & Wilensky, U. (2014). Redesigning Your City – A Constructionist Environment for Urban Planning Education. Informatics in Education-An International Journal, (Vol13_2), 197-208. Chicago
  • Hjorth, A., Wilensky, U., Villamar, J., Brown, H. (2014). Using Agent-Based Modeling to Explore and Visualize the Effects of Prevention Implementation Strategies for Policy. In Computational and Technical Approaches to Improve the Implementation of Prevention Programs. Panel chaired by Dr. Hendricks Brown at 7th Annual Conference on the Science of Dissemination and Implementation. Bethesda, MD.
  • Horn, M., Brady, C., Hjorth, A., Wagh, A., & Wilensky, U. (2014). Frog Pond: A code first learning environment on natural selection and evolution. Proceedings of IDC 2014.
  • Horn, M.S., Weintrop, D., & Routman, E. (2014). Programming in the Pond: A Tabletop Computer Programming Exhibit. In Proceedings of the Extended Abstracts of the 32nd Annual ACM Conference on Human Factors in Computing Systems (pp. 1417-1422). New York, NY, USA: ACM.
  • Iwamura, T., Lambin, F.E., Silvius, M.K., Luzar, B.J & Fragoso, MV.J. (2014). Agent-based modeling of hunting and subsistence agriculture on indigenous lands: Understanding interactions between social and ecological systems. Environmental Modelling and Software 58(2014)109-127 [HTML]
  • Izquierdo, L.R., Izquierdo, S.S. & Vega-Redondo, F. (2014). Leave and let leave: A sufficient condition to explain the evolutionary emergence of cooperation. Journal of Economic Dynamics & Control 46, pp. 91–113. [PDF]
  • Jona, K., Wilensky, U., Trouille, L., Horn, M. S., Orton, K., Weintrop, D., & Beheshti, E. (2014). Embedding Computational Thinking in Science, Technology, Engineering, and Math (CT-STEM). Presented at the 2014 CE21 PI and Community Meeting, Orlando, FL.
  • Kautz, M., Muhammad, A.I., & Schopf, R. (2014). Individual traits as drivers of spatial dispersal and infestation patterns in a host-bark beetle system. Ecological Modelling, 273, 264-276. [HTML]
  • Knoeri, C., Nikolic, I., Althaus, H. J., & Binder, C. R. (2014). Enhancing recycling of construction materials: An agent based model with empirically based decision parameters. Journal of Artificial Science and Social Simulation, 17(3). doi.org/10.18564/jasss.2528
  • Labarbe, E., & Thiel, D. (2014). Information Sharing to Reduce Misperceptions of Interactions Among Complementary Projects: A Multi-Agent Approach. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 9. [HTML] (Jan 2014)
  • Maroulis, S., & Wilensky, U. (2014). Social and Task Interdependencies in the Street-Level Implementation of Innovation. Journal of Public Administration Research and Theory 25(3), 721-750. https://doi-org.turing.library.northwestern.edu/10.1093/jopart/mut084

2013

  • Abdollahian, M., Yang, Z., & Nelson H. (2013). Techno-Social Energy Infrastructure Siting: Sustainable Energy Modeling Programming (SEMPro). Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 6. [HTML] (June 2013)
  • Al-Roomi, M., Salman, A., & Ahmad, I. (2013, November). Analyzing MBSA performance using NetLogo. In 2013 European Modelling Symposium (pp. 67-72). IEEE.
  • Alaliyat, S., Osen, O. L., & Kvile, K. O. (2013). An Agent-Based Model To Simulate Pathogen Transmission Between Aquaculture Sites In The Romsdalsfjord. In ECMS (pp. 46-52).
  • Almudí, I., Fatás-Villafranca, F. & Izquierdo, L.R. (2013).Industry dynamics, technological regimes and the role of demand. Journal of Evolutionary Economics Vol. 23, Issue 5, pp 1073-1098. [PDF]
  • Anghinolfi, D., Capogrosso, A., Paolucci, M., & Perra, F. (2013, October). An agent-based simulator for the evaluation of the measurement of effectiveness in the military naval tasks. In 2013 17th International Conference on System Theory, Control and Computing (ICSTCC) (pp. 733-738). IEEE.
  • Ariuntsetseg, E., & Yom, J. H. (2013). Foot-and-mouth disease spread simulation using agent-based spatial model. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 31(3), 209-219.
  • Aroor, A. (2013). QoS aware distributed service composition using agents (Doctoral dissertation, Dhirubhai Ambani Institute of Information and Communication Technology).
  • Arslan, M.O. & İcan, Ö. (2013). "An Agent-Based Analysis of Tax Compliance for Turkey." Anadolu University Journal of Social Sciences, 13(2), 143-152. [PDF]
  • Arslan, M.O. & İcan, Ö. (2013). "The Effects of Neighborhood on Tax Compliance Rates: Evidence from an Agent-Based Model." Journal of Cukurova University Institute of Social Sciences, 22(1), 337-350. [PDF]
  • Bajracharya, K., & Duboz, R. (2013, April). Comparison of three agent-based platforms on the basis of a simple epidemiological model (WIP). In Proceedings of the Symposium on Theory of Modeling & Simulation-DEVS Integrative M&S Symposium (pp. 1-6). [PDF]
  • Balbi, S., Giupponi, C., Perez, P., & Alberti, M. (2013). A spatial agent-based model for assessing strategies of adaptation to climate and tourism demand changes in an alpine tourism destination. Environmental Modelling & Software, 45, 29-51.
  • Banos, A., Marilleau, N., & MIRO Team. (2013). Improving individual accessibility to the city. In Proceedings of the European Conference on Complex Systems 2012 (pp. 989-992). Springer, Cham.
  • Basu, S., Dickes, A., Kinnebrew, J. S., Sengupta, P., & Biswas, G. (2013, May). CTSiM: A Computational Thinking Environment for Learning Science through Simulation and Modeling. In CSEDU (pp. 369-378).
  • Bezirgiannis, N. (2013). Improving Performance of Simulation Software Using Haskell's Concurrency & Parallelism. Universiteit Utrecht. [HTML] (Sept 2013)
  • Bhattacharya, S., Czejdo, B., Malhotra, R., Perez, N., & Agrawal, R. (2013, July). Agent based modeling of moving point objects in geospatial data. In 2013 Fourth International Conference on Computing for Geospatial Research and Application (pp. 132-133). IEEE.
  • Bhattacharya, S., Czejdo, B., Malhotra, R., Perez, N., & Agrawal, R. (2013, July). Characterization of Moving Point Objects in Geospatial Data. In 2013 Fourth International Conference on Computing for Geospatial Research and Application (pp. 151-151). IEEE.
  • Bichraoui, N., Guillaume, B., & Halog, A. (2013). Agent-based modelling simulation for the development of an industrial symbiosis-preliminary results. Procedia Environmental Sciences, 17, 195-204.
  • Biggs, M. B., & Papin, J. A. (2013). Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation. PLoS One, 8(10), e78011.
  • Bintoro, B. P. K. (2013). Model Berbasis Agen untuk Pengenalan Produk Baru dengan Twitter. Business and Management Review, 2(2).
  • Bintoro, B. P. K., Epicentrum, K. K. K. R., & Jie, F. (2013). AGENT-BASED MODELING FOR NEW PRODUCT INTRODUCTION USING TWITTER. In International DSI and ASIA Pacific DSI 2013 Bali Conference (pp. 1-11). Decision Sciences Institute.
  • Biondo, A.E., Pluchino, A., & Rapisarda, A. (2013). Return Migration After Brain Drain: A Simulation Approach. Journal of Artificial Societies and Social Simulation (JASSS), 16(2), 11. [HTML]
  • Boone, Randall B., Moore, John C., Koyama, Akihiro, Holfelder, Kirstin (2013, November 21). "Soil microbe-predator model with enzymes" (Version 1). CoMSES Computational Model Library.
  • Borsboom, D, & Cramer, A. O. (2013).Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121.[HTML]
  • Bravo, R. and D.E. Axelrod. A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments. Theoretical Biology and Medical Modeling 10:66 (2013), DOI: 10.1186/1742-4682-10-66, [HTML]
  • Bredeche, N., Montanier, J. M., Weel, B., & Haasdijk, E. (2013). Roborobo! a fast robot simulator for swarm and collective robotics. arXiv preprint arXiv:1304.2888.
  • Brooks, M. (2013). Facilitating the creation of advanced agents within netlogo by allowing specification and control using the behaviour oriented design methodology.
  • Buchin, K., Buchin, M., van Kreveld, M., Speckmann, B., & Staals, F. (2013, August). Trajectory grouping structure. In Workshop on Algorithms and Data Structures (pp. 219-230). Springer, Berlin, Heidelberg.
  • Burgos Liz, L. M. (2013). The 3 E's public health-engineering, evolution and effectiveness a framework proposal for research and policy design in chronic disease public health.
  • Cabreira, T. M., de Aguiar, M. S., & Dimuro, G. P. (2013, June). An extended evolutionary learning approach for multiple robot path planning in a multi-agent environment. In 2013 IEEE Congress on Evolutionary Computation (pp. 3363-3370). IEEE.
  • Caillou, P., Gil-Quijano, J., & Zhou, X. (2013). Automated observation of multi-agent based simulations: a statistical analysis approach.
  • Carrasco-Jiménez, J. C., Celaya-Padilla, J. M., Montes, G., Brena, R. F., & Iglesias, S. (2013, June). Social interaction discovery: A simulated multiagent approach. In Mexican Conference on Pattern Recognition (pp. 294-303). Springer, Berlin, Heidelberg.
  • Cecconi, F. (2013). Simulating Crime: Models, Methods, Tools. Informatica e diritto, 22(1), 181-191.
  • Čech, P., Tučník, P., Bureš, V., & Husráková, M. (2013, September). Modelling complexity of economic system with multi-agent systems. In 5th International Conference on Knowledge Management and Information Sharing (KMIS 13), Vilamoura, Algarve, Portugal (pp. 464-469).
  • Chang-hong, L., Feng, L., Jian-ping, F., & Zhi-hong, S. (2013). Research on Selections and Influencing Factors of Industry-university-research Cooperation Models in Developing New Products. Sci-tech Innovation and Productivity, 2.
  • Chen, P., Plale, B., & Evans, T. (2013, October). Dependency provenance in agent based modeling. In 2013 IEEE 9th International Conference on e-Science (pp. 180-187). IEEE.
  • Chen, P., Zhu, S. Y., Xu, L. J., Ma, X. F., & Du, Z. G. (2013). Multi-agent simulation of emergency evacuation on the sidewalk. In Applied Mechanics and Materials (Vol. 253, pp. 2005-2008). Trans Tech Publications Ltd.
  • CHI, J. J., LUO, X. M., & SUN, X. N. (2013). Simulation & Evaluation of Complex Electromagnetic Environment Effect on Radar Network" Four Countering" Capabilities. Journal of Academy of Armored Force Engineering, (1), 16.
  • Ciancamerla, E., Minichino, M., & Palmieri, S. (2013, July). Modeling cyber attacks on a critical infrastructure scenario. In IISA 2013 (pp. 1-6). IEEE.
  • Cimler, R. (2013). Analytic Hierarchy Process and agent-based simulation for traffic modeling. In Proceedings of the 12th International Symposium on the Analytic Hierarchy Process, Kuala Lumpur, Malaysia, 23rd-26th June.
  • Cimler, R., & Olševičová, K. (2013). Analysis Simulation of aircraft disembarking methods. Global Journal on Technology, 3.
  • Collard, P., Mesmoudi, S., Ghetiu, T., & Polack, F. (2013).Emergence of Frontiers in Networked Schelling Segregationist Models. Journal Complex Systems, 22(1). [HTML]
  • Collard, P., Verel, S. & Clergue, M. (2013).Systèmes Complexes: une introduction par la pratique [Complex Systems: An Introduction to Practice]. Presses Polytechniques et Universitaires Romandes. Publisher of the EPFL Press, Lausanne [France], 306 p. (ISBN: 978-2-88074-991-0)
  • Comer, K. W., & Loerch, A. G. (2013). The impact of agent activation on population behavior in an agent-based model of civil revolt. Procedia Computer Science, 20, 183-188.
  • Curtin, T., Donnelly, M., Maynard, G., Nguyen, N., Nourie, A., & Thomas, M. (2013). Can the Great Pacific Garbage Patch be Eliminated With Plastic-eating Microbes?.
  • Dai D, Z. Y. (2013). Simulating fire spread in a community using an agent-based model. In Proceedings of the 12th International Conference on GeoComputation. LIESMARS Wuhan University, Wuhan, China (pp. 130-132).
  • Damaceanu, R. C. (2013). Agent-Based Computational Economics Using NetLogo. Bentham Science Publishers. [HTML]
  • Dangles, O. J. (2013). SimAdapt: An individual-based genetic model for simulating landscape management impacts on populations.
  • Dao, N. B., Revel, A., Menard, M., & El Hamidi, A. (2013). Simulation Models for Grassland Ecosystem and Inter-species Plant Competition: Interation in NetLogo.
  • Davies, B., & Floyd, B. Simulating the effects of prehistoric migration events on body dimensions among Oceanic populations.
  • de Senna Carneiro, T. G., de Andrade, P. R., Câmara, G., Monteiro, A. M. V., & Pereira, R. R. (2013). An extensible toolbox for modeling nature–society interactions. Environmental Modelling & Software, 46, 104-117.
  • de Siqueira Braga, D., Alves, F. O. M., Menezes, L. C. D. S., & de Lima Neto, F. B. (2013, September). Tools for Social Simulation-What Is Missing?. In 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (pp. 651-652). IEEE.
  • De Zeeuw, A., Craig, T., & You, H. S. (2013, October). Assessing conceptual understanding in mathematics. In 2013 IEEE Frontiers in Education Conference (FIE) (pp. 1742-1744). IEEE.
  • Decker, W., Cromartie, J., Brandon, D., Warden, R., Ward, M. O., & Ryder, E. F. (2013). Simulation of embryonic development in C. elegans using agent-based modeling, International C. elegans Meeting, University of California, Los Angeles, CA, United States.
  • Delaney, L., Kleczkowski, A., Maharaj, S., Rasmussen, S., & Williams, L. (2013). Reflections on a Virtual Experiment Addressing Human Behavior During Epidemics. Summer Computer Simulation Conference. ACM Digital Library. [HTML]
  • Demarest, J., Pagsuyoin, S., Learmonth, G., Mellor, J., & Dillingham, R. (2013). Development of a Spatial and Temporal Agent-Based Model for Studying Water and Health Relationships: The Case Study of Two Villages in Limpopo, South Africa. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 3. [HTML] (Oct 2013)
  • Dijkstra, J., & Jessurun, A. J. (2013). Modeling planned and unplanned store stops for the scenario based simulation of pedestrian activity in city centers. In Proceedings of the Fifth International Conference on Advances in System Simulation (SIMUL2013) (Vol. 27, pp. 1-22).
  • Dixit, S. K., & Sachan, D. (2013). A Web Service Selection Model using Cognitive Parameters. International Journal of Computer Applications, 73(21).
  • Dong, S. X., Tang, L. A., & Shao, Z. Z. (2013). Research on Evolution Simulation of Technology Innovation Community in High-tech Zone. In Applied Mechanics and Materials (Vol. 411, pp. 2472-2476). Trans Tech Publications Ltd.
  • Drogoul, A., Amouroux, E., Caillou, P., Gaudou, B., Grignard, A., Marilleau, N., ... & Zucker, J. D. (2013, May). Gama: multi-level and complex environment for agent-based models and simulations. In 12th International Conference on Autonomous agents and multi-agent systems (pp. 2-p). Ifaamas.
  • Duarte Olson, I. (2013). "It's like an epidemic, it catches on": Community Knowledge of Everyday Complex Phenomena. In C. Lee and G. Saxe. (Co-chairs). Capitalizing on Knowledge Co-Constructed via the Praxis of Historically Nondominant Groups. Symposium accepted to the American Educational Research Association Conference.
  • Duarte Olson, I. (2013). Cultural Differences Between Favela and Asfalto in Complex Systems Thinking. Journal of Cognition and Culture, 13(1-2), 145-157. doi:10.1163/15685373-12342089 [PDF]
  • Duarte Olson, I. (2013). Inequality in Levels: Navigating Everyday Complexity. In Ojalehto,B., Medin, D. Conceptualizing Complex Systems: How People Navigate Social and Ecological Systems. Symposium accepted to the Society for Anthropological Sciences Meeting.
  • Duckham, M. (2013). Decentralized Spatial Computing: Foundations of Geosensor Networks.Springer. [HTML]
  • Duckham, M. (2013). Simulating Scalable Decentralized Spatial Algorithms. In Decentralized Spatial Computing (pp. 209-244). Springer, Berlin, Heidelberg.
  • Dykstra, P., Elsenbroich, C., Jager W., de Lavalette, G. R., & Verbrugge, R. (2013). Put Your Money Where Your Mouth Is: DIAL, A Dialogical Model for Opinion Dynamics. Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 4. [HTML] (June 2013)
  • Ekmekci, O. (2013). INFLUENCE OF INTERPROFESSIONAL EDUCATION ON REDUCING STEREOTYPING AND INCREASING COLLABORATIVE BEHAVIOR IN HEALTH CARE TEAMS. In INTED2013 Proceedings (pp. 3771-3776). IATED.
  • Emílio Almeida, J., Kokkinogenis, Z., & Rossetti, R. J. (2013). NetLogo implementation of an evacuation scenario. arXiv, arXiv-1303.
  • Ferguson, J. (2013, January). Collecting and Managing Multiple Video Data Sources for Visual Semiotic Analysis of Science Students’ Reasoning with Digital Representations. In Proceedings of the 2013 Contemporary Approaches to Research in Mathematics, Science, Health and Environmental Education Symposium (pp. 1-9). Centre for Research in Educational Futures and Innovation, Deakin University.
  • Fioretti, G. (2013). Romulus-Catalin Damaceanu: Agent-based computational economics using netlogo.
  • Flores-Parra, J. M. (2013). Wíinik: Towards an Agent-Based Simulation Design Tool for Distributed Agency and Cognitive Software Agents. In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AA-MAS 2013).
  • Forbey, J. S. (2013). Individual-based modeling for the masses. Ecology, 94(1), 260-262.
  • Fotuhi, F., Huynh, N., Vidal, J. M., & Xie, Y. (2013). Modeling yard crane operators as reinforcement learning agents. Research in transportation economics, 42(1), 3-12.
  • Frank, B.M. & Baret, P.V. (2013). "Simulating brown trout demogenetics in a river/nursery brook system: The individual-based model DemGenTrout." Ecological Modelling, 248, 184-202. [HTML]
  • Frey, S. & Goldstone, R.L. (2013). Cyclic game dynamics driven by iterated reasoning. PLOS ONE, 8(2): 2-11. [PDF]
  • Furtado, P., Fakhfakh, R., Frayret, J. M., & Biard, P. (2013, October). Simulation of a Physical Internet—Based transportation network. In Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM) (pp. 1-8). IEEE.
  • Galic, N., G. M. Hengeveld, P. J. Van den Brink, A. Schmolke, P. Thorbek, E. Bruns, & H. M. Baveco. (2013). Persistence of Aquatic Insects across Managed Landscapes: Effects of Landscape Permeability on Re-Colonization and Population Recovery. PLoS ONE 8:e54584.
  • Ghorbani, A., Bots, P., Dignum, V. and Dijkema, G. (2013). MAIA: a Framework for Developing Agent-Based Social Simulations. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 9. [HTML]
  • Ghosh, S., Dutta, A., Mascardi, V., & Briola, D. (2013, September). Exploiting MAS-Based Simulation to Improve the Indian Railways’ Efficiency. In German Conference on Multiagent System Technologies (pp. 278-291). Springer, Berlin, Heidelberg.
  • Ginovart Gisbert, M. (2013). A simple discrete simulation model to explore in the classroom some rules involved in the decision-making process: the cellular automata. Hellenic Mathematical Society International Journal for Mathematics in Education, 5, 3-23.
  • Ginovart, M. (2013). Discovering the Power of Individual-Based Modelling in Teaching and Learning: The Study of a Predator-Prey System. Journal of Science Education and Technology (JSET). [HTML]
  • Gkiolmas, A., Karamanos, K., Chalkidis, A., Skordoulis, C., Papaconstantinou, M., & Stavrou, D. (2013). Using Simulations of NetLogo as a Tool for Introducing Greek High-School Students to Eco-Systemic Thinking. Advances In Systems Science and Applications, 13(3), 275-297. [PDF]
  • Grüter, C., Schürch, R., Farina, W.M. (2013). Task-partitioning in insect societies: Non-random direct material transfers affect both colony efficiency and information flow. J. Theor. Biol., 327, 23–33. doi:10.1016/j.jtbi.2013.02.013
  • Guerin, A. J., Jackson, A. C., & Youngson, A. F. (2013). An agent-based model of Atlantic salmon migration in Scottish coastal waters.
  • Guerreiro, O., Ferreira, M., Cascalho, J., & Borges, P. (2013, September). Towards an Agent Based Modeling: The prediction and prevention of the spread of the drywood termite Cryptotermes brevis. In Portuguese Conference on Artificial Intelligence (pp. 480-491). Springer, Berlin, Heidelberg.
  • Guerrero, D.A., Jimenez, R.M., & Rodriguez-Colina, E. (2013). WSN simulation model with a complex systems approach. In Proceedings of the 2013 Summer Computer Simulation Conference (SCSC '13), Article No. 41.
  • Gulden, T. R. (2013). Agent-Based Modeling as a Tool for Trade and Development Theory. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 1. [HTML]
  • GUO, L. B., LUO, X. X., & ZHU, M. X. (2013). An evolutionary game model in mobile business commerce customer trust. Journal of Theoretical & Applied Information Technology, 48(1).
  • Hajebi, S., Barrett, S., Clarke, A., & Clarke, S. (2013). Multi-agent simulation to support water distribution network partitioning.
  • Halter, C., & Levin, J. (2013). Multi-mediator models of new teacher learning through creating multimedia. Paper presented at the 2013 American Educational Research Association meetings. San Francisco, CA. [HTML]
  • Hang, L. U. O. (2013). Multi-Agent Simulation Research on Urban Agglomeration Integrationand Local Government Interaction. Journal of Dalian University of Technology (Social Sciences), (2), 10.
  • Hart, D. (2013). A general education simulation and modeling course to stimulate interest in science. Journal of Computing Sciences in Colleges, 28(6), 84-89.
  • Hasson, S. T., & Al-Najar, A. A. M. (2013). Performance Evaluation of Breera Using Net Logo Simulator.
  • Hayes, B. (2013). The math of segregation. American Scientist, 101(5), 338-341.
  • Heckbert, S. (2013). MayaSim: An Agent-Based Model of the Ancient Maya Social-Ecological System. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 11. [HTML] (Oct 2013)
  • Hirsh, A., & Levy, S. T. (2013). Biking with particles: Junior triathletes’ learning about drafting through exploring agent-based models and inventing new tactics. Technology, Knowledge and Learning, 18(1-2), 9-37.
  • Hogg, C. J., Keith, R. L., & Tenney, C. M. (2013). Neighborhood Evacuation Model (No. SAND2013-7633P). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
  • Holst, N. (2013). A universal simulator for ecological models. Ecological Informatics, 13, 70-76.
  • Hongtao, X., Lincheng, S., Huayong, Z., Daibing, Z., & Xiaojia, X. (2013, January). Multi-Agent Coevolutionary Learning Method Based on Individual Rule Set. In 2013 Third International Conference on Intelligent System Design and Engineering Applications (pp. 978-981). IEEE.
  • Huang, L., Deng, S., Li, Y., Wu, J., Yin, J., & Li, G. (2013). A trust evaluation mechanism for collaboration of data-intensive services in cloud. Applied Mathematics & Information Sciences, 7(1L), 121-129.
  • Hui, L. I. U. (2013). A NetLogo Simulation Study of the Entry and Exit Behavior of Entrepreneurial Team Members. Journal of Wuyi University (Natural Science Edition), (2), 10.
  • Iandoli, L., Marchione, E., Ponsiglione, C., & Zollo, G. (2013). Learning and structural properties in small firms’ networks: a computational agent-based model. Research in Economics and Business: Central and Eastern Europe, 1(1).
  • Ilyas, Q. M. (2013). A netlogo model for ramy al-jamarat in hajj. Journal of Basic and Applied Scientific Research, 3(12), 199-209.
  • Izquierdo, L.R., Izquierdo, S.S., Galán, J.M. & Santos, J.I. (2013). Combining Mathematical and Simulation Approaches to Understand the Dynamics of Computer Models. In Simulating Social Complexity: A Handbook. Series: Understanding Complex Systems. Springer-Verlag, pp. 235-271. [PDF]
  • Janošek, M., & Farana, R. (2013). Traffic Lights Strategy Adaptation. American Journal of Mechanical Engineering, 1(7), 226-230.
  • Janošek, M., Kocian, V., & Volná, E. (2013). Complex System Simulation Parameters Settings Methodology. In Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems (pp. 413-422). Springer, Heidelberg.
  • Jansson, F. (2013). Pitfalls in Spatial Modelling of Ethnocentrism: A Simulation Analysis of the Model of Hammond and Axelrod. Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 2. [HTML] (June 2013)
  • Jaye, M., & Burks, R. (2013). Docking Two Models of Insurgency Growth. International Journal of Operations Research and Information Systems (IJORIS), 4(3), 19-30.
  • Johnson, J. F., & Hoe, D. H. (2013, July). Designing an agent based model for the efficient removal of red imported fire ant colonies. In Proceedings of the 2013 Summer Computer Simulation Conference (pp. 1-7).
  • Jordan, R. C., Hmelo-Silver, C., Liu, L., & Gray, S. A. (2013). Fostering reasoning about complex systems: using the aquarium to teach systems thinking. Applied Environmental Education & Communication, 12(1), 55-64.
  • Junges, R., & Klügl, F. (2013). Learning tools for agent-based modeling and simulation. KI-Künstliche Intelligenz, 27(3), 273-280.
  • Kasmire, J., Nikolic, I., & Dijkema, G. (2013). Evolving Greenhouses: An Agent-Based Model of Universal Darwinism in Greenhouse Horticulture. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 7. [HTML] (Oct 2013)
  • Kazak, S. (2013). Modeling random binomial rabbit hops. In Modeling Students' Mathematical Modeling Competencies (pp. 561-570). Springer, Dordrecht.
  • Kennedy, W. G., & Harrison, J. F. (2013). Towards Representing Disasters in Computational Social Simulations.
  • Kim, Y., Zhong, W., and Chun, Y. (2013). Modeling Sanction Choices on Fraudulent Benefit Exchanges in Public Service Delivery. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 8. [HTML]
  • Kiourt, C., & Kalles, D. (2013, September). Building a social multi-agent system simulation management toolbox. In Proceedings of the 6th Balkan Conference in Informatics (pp. 66-70).
  • Kokkinogenis, Z., Almeida, J. E., & Rossetti, R. J. (2013). NetLogo implementation for Crowd Evacuation. In MECC 2013–International Conference and Advanced School Planet Earth, Mathematics of Energy and Climate Change.
  • Kononova, K., & López-Sánchez, M. (2013, October). Evolutionary Processes in Economics: Multi-Agent Model of Macrogenerations Dynamics. In CCIA (pp. 311-315).
  • Koohborfardhaghighi, S., & Kim, J. (2013). Using structural information for distributed recommendation in a social network. Applied intelligence, 38(2), 255-266.
  • Kovalev, A. (2013). Stochastic simulation model of employment and migration.
  • Kurniawan, F. (2013, November). Completion camper reaver game scenario in 3D view with uninformed search methode using Netlogo software.
  • Lam, C. Y., Lin, J., Sim, M. S., & Tai, K. (2013). Identifying vulnerabilities in critical infrastructures by network analysis. International journal of critical infrastructures, 9(3), 190-210.
  • LE, Y., ZHANG, B., GUAN, X. J., & LI, Y. K. (2013). Research of the Relationship between Government Investment Projects Institutions and Behavioral Strategies Based on Evolutionary Game Theory. Journal of Engineering Management, (3), 10.
  • Lee, J. K., & Park, J. W. (2013). A Study on the Calculation of Pedestrian’s Conflict Index Using Multi-Agent Based Model (Multi-ABM)-Focused on the Netlogo Simulation Model. Journal of Korea Transport Institute, 20(4), 105-116.
  • Lee, K., Kim, S., Kim, C. O., & Park, T. (2013). An agent-based competitive product diffusion model for the estimation and sensitivity analysis of social network structure and purchase time distribution. Journal of Artificial Socities and Social Simulation, 16(1). doi.org/10.18564/jasss.2080
  • Lee, M., Kim, S., Kim, C-O, Park, T. (2013). An Agent-Based Competitive Product Diffusion Model for the Estimation and Sensitivity Analysis of Social Network Structure and Purchase Time Distribution. Journal of Artificial Societies and Social Simulation, 16 (1): 3 [HTML]
  • Lee, S. J., Lee, K. H., & Kang, S. J. (2013). Study on a pedestrian simulation model of natural movement. Journal of Asian Architecture and Building Engineering, 12(1), 41-48.
  • Lee, T., Yao, R. (2013). Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis. Energy Policy, 52, 63–72. [PDF]
  • Levin, J., & Halter, C. (2013). Creating and using multimedia as artifacts for mediating learning.
  • Li, C., & Zhang, P. (2013, July). Bidding Strategies and Equity Auction Based on Social Network. In 2nd International Conference on Science and Social Research (ICSSR 2013). Atlantis Press.
  • Li, N. W., Huang, J. T., & Niu, L. X. (2013). Evolutionary model of miners’ violation behavior based on multi-agent simulation. China Saf. Sci. J, 23(11), 10-15.
  • Liao, S. L., & Lai, J. Z. (2013). Research on Modeling and Simulation of Complex Adaptive Systems Based on Multi-Agent. In Advanced Materials Research (Vol. 756, pp. 4497-4501). Trans Tech Publications Ltd.
  • Liu, C., Sibly, R.M., Grimm, V. & Thorbek, P. (2013). Linking pesticide exposure and spatial dynamics: an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes. Ecological Modelling, 248, 92-102. [PDF]
  • Liu, H., & Silva, E. A. (2013). Simulating the dynamics between the development of creative industries and urban spatial structure: an agent-based model. In Planning support systems for sustainable urban development (pp. 51-72). Springer, Berlin, Heidelberg.
  • Liu, H., Chen, X., & Zhang, B. (2013). An approach for the accurate measurement of social morality levels. PloS one, 8(11), e79852.
  • Liu, S., Dong, S. H., & An, Q. H. (2013). Research on Public Transportation Based on Complex Network and Multi-Agent Simulation. In Applied Mechanics and Materials (Vol. 364, pp. 183-187). Trans Tech Publications Ltd.
  • Liu, Y. (2013). Relationship between industrial firms, high-carbon and low-carbon energy: An agent-based simulation approach. Applied Mathematics and Computation, 219(14), 7472-7479.
  • LIU, Z. D., & JIANG, C. H. (2013). Study on the association of safety production and economic development based on NetLogo platform [J]. Journal of Safety and Environment, 4.
  • Ma, Y., Shen, Z., & Kawakami, M. (2013). Agent-Based Simulation of Residential Promoting Policy Effects on Downtown Revitalization. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 2. [HTML]
  • Madani, A. (2013). An agent based simulation model for warning messages dissemination in a vehicular ad hoc network. International Journal on Computer Science and Engineering, 5(11), 914.
  • Madey, A. G. (2013). Unmanned Aerial Vehicle Swarms: The Design and Evaluation of Command and Control Strategies using Agent-Based Modeling. International Journal of Agent Technologies and Systems (IJATS), 5(3), 1-13.
  • Madey, A. G., & Madey, G. R. (2013, April). Design and evaluation of UAV swarm command and control strategies. In Proceedings of the Agent-Directed Simulation Symposium (pp. 1-8).
  • Malik, A.A., Crooks, A.T., & Root, H.L. (2013). “Can Pakistan have Creative Cities? An Agent Based Modeling Approach with Preliminary Application to Karachi.” Pakistan Strategy Support Program Working Paper 13, International Food Policy Research Institute (IFPRI), Washington, D.C. [HTML]
  • Manzo, G. (2013). “Educational Choices and Social Interactions: A Formal Model and A Computational Test.” Comparative Social Research, 30, 47-100. [PDF]
  • Martin, B.T., Jager, T., Preuss, T., Nisbet, R. & Grimm, V. (2013). Predicting population dynamics from the properties of individuals: a test of Dynamic Energy Budget theory. American Naturalist 18(4), 506-519 [PDF]
  • Mas, E., Adriano, B., & Koshimura, S. (2013). An integrated simulation of tsunami hazard and human evacuation in La Punta, Peru. Journal of Disaster Research, 8(2), 285-295. [HTML]
  • Meister, T., Thenius, R., Kengyel, D., & Schmickl, T. (2013, September). Cooperation of two different swarms controlled by BEECLUST algorithm. In Artificial Life Conference Proceedings 13 (pp. 1124-1125). One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press.
  • Mian, Z. T., Hall, L. A., & Mavris, D. N. (2013). Screening Level Mission Modeling and Simulation of Micro Autonomous Systems and Technologies. In AIAA Modeling and Simulation Technologies (MST) Conference (p. 4592).
  • Michel, F. (2013). Translating Agent Perception Computations into Environmental Processes in Multi‐Agent‐Based Simulations: A means for Integrating Graphics Processing Unit Programming within Usual Agent‐Based Simulation Platforms. Systems Research and Behavioral Science, 30(6), 703-715.
  • Milićević, D., Blagojević, B., & Trajković, S. (2013). Agent-based study of stormwater reuse system operational capabilities during drought.
  • Minichino, M., & Ciancamerla, E. (2013). Modeling cyber attacks on a critical infrastructure scenario. In 4th International Conference on Information, Intelligence, Systems and Applications, IISA 2013.
  • Minichino, M., & Ciancamerla, E. (2013). Modelling SCADA and corporate network of a medium voltage power grid under cyber attacks. In 10th International Conference on Security and Cryptography, SECRYPT 2013-Part of 10th International Joint Conference on E-Business and Telecommunications, ICETE 2013.
  • Morariu, C., Morariu, O., & Borangiu, T. (2013). Customer order management in service oriented holonic manufacturing. Computers in Industry, 64(8), 1061-1072.
  • Moreno, F., Guzmán, J., & Gómez, S. (2013). A formal model to identify patterns of movement in sets of moving objects. MODELLING FOR ENGINEERING AND HUMAN BEHAVIOUR 2013, 106.
  • Mota, F. P., Dimuro, G. P., Rosa, V., & Botelho, S. S. D. C. (2013, May). Simulating the impacts of the energy consumption using multi-agent systems. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 399-404). Springer, Berlin, Heidelberg.
  • Mota, F. P., Rosa, V., Botelho, S. S. D. C., Santos, I., & Dimuro, G. (2013, September). Simulating the Consumers' Energy Profiles Using Multiagent Systems. In 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (pp. 394-401). IEEE.
  • Murimi, R. (2013, October). Sparking creativity in computer science for interdisciplinary students. In 2013 IEEE Frontiers in Education Conference (FIE) (pp. 1111-1112). IEEE.
  • Muscalagiu, I. (2013). MAS NetLogo Models-a. [HTML]
  • Muscalagiu, I., Illes, C., & Popa, H.E. (2013). Large Scale Multi-Agent-Based Simulation using NetLogo for the multi-robot exploration problem. In IEEE 11th International Conference on Industrial Informatics, Bochum, Germany, pp. 225-230. [HTML]
  • Muscalagiu, I., Muscalagiu, D. M., Berdie, A., & Pănoiu, M. (2013). Modeling of the Protein Folding Problem Using Distributed Constraint Optimization. Global Journal on Technology, 3.
  • Muscalagiu, I., Popa, H. E., Panoiu, M., & Negru, V. (2013, September). Multi-agent systems applied in the modelling and simulation of the protein folding problem using distributed constraints. In German Conference on Multiagent System Technologies (pp. 346-360). Springer, Berlin, Heidelberg.
  • Muscalagiu, I., Popa, H.E., & Vidal, J. (2013). Clustered Computing with NetLogo for the evaluation of asynchronous search techniques. In proceedings of 12th IEEE International Conference on Intelligent Software Methodologies, Tools and Techniques (SOMET 2013) (pp. 115-120). Budapest. [HTML]
  • Muscalagiu, I., Popa, H.E., & Vidal, J. (2013). Large Scale Multi-Agent-Based Simulation using NetLogo for implementation and evaluation of the distributed constraints. In proceedings of IJCAI – DCR 2013, 23rd International Joint Conference on Artificial Intelligence - Workshop on Distributed Constraint Reasoning, Beijing.
  • Na, Y. G., Lee, S., & Joh, C. H. (2013). A Study on Relationships between Travel Time and Provision of Road Inundation Information in Heavy Rain and Snow using an Agent-based Simulation Model. Journal of the Economic Geographical Society of Korea, 16(2), 262-274.
  • Nava, R., & Wilhelmy, R. (2013). Multiagent modeling of a hunter-prey scenario using ContractNET.
  • Newman, S. D., & Soylu, F. (2013). The impact of finger counting habits on arithmetic in adults and children. Psychological research, 1-8. [HTML]
  • O'Neil, D. A., & Petty, M. D. (2013). Organizational simulation for model based systems engineering. Procedia Computer Science, 16, 323-332.
  • Olaya, S. S., & Díaz, S. R. Integrated Household Solid Waste Management Simulator.
  • Olševičová, K., & Cimler, R. (2013, July). Simulation of Visitor Flow Management with Context-based Information System. In Workshop Proceedings of the 9th International Conference on Intelligent Environments (Vol. 17, p. 386). IOS Press.
  • Olševičová, K., Cimler, R., & Machálek, T. (2013). Agent-based model of celtic population growth: Netlogo and python. In Advanced Methods for Computational Collective Intelligence (pp. 135-143). Springer, Berlin, Heidelberg.
  • Otcenaskova, T. (2013). Management of biological and chemical incidents: simulation-based decision support. Scientia Agriculturae Bohemica (Czech Republic).
  • Ozik, J., Collier, N. T., Murphy, J. T., & North, M. J. (2013, December). The ReLogo agent-based modeling language. In 2013 Winter Simulations Conference (WSC) (pp. 1560-1568). IEEE.
  • Pallant, A., Damelin, D., & Pryputniewicz, S. (2013). Deep space detectives. The Science Teacher, 80(2), 45.
  • Pallant, A., Damelin, D., & Pryputniewicz, S. (2013). Searching for planets suitable for life. The Science Teacher, 80(2), 45.
  • Paolucci, M., & Vicidomini, L. (2013, August). A Distributed Simulation of Roost-Based Selection for Altruistic Behavior in Vampire Bats. In European Conference on Parallel Processing (pp. 575-584). Springer, Berlin, Heidelberg.
  • Passos, G.F., & Chamovitz, I. (2013). Modelo De Responsabilidade Organizacional, Aplicado Em Empresa Pública De Tecnologia Da Informação E Fundamentado Em Dinâmica De Sistemas. In: IX Congresso Nacional de Excelência em Gestão – CNEG 2013, 2013, Rio de Janeiro – RJ. [PDF]
  • Passos, G.F., Chamovitz, I., Theodoulidis, B. (2013).Organizational Responsibility Model: Dealing with Demand for Services Higher Than Installed Capacity. In: Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on. IEEE, 2013. p. 4415-4420. [HTML]
  • Pennisi, Marzio; Rajput, Abdul-Mateen; Pappalardo, Francesco; Toldo, Luca Pennisi, M.; Rajput, A.; Toldo, L.; Pappalardo, F. (2013). Agent based modeling of Treg-Teff cross regulation in relapsing-remitting multiple sclerosis. Bio Med Central [HTML]
  • Petreska, I., & Stamatopoulou, I. (2013, September). A comparative study of tools for visualisation of state-based spatial multi-agent models. In Proceedings of the 6th Balkan Conference in Informatics (pp. 53-60).
  • Piau, P., Siang, A. K., & Labadin, J. (2013, July). An epidemic aggregate and individual-based model in a patchy environment. In 2013 8th International Conference on Information Technology in Asia (CITA) (pp. 1-5). IEEE.
  • Pinacho, P., Pau, I., Chacón, M., Sánchez, S. (2013). An ecological approach to anomaly detection: The EIA model. Artifical Immune Systems: Lecture Notes in Computer Science, Vol. 7597, 2012. [PDF]
  • Pontoppidan, M.B., & Nachman, G. (2013). Changes in behavioural responses to infrastructure affect local and regional connectivity - A simulation study on pond breeding amphibians. Nature Conservation, 5, 13-28. doi: 10.3897/natureconservation.5.4611
  • Pontoppidan, M.B., & Nachman, G. (2013). Effects of within-patch heterogeneity on connectivity in pond-breeding amphibians studied by means of an individual-based model. Web Ecol., 13, 21-29, doi:10.5194/we-13-21-2013
  • Pontoppidan, M.B., & Nachman, G. (2013). Spatial Amphibian Impact Assessment - A management tool for assessment of road effects on regional populations of Moor frogs (Rana arvalis). Nature Conservation, 5, 29-52. doi: 10.3897/natureconservation.5.4612
  • Purnomo, H., Suyamto, D., & Irawati, R. H. (2013). Harnessing the climate commons: an agent-based modelling approach to making reducing emission from deforestation and degradation (REDD)+ work. Mitigation and Adaptation Strategies for Global Change, 18(4), 471-489.
  • Qin, Y., Jim, G., Min, L., & Yao, Y. (2013, April). Micro Behavior Information Decision Research in An ABM Traffic and Energy Model. In 2013 IEEE Green Technologies Conference (GreenTech) (pp. 22-27). IEEE.
  • Ratamero, E. M. (2013). Modelling Peloton Dynamics in Competitive Cycling: A Quantitative Approach. In Sports Science Research and Technology Support (pp. 42-56). Springer International Publishing. (DOI 10.1007/978-3-319-17548-5_4).
  • Rebaudo, F., & Dangles, O. (2013). An agent-based modeling framework for integrated pest management dissemination programs. Environmental modelling & software, 45, 141-149.
  • REN, K., & PU, J. Y. (2013). The parameterized simulation methods of the flame control of a new firefighting training devices. Fire Science and Technology, (8), 34.
  • Reuillon, R., Leclaire, M., & Rey-Coyrehourcq, S. (2013). OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models. Future Generation Computer Systems, 29(8), 1981-1990.
  • Rogers, K., Jenkin, T. A., Corbett, J., & Webster, J. (2013, January). The effects of'green'on IT/S projects: Recycling the garbage can model. In 2013 46th Hawaii International Conference on System Sciences (pp. 974-983). IEEE.
  • Roman, B. (2013).An Agent-based Model for the Humanities. Digital Humanities Quarterly, 7 (1). [HTML]
  • Romanowska, I. (2013). Agent-based Modelling and Archaeological Hypothesis Testing: the Case Study of the European Lower Palaeolithic. The 41st Conference on Computer Applications and Quantitative Methods in Archaeology. http://doi.org/10.5117/9789089647153-20
  • RU, B. F., & ZHANG, C. L. (2013). Simulation on credit evolutionary of business cooperation based on small world network. Application Research of Computers, (12), 13.
  • Ruoyu, L., & Weijie, Z. (2013). The Simulation Research of Multi-agent Innovation Performance. Science & Technology Progress and Policy, 2013(21), 2.
  • Rush, R., Daugherty, A., Raulie, R., & Rush, D. (2013). Team Members.
  • Russell, J. E. (2013). Using a retail location game to explore Hotelling’s Principle of Minimum Differentiation. Business Education Innovation Journal, 5(2), 48-52.
  • Ryder, E. F., Boyd, J. R., Marsden, T. B., Sullender, M. E., Reider, D., & White, B. T. (2013). Modeling from molecules to moose: teaching students to develop agent-based simulations in biology, Supercomputing Conference 2013 (SC13), Denver, CO, United States.
  • Samon, S., & Levy, S. T. (2013, April). What Does a Complex Systems Perspective Offer Science Learning? Junior High-‐school Students’ Learning of Chemical Systems with an Agent-‐based Viewpoint versus a Disciplinary Viewpoint. In The Annual meeting of the American Educational Research Association.
  • Sanzgiri, A., Hughes, A., & Upadhyaya, S. (2013, September). Analysis of malware propagation in Twitter. In 2013 IEEE 32nd International Symposium on Reliable Distributed Systems (pp. 195-204). IEEE.
  • Sathasivam, S., Ng, P. F., & Hamadneh, N. (2013). Developing agent based modelling for reverse analysis method. Journal of Applied Sciences, Engineering and Technology, 6(22), 4281-4288.
  • Sengupta, P., Kinnebrew, J. S., et al. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. [HTML] (Jan 2013)
  • Shanshan, W., & Chunxiao, Z. (2013, January). NetLogo Based Model for VANET Behaviors Dynamic Research. In 2013 Third International Conference on Intelligent System Design and Engineering Applications (pp. 990-993). IEEE.
  • Sharma, R., Tiwari, A. K., & Kumar, G. S. (2013, April). Novel modeling paradigm for the algal production of biofuel. In 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO) (pp. 1-6). IEEE.
  • Shen, T., Cai, H., & Chen, X. (2013, November). Simulation Research of the Downloads Influence in Social Applications. In Proceedings of the 2013 Fifth International Conference on Multimedia Information Networking and Security (pp. 762-768).
  • Shiba, N. (2013). Analysis of Asymmetric Two-Sided Matching: Agent-Based Simulation with Theorem-Proof Approach. Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 11. [HTML] (June 2013)
  • SHIRAFKAN, M., SETAYESHI, S., & ATAR, M. A. (2013). CHECKING OF ANTIBACTERIAL EFFECT OF NANO-ENCAPSULATED NISIN ON LISTERIA MONOCYTOGENES POPULATION IN FETA CHEESE.
  • SHIRAFKAN, M., SETAYESHI, S., & ATAR, M. A. (2013). CHECKING OF POPULATION OF LISTERIA MONOCYTOGENES IN FETA CHEESE DURING ITS RIPENING BY CELLULAR AUTOMATA.
  • Song, Y., Sun, D., & Chu, Z. (2013). Modeling and Simulation of Reliability of Collaborative Decision-making System in Nuclear Power Plant Accidents. Advances in Information Sciences and Service Sciences, 5(13), 26.
  • Squazzoni, F. Gandelli, C. (2013). Opening the Black-Box of Peer Review: An Agent-Based Model of Scientist Behaviour. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 3. [HTML]
  • Stonedahl, F., Weintrop, D., Blikstein, P., & Shannon, C. (2013, March). NetLogo: teaching with turtles and crossing curricular boundaries. In Proceeding of the 44th ACM technical symposium on Computer science education (pp. 763-763).
  • Sumam, M. I., & Vani, K. (2013). Agent based evacuation simulation using leader-follower model.
  • Sun, Z., & Müller, D. (2013). A framework for modeling payments for ecosystem services with agent-based models, Bayesian belief networks and opinion dynamics models. Environmental modelling & software, 45, 15-28.[HTML]
  • Szilagyi, M. N. (2013). Determination of the potential distribution of electron optical systems by agent-based simulation. Optik-International Journal for Light and Electron Optics, 124(18), 3451-3452.
  • Szilagyi, M. N. (2013). Solution of partial differential equations by agent-based simulation. European Journal of Physics, 35(1), 018003.
  • Talbot, K., & Hug, B. (2013). WHAT MAKES US TICK… TOCK?: USING FRUIT FLIES TO STUDY CIRCADIAN RHYTHMS. Science teacher (Normal, Ill.), 80(9), 37.
  • Terna, P. (2013). Review of Methodological Cognitivism: Vol. 1: Mind, Rationality, and Society.
  • Thierry, H., Amalric, M., Corson, N., Langlois, P., Monteil, C., Marilleau, N., & Sheeren, D. (2013, October). Linking macro and micro scales in a predator‐prey individual‐based model. In Colloque ISEM 2013: Modélisation des écosystèmes durables dans le contexte des changements globaux (pp. 22-p).
  • Thornburg, D. D. (2013). 5 Tech Tools for the Next Generation Science Standards: An Education Futurist Shares His Favorite Software and Hardware to Help Teach the NGSS. THE Journal (Technological Horizons In Education), 40(10), 10.
  • Tisue, S., & Wilensky, U. (2004, updated 2013).NetLogo: Design and implementation of a multi-agent modeling environment. In Proceedings of the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity and Emergence, Chicago, Illinois, October 2004. [PDF]
  • Tomasini, M., et al. (2013).Evaluating the Performance of Social Networks of Sensors under Different Mobility Models. Social Computing (SocialCom), International Conference on. IEEE.
  • Tomasini, M., Zambonelli,, F., & Menezes, R. (2013).Using patterns of social dynamics in the design of social networks of sensors. Green Computing and Communications (GreenCom), IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing.
  • Trouille, L., Beheshti, E., Horn, M., Jona, K., Kalogera, V., Weintrop, D., & Wilensky, U. (2013).Bringing Computational Thinking into the High School Science and Math Classroom. In American Astronomical Society, AAS Meeting #221, #201.09
  • Tseng, S. H., & Allen, T. T. (2013, December). A magic number versus trickle down agent-based model of tax policy. In 2013 Winter Simulations Conference (WSC) (pp. 1407-1418). IEEE.
  • Tuker, M., Balli, S., & Pembeci, İ. (2013). Ant Colony Optimization In Multi-Agent Systems With NetLogo.
  • Turi, D. (2013). Hardware acceleration of simulations of distributed systems.
  • Vinatier, F., Gosme, M., & Valantin-Morison, M. (2013).Explaining host-parasitoid interactions at the landscape scale: a new approach for calibration and sensitivity analysis of complex spatio-temporal models. Landscape Ecology, 28 (2): 217-231. [PDF]
  • Wafda, F., Saputra, R. W., Nurdin, Y., & Munadi, K. (2013, June). Agent-based tsunami evacuation simulation for disaster education. In International Conference on ICT for Smart Society (pp. 1-4). IEEE.
  • Wagh. A. & Wilensky, U. (2013).Leveling the playing field: Making multi-level evolutionary processes accessible through participatory simulations. Proceedings of CSCL, Madison, Wisconsin, June 15-19
  • Wagner, N., Agrawal, V. (2013). An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster. Expert Systems with Applications, 41(6), 2807-2815. [HTML]
  • Waldeck, R. (2013). Segregated Cooperation. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 14. [HTML] (Oct 2013)
  • Wan, S. S., Wang, D. L., & Cao, Q. (2013). Multi-agent Based Modeling Simulation about VANET. In Advanced Materials Research (Vol. 760, pp. 680-684). Trans Tech Publications Ltd.
  • Wang, L., Zhang, Q., Cai, Y., Zhang, J., & Ma, Q. (2013). Simulation study of pedestrian flow in a station hall during the Spring Festival travel rush. Physica A: Statistical Mechanics and its Applications, 392(10), 2470-2478.
  • Weidong, W., & Xilai, L. (2013). Soil Erosion Model and Simulation of Degraded Grassland of Alpine Meadow in Sanjiangyuan Region. Environmental Science and Management, (7), 9.
  • Weintrop, D, Hjorth, A, & Wilensky, U. (2013).Know Your Network: Learning Social Networks Analysis Through Meaningful Manipulation. Poster presented at InfoSocial 2013. Evanston, IL, USA.
  • Weisberg, M. (2013). Modeling Herding Behavior and Its Risks. Journal of Economic Methodology, 20, 6–18.
  • Weisberg, M. (2013). Simulation and Similarity: Using Models to Understand the World. Oxford University Press.
  • White, D. & Levin, J. (2013). Navigating the Turbulent Waters of School Reform Guided by Complexity Theory. Paper presented at the meetings of the American Educational Research Association, San Francisco, California, USA. [PDF]
  • Wiens, J., & Monett, D. (2013). Using BDI-extended NetLogo agents in undergraduate CS research and teaching. In Proceedings of the International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). [PDF]
  • Wilensky, U. (2001, updated 2013)Modeling nature's emergent patterns with multi-agent languages. Proceedings of EuroLogo 2001. Linz, Austria. [PDF]
  • Xiong, J., Duan, Z., & Wang, Y. (2013). Modeling and simulation of the inter-organizational knowledge transfer impact factors in industrial clusters. In The 19th International Conference on Industrial Engineering and Engineering Management (pp. 161-171). Springer, Berlin, Heidelberg.
  • Yang, S., Lu, M., & Ge, L. (2013, June). Bayesian Network Based Software Reliability Prediction by Dynamic Simulation. In 2013 IEEE 7th International Conference on Software Security and Reliability (pp. 13-20). IEEE [HTML]
  • Yaning, L. (2013). Legal Solutions on the Basis of Netlogo for Moral Failure. Xiamen University Law Review, (2), 11.
  • Yi, S. U. (2013). The model and simulation of mass unexpected events. Journal of Liaoning Normal University (Natural Science Edition), (3), 11.
  • Yongdan, L. (2013). Model Implementation of Wechat Public Opinion evolution based on Netlogo. Wireless connected Technology, 11, 197-198.
  • Yu, K. K. (2013). Evolution of Macro-generations: Multi-agent Approach. Business Inform, (10), 113-117.
  • Yu, Y., Shi, Q. F., & Huang, Y. (2013). Simulation of Tacit Knowledge Sharing among Organizations Using a Cellular Automaton Model. In Applied Mechanics and Materials (Vol. 263, pp. 3184-3187). Trans Tech Publications Ltd.
  • Yuanyuan, G., & Jingyuan, D. J. Y. (2013). An agent based simulation for the adoption behavior of multi-energy. Electronic Measurement Technology, (1), 3.
  • Zaharija, G., Mladenović, S., & Boljat, I. (2013). Introducing basic programming concepts to elementary school children. Procedia-social and behavioral sciences, 106, 1576.
  • Zeuner, R., Lopez, S., Link, A., Muehlhausen, S., & Chaney, M. (2013). Predator Mayhem.
  • Zhenjiang, S., Yan, M., & Yunfeng, L. (2013). Web-Based Decision Making Support System for Integrated Urban Water Management.

2012

  • Abrahamson, D., Gutierrez, J.F., & Baddorf, A.K. (2012). Try to see it my way: the discursive function of idiosyncratic mathematical metaphor. Mathematical Thinking and Learning , 14(1), 55-80.
  • Almeida, J., Kokkinogenis, Z., & Rossetti, R. (2012). NetLogo implementation of an evacuation scenario. In the Proceedings of the 7th Iberian Conference on Information Systems and Technologies (CISTI).
  • Almudí, I., Fatás-Villafranca, F. & Izquierdo, L.R. (2012). Innovation, Catch-up and Leadership in Science-Based Industries. Industrial and Corporate Change, Volume 21, Number 2, pp. 345–375. [PDF]
  • Alqithami, S., & Hexmoor, H. (2012). Rapid adaptation in computational organizations. In Proceedings on the International Conference on Artificial Intelligence (ICAI) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
  • Amato, K. R., Martin, B., Pope, A., Theiling, C., Landwehr, K., Petersen, J., ... & Sparks, R. (2012). Spatially explicit modeling of productivity in pool 5 of the Mississippi River. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 151-170). Springer, Boston, MA.
  • Aristotelis, G., Anthimos, C., Kostas, K., Maria, P., & Constantine, S. (2012). A Constructionist Method for Teaching Teachers about Basic Properties of Complex Systems, using a NetLogo Model.
  • Arsenault, A., Nolan, J., Schoney, R., & Gilchrist, D. (2012). Outstanding in the field: Evaluating auction markets for farmland using multi-agent simulation. Journal of Artificial Societies and Social Simulation, 15(1). doi.org/10.18564/jasss.1827
  • Azab, A., & AlGeddawy, T. (2012). Simulation methods for changeable manufacturing. Procedia CIRP, 3, 179-184.
  • Babiš, M., & Magula, P. (2012, September). NetLogo—An alternative way of simulating mobile ad hoc networks. In 2012 5th Joint IFIP Wireless and Mobile Networking Conference (WMNC) (pp. 122-125). IEEE.
  • Barth, R., Meyer, M., & Spitzner, J. (2012). Typical pitfalls of simulation modeling - Lessons learned from armed forces and business. Journal of Artificial Societies and Social Simulation, 15(2). doi.org/10.18564/jasss.1935
  • Bastianelli, G., Salamon, D., Schisano, A., & Iacobacci, A. (2012, October). Agent-based simulation of collaborative unmanned satellite vehicles. In 2012 IEEE First AESS European Conference on Satellite Telecommunications (ESTEL) (pp. 1-6). IEEE.
  • Belbase, S., Hutchison, L. S., & Edelman, J. L. (2012, November). TECHNOLOGY INTEGRATION IN PRE-SERVICE SECONDARY MATHEMATICS TEACHER EDUCATION: PROSPECTS, PRIORITIES, AND PROBLEMS. In Psychology of Mathematics Education (p. 1142).
  • BenDor, T., & Westervelt, J. D. (2012). A technique for rapidly forecasting regional urban growth. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 223-234). Springer, Boston, MA.
  • Berkel, S. v., Turi, D., Pruteanu, A., & Dulman, S. (2012). Automatic discovery of algorithms for multi-agent systems. Paper presented at the Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, Philadelphia, Pennsylvania, USA. [PDF]
  • Bersini, H. (2012). UML for ABM. Journal of Artificial Societies and Social Simulation, 15(1). doi.org/10.18564/jasss.1897
  • Biondo, A. E., Rapisarda, A., & Pluchino, A. (2012). Return migration after brain drain: An agent based simulation approach (No. arXiv: 1206.4280).
  • Braga, D. D. S., Alves, F. O. M., Neto, F. B. D. L., & Menezes, L. C. D. S. (2012, August). An aspect-oriented domain-specific language for modeling multi-agent systems in social simulations. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 578-585). Springer, Berlin, Heidelberg.
  • Bragin, J. (2012). Book review of: Niazi, M.A., & Hussain, A. (2012). Cognitive Agent-Based Computing-I: a Unified Framework for Modeling Complex Adaptive Systems Using Agent-Based & Complex Network-Based Methods (SpringerBriefs in Cognitive Computation). Journal of Artificial Societies and Social Simulation. 16 (3) Reviews - 4 [HTML]
  • Brandl, M., Kellner, K., & Fabian, C. (2012). Simulation and Implementation of an Attractiveness based On-Demand Routing Algorithm for Wireless Sensor Networks. Procedia Engineering, 47, 908-911.
  • Bureš, V., Čech, P., & Otčenášková, T. (2012). Proposal of simulation-based management of biological or chemical incidents as a smart solution. International Review on Computers and Software, 7(5), 2173-2178.
  • Burguillo-Rial, J. C., Rodriguez-Hernandez, P. S., Montenegro, E. C., & Castiñeira, F. G. (2012). History-based self-organizing traffic lights. Computing and Informatics, 28(2), 157-168.
  • Burns, A., Nanayakkara, P., Courtney, J., & Roberts, T. (2012). Complex Adaptive Systems, Agent-Based Modeling and Information Assurance.
  • Burton, J. L., Drigo, M., Li, Y., Peralta, A., Salzer, J., Varala, K., ... & Westervelt, J. D. (2012). A model for evaluating hunting and contraception as feral hog population control methods. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 133-150). Springer, Boston, MA.
  • Burton, J. L., Lance, R. F., Westervelt, J. D., & Leberg, P. L. (2012). An individual-based model for metapopulations on patchy landscapes-genetics and demography (IMPL-GD). In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 197-209). Springer, Boston, MA.
  • Burton, J. L., Robinson, E., & Ye, S. (2012). Spatially Explicit Agent-Based Model of Striped Newt Metapopulation Dynamics Under Precipitation and Forest Cover Scenarios. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 63-83). Springer, Boston, MA.
  • Cabreira, T. M., Dimuro, G. P., & de Aguiar, M. S. (2012, October). An evolutionary learning approach for robot path planning with fuzzy obstacle detection and avoidance in a multi-agent environment. In 2012 Third Brazilian Workshop on Social Simulation (pp. 60-67). IEEE.
  • Cabrera, E., Luque, E., Taboada, M., Epelde, F., & Iglesias, M. L. (2012, December). ABMS optimization for emergency departments. In Proceedings of the 2012 Winter Simulation Conference (WSC) (pp. 1-12). IEEE.
  • Cadiz, R. F., & Colasso, M. (2012). Osc-netlogo: A tool for exploring the sonification of complex systems using netlogo. In ICMC.
  • Caillou, P., & Gil-Quijano, J. (2012, June). Simanalyzer: Automated description of groups dynamics in agent-based simulations.
  • CAO, X., LIU, G. W., & YANG, Y. F. (2012). Industry Crisis Spread Analysis with Game Theory Based on Cellular Automaton. Systems Engineering, (1), 16.
  • Chen, L. (2012). Agent-based modeling in urban and architectural research: A brief literature review. Frontiers of Architectural Research, 1(2), 166-177.
  • Chi, J. J., Luo, X. M., Han, X. M., & Wang, B. W. (2012). Study on Simulation & Evaluation of Radar Network Operation Capability in Complex Electromagnetic Environment. In Advanced Materials Research (Vol. 591, pp. 862-866). Trans Tech Publications Ltd.
  • Cimler, R., Kautzká, E., Olševičová, K., & Gavalec, M. (2012, September). Agent-based model for comparison of aircraft boarding methods. In Proceedings of 30th International Conference Mathematical Methods in Economics Agent-Based (pp. 73-78).
  • Cimler, R., Olševičová, K., Machálek, T., & Danielisová, A. (2012, September). Agent-base model of agricultural practices in Late Iron Age. In Proceedings of the 15th Czech-Japan seminar on data analysis and decision making under uncertainty (pp. 166-171).
  • Ciutacu, I., Vîntu, D., & SĂVULESCU, I. (2012). Why can't we use the same cure for all crises?-An agent based modelling approach. REVISTA ECONOMICĂ, 45.
  • Collard, M., Collard, P., & Stattner, E. (2012). Mobility and Information Flow: Percolation in a Multi-Agent Model. International Conference on Ambient Systems, Networks and Technologies (ANT), ed. Elseiver. Procedia CS, Vol. 10, p. 22-29, 2012. [HTML]
  • Dămăceanu, R. C. (2012). Using Agent-Based Simulation Methodology for Teaching Economics. Analele Ştiinţifice ale Universităţii» Alexandru Ioan Cuza «din Iaşi. Ştiinţe ale Educatiei, (XVI), 115-122.
  • Damaceanu, R. C., & Capraru, B. S. (2012). Implementation of a multi-agent computational model of retail banking market using netlogo. Metalurgia International, 17(5), 230.
  • Drigo, M., Ehlschlaeger, C. R., & Sweet, E. L. (2012). Modeling intimate partner violence and support systems. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 235-253). Springer, Boston, MA.
  • Dyer, E.B. (2012, November). Exploring classroom discourse through an agent-based model. Poster presented at the 34th Annual Conference of PME-NA: Kalamazoo, MI: PME.
  • Fadaeian, M., & Fesharaki, M. N. (2012). A self-configuration model for execution management in Grid computing. International Journal of Computer Science Issues (IJCSI), 9(6), 396.
  • Ferguson, J. (2012). Semiotic Analysis of Students’ Use of Multi-Agent Based Modeling Software (NetLogo) in Making Meaning of Complex Systems in Science. Paper presented at the Proceedings of the Contemporary Approaches to Research in Mathematics, Science, Health and Environmental Education Symposium, Melbourne, Australia. [PDF]
  • Frenken, K., Izquierdo, L.R. & Zeppini,P. (2012). Branching innovation, recombinant innovation, and endogenous technological transitions. Environmental Innovation and Societal Transitions, Volume 4, pp. 25–35. [PDF]
  • Galic, N., H. Baveco, G. M. Hengeveld, P. Thorbek, E. Bruns, & P. J. van den Brink. (2012). Simulating population recovery of an aquatic isopod: Effects of timing of stress and landscape structure. Environmental Pollution 163:91-99.
  • Gargiulo, F., Lenormand, M., Huet, S., & and Baqueiro Espinosa, O. (2012). Commuting Network Models: Getting the Essentials. Journal of Artificial Societies and Social Simulation, 15(2). doi.org/10.18564/jasss.1964
  • Georgeon, O. L., & Sakellariou, I. (2012, June). Designing environment-agnostic agents.
  • Ginovart, M. & Prats, C. (2012). A bacterial individual-based virtual bioreactor to test handling protocols in a NetLogo platform. Paper presented at the Proceedings of the 7th Vienna International Conference on Mathematical Modelling, Vienna, Austria. [PDF]
  • Ginovart, M., Portell, X., & Blanco, M. (2012). Discovering the power of agent-based modelling to deal with complex systems in diverse contexts. FACILITATING ACCESS AND PARTICIPATION: MATHEMATICAL PRACTICES INSIDE AND OUTSIDE THE CLASSROOM, 383.
  • Gkiolmas A., Chalkidis A., Karamanos K., Papaconstantinou M., Skordoulis C. (2012). A constructionist method for teaching teachers about basic properties of complex systems, using a NetLogo model. Paper presented at the Proceedings of the conference "CONSTRUCTIONISM 2012", Athens, Greece. [PDF]
  • Grüter, C., Schürch, R., Czaczkes, T.J., Taylor, K., Durance, T., Jones, S.M., Ratnieks, F.L.W. (2012). Negative feedback enables fast and flexible collective decision-making in ants. Public Libr. Sci. One 7, e44501. doi:10.1371/journal.pone.0044501zzzz
  • Grüters, U., Cannicci, S., Vannini, M., & Dahdouh-Guebas, F. (2012). Excluding random walks in the foraging behaviour of the Portunid crab Thalamita crenata: modelisation and simulation based on real data. VLIZ Special Publication, 57, 77.
  • Grüters, U., Schmidt, H., & Berger, U. (2012). Mapping Functional-Structural Models to Fields of Neighborhood. In Meeting on Mangrove ecology, functioning and Management (MMM3) 2-6 July 2012, Galle, Sri Lanka (p. 76).
  • Guoxiang, R. (2012, October). Decision on firms' explorative and exploitative innovation strategy based on multi-agents simulation method. In 2012 International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 1, pp. 232-235). IEEE.
  • Hargrove, W. & Westerveldt, J. (2012). An Implementation of the Pathway Analysis Through Habitat (PATH) Algorithm Using NetLogo. Ecologist-developed Spatially-explicit Dynamic Landscape Models; Modeling Dynamic Systems; Springerlink pg. 211-222. [PDF]
  • Hashem, K., & Mioduser, D. (2012). The Contribution of Agent Based Modeling to Students" Evolving Understanding of Complexity. International Journal of Information and Education Technology, 2(5), 538.
  • Hassani-Mahmooei, B & Parris, B.W. (2012). Resource Scarcity, Effort Allocation and Environmental Security: An agent-based theoretical approach. Economic Modeling, 30, 183-192. DOI: 10.1016/j.econmod.2012.08.020
  • Hassani-Mahmooei, B. & Parris, B.W. (2012). Climate change and internal migration patterns in Bangladesh: an agent-based model. Environment and Development Economics, 17(6), 763-780. DOI: 10.1017/S1355770X12000290, Published online: 27 July 2012
  • Heckbert, S., Isendahl, C., Gunn, J., Brewer, S., Scarborough, V., Chase, A.F., Chase, D.Z., Costanza, R., Dunning, N., Beach, T., Luzzadder-Beach, S., Lentz D., & Sinclair, P. (2012). Growing the Maya social-ecological system from the bottom up: complex systems models of resilience. In: Handbook of Historical Ecology and Applied Archaeology, C. Isendahl & D. Stump, eds. Oxford University Press
  • Hemmerich, K. M. (2012). Implementation of the Model" Simulating Collective Misbelief" into a NetLogo environment.
  • Hickey, D.T., Soylu, F. (2012). Wikifolios, reflections, and exams for online engagement, understanding, and achievement. Journal of Teaching and Learning with Technology: 64-71. [PDF]
  • Hinojos-Mendoza, G., Garbolino, E., & Sanseverino-Godfrin, V. (2012, September). Urban dynamics simulation and climatic change scenarios for 2050 in order to identify areas of biodiversity's conservation in the French's Maritime Alps.
  • Hjorth, A. & Wilensky, U. (2012). Acting like a Turtle: A NetLogo Kinect Extension. Proceedings of the Constructionism 2012 Conference. Athens, Greece, Aug 21-25. [HTML]
  • Horn, M. S., Weintrop, D., Beheshti, E., & Olson, I. C. (2012). Spinners, Dice, and Pawns: Using Board Games to Prepare for Agent-Based Modeling Activities. Presented at the AERA, Vancouver, Canada. [PDF]
  • Horn, M.S. & Wilensky, U. (2012). NetTango: A mash-up of NetLogo and Tern. In Moher, T. (chair) and Pinkard, N. (discussant), When systems collide: Challenges and opportunities in learning technology mashups. Symposium presented at the annual meeting of the American Education Research Association,Vancouver, British Columbia.[PDF]
  • Hu, P., Gao, H. W., & Wang, G. R. (2012, December). The Strategic Interaction Based on Public Goods Provision on Infinite Endogenous Plane Lattice. In International Conference on Network Computing and Information Security (pp. 414-421). Springer, Berlin, Heidelberg.
  • Infante, W. F., Khan, A. F., Libatique, N. J. C., Tangonan, G. L., & Uy, S. N. Y. (2012, November). Performance evaluation of series hybrid and pure electric vehicles using lead-acid batteries and supercapacitors. In TENCON 2012 IEEE Region 10 Conference (pp. 1-5). IEEE.
  • Isaac, A. G. (2012). NetLogo Programming.
  • Iwamura, T., Fragoso, J., & Lambin, E. (2012). Agent-based modeling for the landuse change of hunter-gather societies and the impacts on biodiversity in Guyana. AGUFM, 2012, B53F-0755.
  • Jacobson, M. J., Markauskaite, L., Kelly, N., & Stokes, P. G. (2012). Model-based learning about climate change with productive failure: preliminary findings. In Proceedings of the American Educational Research Association Annual Meeting (AERA 2012) (pp. 1-10). American Educational Research Association.
  • Janssen, M. A. & Rollins, N. (2012). Predicting behavior in new behavioral experiments: Outcomes of a modeling competition. Journal of Artificial Societies and Social Simulation, 15(3). doi.org/10.18564/jasss.2015
  • Jiang, H., Karwowski, W., & Ahram, T. Z. (2012). Applications of agent-based simulation for human socio-cultural behavior modeling. Work, 41(Supplement 1), 2274-2278.
  • Jorge, J. P., Kokkinogenis, Z., Rossetti, R. J., & Marques, M. A. (2012). Simulation of an order picking system in a pharmaceutical warehouse. In Conference proceeding of the Fourth International Conference on Advances in System Simulation (pp. 107-112). Lisbon: Portugal.
  • Kahn, K., Noble, H., Hjorth, A., & Sampaio, F.F. (2012). Three-minute Constructionist Experiences. Paper presented at the Proceedings of Constructionism 2012, Athens, Greece
  • Karamanos, K., Gkiolmas, A., Chalkidis, A., Skordoulis, C., Papaconstantinou, M., & Stavrou, D. (2012). Ecosystem food-webs as dynamic systems: Educating undergraduate teachers in conceptualizing aspects of food-webs’ systemic nature and comportment. Advances in Systems Science and Application 12 (4): 49-68 [PDF]
  • Khan, S. (2012). A Hidden GEM. The Science Teacher, 79(8), 59.
  • Khatua, A., & Chaki, N. (2012, September). Sustainable peer-based structure for content delivery networks. In Proceedings of the CUBE International Information Technology Conference (pp. 403-408).
  • Kimbrough, S. (2012). Agents, Games, and Evolution. CRC Press, Boca Raton, Florida.
  • Klopfer, E., & Purushotma, R. (2012). Using Simulations as a Starting Point for Constructing Meaningful Learning Games. Computer Games and New Media Cultures, 603–617. http://dx.doi.org/10.1007/978-94-007-2777-9_38
  • Kochanski, Tim. (2012). Toward Teaching Markets as Complex Systems: A Web Based Simulation Assignment Implemented in Netlogo. International Review of Economics Education, 11 (2), 102-114.[HTML]
  • Kocyigit, P. (2012). Agent Based Optical Character Recognition (Doctoral dissertation, MSc. thesis, Dept. CS. Bangor University, Bangor).
  • Koohborfardhaghighi, S., & Kim, J. (2012, March). Improving Recommendation Flow with Centrality Measure in an Evolving Social Network. In 2012 26th International Conference on Advanced Information Networking and Applications Workshops (pp. 1264-1269). IEEE.
  • Kponyo, J. J., Kuang, Y., & Li, Z. (2012, October). Real time status collection and dynamic vehicular traffic control using ant colony optimization. In 2012 international conference on computational problem-solving (ICCP) (pp. 69-72). IEEE.
  • Kuo, W. L., He, Y. Y., & Chang, C. K. (2012, March). Development of a Simulation Learning Environment for Inquiry-based Learning: An Example of Stray Dogs Problem in Taiwan. In 2012 IEEE Fourth International Conference On Digital Game And Intelligent Toy Enhanced Learning (pp. 182-186). IEEE.
  • Kwiatkowska, J., & Gutowska, J. (2012). Memetics on the Facebook. Polish Journal of Management Studies, 5, 289-298.
  • Larrosa, Juan MC. (2012). Algoritmos evolutivos en juegos de formación de red. Una aplicación en NetLogo. [Evolutive Algorithms in Network Formation Games: A NetLogo Application]. Editorial Académica Española: Saarbrucker (Germany), 168 p. (ISBN-13: 978-3-659-05434-1)
  • Larrosa, Juan MC. (2012). Introducción a la economía computacional basada en agentes con aplicaciones NetLogo. [Introduction to Agent-Based Computational Economics with NetLogo Applications]. EdiUNS: Bahía Blanca (Argentina). 216 p. (ISBN: 978-987-1907-15-1)
  • Laver, M. & Sergenti, E. (2012). Party Competition: An Agent-Based Model. Princeton University Press.
  • Le Page, C., Becu, N., Bommel, P., & Bousquet, F. (2012). Participatory Agent-Based Simulation for Renewable Resource Management: The Role of the Cormas Simulation Platform to Nurture a Community of Practice. Journal of Artificial Societies and Social Simulation (JASSS), 15 (1), 10.
  • Levin, J. & Datnow, A. (2012). "The Principal Role in Data Driven Decision Making: Using Case Study Data to Develop Multi-Mediator Models of Educational Reform." School Effectiveness and School Improvement. 23 (2), 179-201 [PDF]
  • Levin, J., & Datnow, A. (2012). Multiple mediator models of educational reform: Organizational learning as persistent change. Paper presented at the 2012 American Educational Research Association meetings. Vancouver Canada.
  • Leykum, L., Kumar, P., Parchman, M., McDaniel, R. R., Lanham, H., & Agar, M. (2012). Use of an agent-based model to understand clinical systems. Journal of Artificial Societies and Social Simulation, 15(3). doi.org/10.18564/jasss.1905
  • Li, K., Gao, H. W., Wang, G. R., Chen, C. R., Hu, P., & Wang, K. (2012, November). Strategic Interaction in k-neighborhood on the Network Formation. In 2012 Third Global Congress on Intelligent Systems (pp. 369-372). IEEE.
  • Lin, Y., Berger, U., Grimm, V., Ji, Q-R. (2012). Differences between symmetric and asymmetric facilitation matter: exploring the interplay between modes of positive and negative plant interactions. Journal of Ecology. DOI: 10.1111/j.1365-2745.2012.02019.x
  • Liu, C., Sibly, R.M., Grimm, V. & Thorbek, P. (2012). Linking pesticide exposure and spatial dynamics: an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes. Ecological Modelling.
  • Lytinen, S. L. and S. F. Railsback. (2012). "The Evolution of Agent-based Simulation Platforms: A Review of NetLogo 5.0 and ReLogo." To appear in Proceedings of the Fourth International Symposium on Agent-Based Modeling and Simulation, at the 21st European Meeting on Cybernetics and Systems Research (EMCSR 2012), Vienna, Austria, April 2012.
  • Ma, L., Chu, X. L., Huang, X., Xiong, R., Li, H., & Lin, Q. (2013, December). Simulation research on diffusion of agricultural science and technology for peasant. In 2013 International Conference on Information Science and Cloud Computing Companion (pp. 97-102). IEEE.
  • Machálek, T., Olševičová, K., & Cimler, R. (2012). Modelling population dynamics for archaeological simulations. Mathematical Methods in Economy, 536-539.
  • Maharaj, S., Kleczkowski, A. (2012). Controlling Epidemic Spread by Social Distancing: Do it well or not at all. BMC Public Health 12:679 [HTML]
  • Martin, B.T., Jager, T., Preuss, T., Nisbet, R. & Grimm, V. (2012). Predicting population dynamics from the properties of individuals: a test of Dynamic Energy Budget theory. American Naturalist, 18(4), 506-519.
  • Martin, B.T., Zimmer, E.I., Grimm, V. & Jager, T. (2012). Dynamic Energy Budget theory meets individual-based modelling: a generic and accessible implementation. Methods in Ecology and Evolution, 3, 445-449.
  • Metz, S. (2012). Systems thinking. The Science Teacher, 79(7), 6.
  • Mialhe, F., Becu, N., & Gunnell, Y. (2012). An agent-based model for analyzing land use dynamics in response to farmer behaviour and environmental change in the Pampanga delta (Philippines). Agriculture, Ecosystems & Environment, 161, 55-69. [HTML]
  • Miller, A., Rosenbaum, C., & Blikstein, P. (2012, February). MagneTracks: a tangible constructionist toolkit for Newtonian physics. In Proceedings of the Sixth International Conference on Tangible, Embedded and Embodied Interaction (pp. 253-256).
  • Montes, G. (2012). Using Artifical Societies to Understand the Impact of Teacher Student Match on Academic Performance: The Case of Same Race Effects. Journal of Artificial Societies and Social Simulation, 15(4), 8 [HTML]
  • Morariu, C., Morariu, O., & Borangiu, T. (2012, May). Modeling and simulation for service-oriented agent based manufacturing systems. In Proceedings of 2012 IEEE International Conference on Automation, Quality and Testing, Robotics (pp. 44-49). IEEE.
  • Morariu, C., Morariu, O., & Borangiu, T. (2012). Customer Order Management in Service Oriented Holonic Manufacturing. IFAC Proceedings Volumes, 45(6), 697-703.
  • Moreno Arboleda, F. J., Duitama Muñoz, J. F., & Camilo Ospina, E. (2012). A method for estimating the position and direction of a leader of a set of moving objects. Revista Facultad de Ingeniería Universidad de Antioquia, (62), 11-20.
  • Mudrak, G., & Semwal, S. K. (2012, May). AgentCity-An agent-based modeling approach to city planning and population dynamics. In 2012 International Conference on Collaboration Technologies and Systems (CTS) (pp. 91-96). IEEE.
  • Muldoon, R., Smith, T., & Weisberg, M. (2012). Segregation That No One Seeks. Philosophy of Science, 79, 38–62.
  • MUSCALAGIU, I., IORDAN, A., OSACI, M., & PANOIU, M. (2012). Extending the modeling of the protein folding problem in DisCSP-Netlogo using triangular lattice models.
  • Muscalagiu, I., Iordan, A., Osaci, M., & Panoiu, M. (2012). Modeling and simulation of the protein folding problem in DisCSP-Netlogo. AWERProcedia Information Technology and Computer, 2.
  • Newstead, A., & Jacobson, M. J. (2012). Collaborative Virtual Worlds for Enhanced Scientific Understanding.
  • Niazi, M. A. & Hussain, A. (2012). Cognitive agent-based computing-I: A unified framework for modeling complex adaptive systems using agent-based & complex network-based methods. Springer-Verlag.
  • OTČENÁŠKOVÁ, T., BUREŠ, V., & ČECH, P. Decision Support during Biological Incident Management: the Employment of Multi-agent Simulations.
  • Parunak, H. V. D. (2012). Review of Agent-Based and Individual-Based Modeling: A Practical Introduction.
  • Patel, A., Crooks, A., & Koizumi, N. (2012). Slumulation: An Agent-Based Modeling Approach to Slum Formulations. Journal of Artificial Societies and Social Simulation (JASSS), 15 (4), 2. [HTML] (Oct 2012)
  • Petreska, I., Kefalas, P., & Gheorghe, M. (2012). Tools for simulating spatial mas. In Proceedings of the 7th Annual SEERC Doctoral Student Conference, DSC.
  • Pinacho, P., Pau, I., Chacón, M., & Sánchez, S. (2012). “An ecological approach to anomaly detection: the EIA model” in Artificial Immune Systems: Lecture Notes in Computer Science, 7597, 232-245. [HTML]
  • Prachai, S. (2012). The Design of Diabetes Simulation System using Multi-Agent. Procedia-Social and Behavioral Sciences, 40, 146-151.
  • Pu, L. (2012). Application of participatory simulation in experimental teaching of Economics and Management major [J]. Experimental Technology and Management, 11.
  • Purnomo H, Suyamto D, Irawati RH. (2012). Harnessing the climate commons: an agent-based modelling approach to making reducing emission from deforestation and degradation (REDD)+work. Mitigation and Adaptation Strategies for Global Change.
  • Qian, L., & Song, X. (2012, October). Command and Control Evolutive Network Models for Command Substitution. In Asian Simulation Conference (pp. 63-70). Springer, Berlin, Heidelberg.
  • Quadros, C., da Silva, F. N., Silveira, J., Rodrigues, L. M., Antiqueira, L. S., Briao, S. L., ... & Cabreira, T. M. (2012, October). A study on socio-spatial segregation models based on multi-agent systems. In 2012 Third Brazilian Workshop on Social Simulation (pp. 31-38). IEEE.
  • Ramírez, R., Harris, P.D., Bakke, T.A. (2012). An agent-based modelling approach to estimate error in gyrodactylid population growth. International Journal for Parasitology, 2(9), 809-17. doi: 10.1016/j.ijpara.2012.05.012 [PDF]
  • Rhee, J. M. & Iannaccone, P. M. (2012). Mapping mouse hemangioblast maturation from headfold stages. Developmental Biology, 365 (1), 1-13. doi:10.1016/j.ydbio.2012.02.023 [PDF]
  • Rodriguez, I. (2012). Land as a Renewable Resource: Integrating Climate, Energy, and Profitability Goals using an Agent-Based NetLogo Model.
  • Ross, J. (2012). A comparative study of simulation software for modeling stability operations. In the Proceedings of the 2012 Symposium on Military Modeling and Simulation. [PDF]
  • Rossmann, B., Peterson, T., & Drake, J. (2012). A Simulation Model of Fire Ant Competition with Cave Crickets at Fort Hood, Texas. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 43-62). Springer, Boston, MA.
  • Rusinko, J., & Swan, H. (2012). Agent-based fabric modeling using differential equations. CODEE Journal, 9(1), 7.
  • Safia, A., & Mala, T. (2012, December). Ascertaining the More Knowledgeable Other among peers in collaborative e-learning environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-7). IEEE.
  • Sakellariou, I. (2012, July). Agent based Modelling and Simulation using State Machines. In SIMULTECH (pp. 270-279).
  • Sakellariou, I. (2012). Turtles as state machines-agent programming in netlogo using state machines. In ICAART 2012-Proceedings of the 4th International Conference on Agents and Artificial Intelligence, Volume 2-Agents, Vilamoura.
  • Sarah, B., Soumia, L., & Merouani, H. F. (2012). Segmentation of images based cellular automata-reactive agent implemented in netlogo platform. International Journal of Computer Applications, 975, 8887.
  • Schindler, J. (2012). Rethinking the Tragedy of the Commons: The Integration of Socio-Psychological Dispositions. Journal of Artificial Societies and Social Simulation (JASSS), 15 (1): 4.
  • Schwarz, N., Kahlenberg, D., Haase, D., & Seppelt, R. (2012). ABMland - A tool for agent-based model development on urban land use change. Journal of Artificial Societies and Social Simulation, 15(2). doi.org/10.18564/jasss.1875
  • Shen, Z., Yao, X. A., Kawakami, M., Chen, P., & Koujin, M. (2012). Integration of MAS and GIS using Netlogo. In Geospatial Techniques in Urban Planning (pp. 369-388). Springer, Berlin, Heidelberg.
  • Smojver, S. (2012). Analysis of banking supervision via inspection Game and agent-based modeling. In the Proceedings of the 2012 Central European Conference on Information and Intelligent Systems, Varaždin, Croatia, Sep 19 - 21, 2012: 355-362 [PDF]
  • Sokolov, O., & Tovstik, A. (2012). Cooperative Behavior Modeling of Intelligent Agents in NetLogo Environment using Fuzzy Logic. In Proceedings of East West Fuzzy Colloquium (Vol. 19, p. 147).
  • Stamatopoulou, I., Sakellariou, I., & Kefalas, P. (2012, November). Formal agent-based modelling and simulation of crowd behaviour in emergency evacuation plans. In 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (Vol. 1, pp. 1133-1138). IEEE.
  • Stigberg, D. (2012). An introduction to the Netlogo modeling environment. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 27-41). Springer, Boston, MA.
  • Svihla, V., & Linn, M. C. (2012). A design-based approach to fostering understanding of global climate change. International Journal of Science Education, 34(5), 651-676.
  • Taboada, M., Cabrera, E., Luque, E., Epelde, F., & Iglesias, M. L. (2012, December). A decision support system for hospital emergency departments designed using agent-based techniques. In Proceedings of the winter simulation conference (pp. 1-2).
  • Taveter, K., Du, H., & Huhns, M. N. (2012, September). Method for rapid prototyping of societal information systems. In 2012 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 1221-1228). IEEE.
  • Tempel, M. (2012). Logo: A language for all ages. Comput. Sci. K–8 Build. a Strong Found, 16-17.
  • Thiele, J. C., Kurth, W. & Grimm, V. (2012). RNetLogo: an R package for running and exploring individual-based models implemented in NetLogo. Methods in Ecology and Evolution, 3, 480-483.
  • Thiele, J., Kurth, W. & Grimm, V. (2012). Agent-based modelling: Tools for linking NetLogo and R. Journal of Artificial Societies and Social Simulation (JASSS), 15 (3): 8. [HTML]
  • Tuberville, T. D., Andrews, K. M., Westervelt, J. D., Balbach, H. E., Macey, J., & Carlile, L. (2012). Using demographic sensitivity testing to guide management of gopher tortoises at Fort Stewart, Georgia: A comparison of individual-based modeling and population viability analysis approaches. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 109-131). Springer, Boston, MA.
  • Van Berkel, S., Turi, D., Pruteanu, A., & Dulman, S. (2012, July). Automatic discovery of algorithms for multi-agent systems. In Proceedings of the 14th annual conference companion on Genetic and evolutionary computation (pp. 337-344).
  • Vázquez-Vélez, E. (2012). Agent-based Models of Nurse Behavior to Evaluate the Medication Administration Process.
  • Viale, R. (2012). Methodological Cognitivism: Vol 1: Mind, Rationality, and Society. Springer-Verlag.
  • Villegas-Febres, J. C., & Rousse-Romero, R. (2012). Conformation of polyelectrolytes using cellular automata techniques. Journal of Computational Methods in Sciences and Engineering, 12(4-6), 283-292.
  • Vinatier, F., Gosme, M., & Valantin-Morison, M. (2012). A tool for testing integrated pest management strategies on a tritophic system involving pollen beetle, its parasitoid and oilseed rape at the landscape scale. Landscape Ecology, 27 (10): 1421-1433. [PDF]
  • Vinatier, F., Lescourret, F., Duyck, P.-F., & Tixier, P. (2012). From IBM to IPM: Using Individual-Based Models to design the spatial arrangement of traps and crops in Integrated Pest Management strategies. Agriculture, Ecosysems and Environment, 146 (1): 52-59. [HTML]
  • Wagh, A. & Wilensky, U. (2012). Evolution in blocks: Building models of evolution using blocks. Proceedings of Constructionism, Athens, Greece, Aug 21-25. [HTML]
  • Wagh, A. & Wilensky, U. (2012). Mechanistic Explanations of evolutionary change facilitated by agent-based models. Paper presented at AERA, Vancouver, April 13-17.
  • Wagh. A. & Wilensky, U. (2012). Breeding birds to learn about artificial selection: Two birds with one stone? Proceedings of ICLS, Sydney, Australia, July 2-6.
  • Walker, L., & Barros, J. (2012). An agent-based population model for Wolverhampton, UK: a spatio-temporal activity based approach to population modelling. GISRUK, Lancaster.
  • Wan, S., Cao, Q., & Wang, D. (2012, December). Aoc based vehicular Ad-hoc network modeling research. In Proceedings of 2012 2nd International Conference on Computer Science and Network Technology (pp. 964-967). IEEE.
  • Wang, Y.S., Ellwood, F., Maestre, F.T., Yang, Z.Y., Wang, G. & Chu C.J. (2012). Positive interactions can produce species-rich communities and increase the species temporal turnover. Journal of Plant Ecology, 5:417-421. [PDF]
  • Wen-ze, N. I. N. G., & Wei-xing, J. I. N. G. (2012). The impact of guidance price fuzziness on construction production earning power [J]. Journal of Xi'an University of Architecture & Technology (Natural Science Edition), 1.
  • Westervelt, J. & Cohen, G. (2012). Ecologist-Developed Spatially-Explicit Dynamic Landscape Models. Springer.
  • Westervelt, J. D., & Cohen, G. L. (2012). Never fear: You already model!. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 1-6). Springer, Boston, MA.
  • Westervelt, J. D., & Hannon, B. (2012). A Collaborative Process for Multidisciplinary Group Modeling Projects. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 7-25). Springer, Boston, MA.
  • Westervelt, J. D., & MacAllister, B. (2012). Forecasting gopher tortoise (Gopherus polyphemus) distribution and long-term viability at Fort Benning, Georgia. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 85-107). Springer, Boston, MA.
  • Whitehouse, H., Kahn, K., Hochberg, M.E., & Bryson, J.J. (2012). The role for simulations in theory construction for the social sciences: case studies concerning Divergent Modes of Religiosity. Religion, Brain & Behavior, 2(3).
  • Wong, R., & Sheng, S. Y. (2012). A business application of the system dynamics approach: Word-of-mouth and its effect in an online environment. Technology Innovation Management Review, 2(6).
  • Worsley, M. (2012, October). Multimodal learning analytics: enabling the future of learning through multimodal data analysis and interfaces. In Proceedings of the 14th ACM international conference on Multimodal interaction (pp. 353-356).
  • Worsley, M., & Blikstein, P. (2012). An Eye For Detail: Techniques for using eye tracker data to explore learning in computer-mediated environments.
  • Worth, D. J., Chin, L. S., & Greenough, C. (2012). FLAME tutorial examples: a simple SIR infection model. STFC.
  • Xianjun, C. M. Q. (2012). Simulation Study on Information Resources Allocationin Network Information Ecosystem [J]. Journal of Intelligence, 5.
  • Xiaobo, L. (2012). Netlogo Platform-based Realization of Dynamic Model of Public Opinion. Information and Documentation Services, 33(1), 55-60.
  • Xike, S., & Fengxian, L. (2012, December). An agent-based simulation of farmer's behaviors adapting to ecological policies using netlogo. In 2012 Fourth International Symposium on Information Science and Engineering (pp. 426-429). IEEE.
  • Xu, J., Liu, L., & Shao, Z. (2012, August). Research on the evolution of enterprise clusters based on theory of MAS. In 2012 International Symposium on Information Technologies in Medicine and Education (Vol. 2, pp. 1094-1098). IEEE.
  • Yalvac, B., Ayar, M. C., & Soylu, F. (2012). Teaching Engineering with Wikis. International Journal of Engineering Education, 28(3), 701. [PDF]
  • Yanli, Z. D. Y. X. C. (2012). Modeling and Simulation of Knowledge Transferring Process in Innovation Network Based on Multi-agent [J]. Chinese Journal of Management, 12.
  • Yu, S. Y. (2012). GIS and agent-based modeling of emergency evacuation. Journal of the Korean Society of Hazard Mitigation, 12(1), 127-132.
  • Zhang, D., Sun, Y., & Zhong, Z. (2012, August). Simulation of the Incentive Mechanism for Knowledge Sharing between Employees Based on the Organization Design. In 2012 Fifth International Conference on Business Intelligence and Financial Engineering (pp. 89-92). IEEE.
  • Zhang, F., More, T. K., & Zhang, X. (2012). Simulation of Wealth Distribution. In Recent Advances in Computer Science and Information Engineering (pp. 175-179). Springer, Berlin, Heidelberg.
  • Zheng, M. (2012). Based on NetLogo Simulation for Credit Risk Management. In Advances in Computer Science and Engineering (pp. 395-401). Springer, Berlin, Heidelberg. [PDF]
  • Zhou, Y. X., & Wang, X. (2012). Overview of Computer Simulation Modeling Platforms Based on Multi-Agent. In Advanced Materials Research (Vol. 472, pp. 3111-3116). Trans Tech Publications Ltd.
  • Zhou, Y., Wang, L., & Chen, Q. (2012, May). Intervention of flocking behavior based on collision avoidance. In 2012 24th Chinese Control and Decision Conference (CCDC) (pp. 591-596). IEEE.

2011

  • Stonedahl, F., Wilkerson-Jerde, M. & Wilensky, U. (2011). MAgICS: Toward a Multi-Agent Introduction to Computer Science. In M.Beer, M.Fasli, and D. Richards (Eds.) Multi-Agent Systems for Education and Interactive Entertainment: Design, Use and Experience. IGI Global. pp.1-25.[PDF]
  • Ahrweiler, P., Pyka, A., & Gilbert, N. (2011). An implementation of the pathway analysis through habitat (PATH) algorithm using NetLogo. The Journal of Product Innovation Management, 28(2).
  • Al-Dmour, N. A. A. H. (2011). TarffSim: Multiagent traffic simulation. European Journal of Scientific Research, 53(4), 570-575.
  • Alfaro, J. F. & Miller, S. A. (2011). Planning the development of electricity grids in developing countries: An initial approach using Agent Based Models. Proceedings of 2011 IEEE International Symposium on Sustainable Systems and Technology (ISSST), May 16-18, 2011, Chicago, IL, 1-6. [PDF]
  • Allen, T. T. (2011). Agents and new directions. In Introduction to Discrete Event Simulation and Agent-Based Modeling (pp. 175-190). Springer, London.
  • Armendáriz, M., Burguillo, J. C., Peleteiro-Ramallo, A., Arnould, G., & Khadraoui, D. (2011, June). Carpooling: A Multi-Agent Simulation In Netlogo. In ECMS (pp. 61-67).
  • Arnould, G., Khadraoui, D., Armendariz, M., Burguillo, J. C., & Peleteiro, A. (2011). A transport based clearing system for dynamic carpooling business services. Proceedings of 2011 11th International Conference on ITS Telecommunications (ITST), 23-25 Aug. 2011, pp. 527-533. [PDF]
  • Asman, B. C., Kim, M. H., Moschitto, R. A., Stauffer, J. C., & Huddleston, S. H. (2011). Methodology for analyzing the compromise of a deployed tactical network. Proceedings of the 2011 Systems and Information Engineering Design Symposium (SIEDS), April 29, 2011, pp. 164-169. [PDF]
  • Balasubramanian, G. P. (2011). An agent based simulation of Human blood coagulation system. Student Papers Complex Adaptive Systems Fall 2011, 27.
  • Ballinas-Hernandez, A.L., Munoz Melendez, A., & Rangel-Huerta, A. (2011). Multiagent System Applied to the Modeling and Simulation of Pedestrian Traffic in Counterflow. Journal of Artificial Societies and Social Simulation (JASSS), 14 (3) 2. [HTML] (June 2011)
  • Balzer, W. & Manhart, K. (2011). A social process in science and its content in a simulation program. Journal of Artificial Societies and Social Simulation, 14(4). doi.org/10.18564/jasss.1836
  • Banirostam, T., & Fesharaki, M. N. (2011, March). Effective parameters in convergence of autonomous distributed systems using with immune system approch. In 2011 Tenth International Symposium on Autonomous Decentralized Systems (pp. 204-208). IEEE.
  • Banirostam, T., & Fesharaki, M. N. (2011, May). Modeling and Simulation of Influenza with Biological Agent: A New Approch for Increasing System Robustness. In 2011 Fifth Asia Modelling Symposium (pp. 13-17). IEEE.
  • Banirostam, T., & Fesharaki, M. N. (2011, November). Effect of Learning and Database in Robustness of Security Tools: Based on Immune System Modeling. In 2011 UKSim 5th European Symposium on Computer Modeling and Simulation (pp. 47-52). IEEE.
  • Baoyan, Z. L. S. S. G. (2011). System Design of Behavior Management Based on Multi-Agent Modeling [J]. Chinese Journal of Management, 9.
  • Baracaldo, N., Lopez, C., Anwar, M., Lewis, M. (2011)"Simulating the Effect of Privacy Concerns in Online Social Networks." Proceedings of the IEEE International Conference on Information Reuse and Integration, IRI 2011, 3-5 August 2011, Las Vegas, Nevada, USA 2011. [PDF]
  • Barbosa, J. & Leitao, P. (2011). "Simulation of Multi-agent Manufacturing Systems using Agent-based Modelling Platforms." Proceedings of the 2011 9th IEEE International Conference on Industrial Informatics (INDIN), 26-29 July 2011, pp. 477-482. [PDF]
  • Basu, S., & Biswas, G. (2011). Multiple representations to support learning of complex ecological processes in simulation environments. In Proceedings of the 19th International Conference on Computers in Education. Chiang Mai, Thailand.
  • Beer, M. D., Fasli, M., & Richards, D. (2011). Multi-agent systems for education and interactive entertainment: design, use and experience. Information Science Reference.
  • Bernardes, S., Eury, A. H., Presotto, A., Madden, M., Jordan, T., Fragaszy, D. M., ... & Tavares-Rocha, Y. (2011). An agent based modeling approach for representing capuchin (sapajus spp.) behavior in brazil. In Proceedings Of The Annual Conference-American Society For Photogrammetry AND Remote Sensing (pp. 41-47).
  • Bida, M., Brom, C., & Popelova, M. (2011)."To date or not to date? A Minimalist affect-modulated control architecture for dating virtual characters." Lecture Notes in Computer Science, 6895(2011): 419-425. [PDF]
  • Blachford, A. M. (2011). Five Studies in Life History Evolution. [Doctoral Thesis, University of British Columbia]. Available at UBC Theses and Dissertations. http://hdl.handle.net/2429/33953
  • Blikstein, P. (2011, February). Using learning analytics to assess students' behavior in open-ended programming tasks. In Proceedings of the 1st international conference on learning analytics and knowledge (pp. 110-116).
  • Bollinger, L. A. (2011). Evolving climate-resilient energy infrastructures. TU Delft.
  • Braga, D. S., Alves, F. O. M., Lima Neto, F. B., & Menezes, L. C. S. (2011). AspectNetLogo: Uma Proposta de Linguaguem Orientada a Aspectos para a Modelagem de Sistemas Multi-Agentes em Simulacoes Sociais. In: X Congresso Brasileiro de Inteligencia Computacional, 2011. Fortaleza, CE. Sessao Tecnica 28 (Interfaces e Ferramentas).
  • Braun, A. & Rosner, H.-J. (2011). Disturbance and succession. Potential of agent-based systems for modeling vulnerable ecosystems. Application to land degradation processes. In Car, A. / Griesebner, G. / Strobl, J. (Eds.) Geospatial Crossroads @ GI_Forum '11. Proceedings of the Geoinformatics Forum Salzburg, pp. 12-21.
  • Briola, D., & Mascardi, V. (2011). Design and Implementation of a NetLogo Interface for the Stand-Alone FYPA System. In WOA (pp. 41-50).
  • Brown, B. N., Price, I. M., Toapanta, F. R., DeAlmeida, D. R., Wiley, C. A., Ross, T. M., ... & Vodovotz, Y. (2011). An agent-based model of inflammation and fibrosis following particulate exposure in the lung. Mathematical biosciences, 231(2), 186-196.
  • Bryson, J. J. (2011). Coursework 2: Understanding Lions or Map Learning.
  • Buchtová, M., Brom, C., & Šisler, V. (2011). EDUCATIONAL GAMES AND SIMULATIONS AT SCHOOL: THE HIGH-SCHOOL STUDENTS’EXPERIENCES AND ATTITUDES. A QUALITATIVE STUDY. 7th DisCo Conferen New Media and E.
  • Cain, N., Nelson, H., Abdollahian, M., Close, B., & Hoffman, J. (2011). Not on Planet Earth (Nope): An Agent Based Model Simulating Energy Infrastructure Siting Dynamics. In APSA 2011 Annual Meeting Paper.
  • Casilli, A. A., & Tubaro, P. (2011). Why net censorship in times of political unrest results in more violent uprisings: A social simulation experiment on the UK riots. Available at SSRN 1909467.
  • Challenge, S. C., Ramirez, I., Chao, L., Miller, G., & Edington, C. (2011). Water & Ice.
  • Chang, C. K. (2011, August). Integrate social simulation content with game designing curriculum to foster computational thinking. In The 7th International Conference on Digital Content, Multimedia Technology and its Applications (pp. 115-118). IEEE.
  • Chang, C. K., & Biswas, G. (2011, June). Design engaging environment to foster computational thinking. In EdMedia+ Innovate Learning (pp. 2898-2902). Association for the Advancement of Computing in Education (AACE).
  • Chen-dong, Y. L. D. (2011). The Research of Intersection Traffic Control Based on Agent [J]. Microcomputer Information, 9.
  • Chen, W., Ward, K., Li, Q., Kecman, V., Najarian, K., & Menke, N. (2011, August). Agent based modeling of blood coagulation system: implementation using a GPU based high speed framework. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 145-148). IEEE.
  • Chen, X. (2011, July). Agent-Based modeling of emergency evacuation in a spatially-aware and time-aware environment. In Proceedings of the 25th International Cartographic Conference (pp. 3-8).
  • Cheng, G. J., Yan, Y. J., Qiang, X. J., & Li, Z. (2011). Modeling and simulation of the ecosystem based on multi-agent system. Xi'an Shiyou Daxue Xuebao(Ziran Kexue Ban)- Journal of Xi'an Shiyou University(Natural Science Edition), 26(2), 99-103.
  • Collard, P., Mesmoudi, S. (2011)."How to Prevent Intolerant Agents from High Segregation?" Advances in Artificial Life, ECAL 2011: Proceedins of the Eleventh European Conference on the Synthesis and Simulation of Living Systems, T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo and R Doursat. MIT Press, (ISBN 978-0-262-29714-1). 2011. [PDF]
  • Contractor, N. (2011). Social Network Analysis. Cell, 617, 803-6971.
  • D'Alessandro, S. (2011). If we build it will they come? Or is this only if the price right? Adoption and use of 4G versus 3G technology: results from a Netlogo simulation. In Australian and New Zealand Marketing Academy Conference (2011) (p. 4). ANZMAC2011 Conference.
  • Damaceanu, R-C. (2011). Agent-based Computational Social Sciences using NetLogo. [LAP LAMBERT Academic Publishing.]
  • Damaceanu, R-C. (2011). An Agent-based Computational Study of Wealth Distribution in Function of Technological Progress Using NetLogo. [American Journal of Economics, Vol 1.1, pg. 15-20.]
  • Dauschan, A. A. M. (2011). Implementation of Virtual Embryology using the Thrust library for CUDA.
  • Dave, S., Sooriyabandara, M., & Yearworth, M. (2011). A systems approach to the smart grid. Energy, 130-134.
  • de Bakker, G., van Bruggen, J. Jochems, W., & Sloep, P. B. (2011). Introducing the SAPS System and a corresponding allocation mechanism for synchronous online reciprocal peer support activities. Journal of Artificial Societies and Social Simulation (JASSS), 14 (1). [PDF]
  • Dermody, J.; Tanner, C. & Jackson, A. (2011). The Evolutionary Pathway to Obligate Scavenging in Gyps Vultures PLoS ONE 6(9): e24635.
  • Dickerson, M. (2011). "Multi-agent simulation and NetLogo in the introductory computer science curriculum." Journal of Computing Sciences in Colleges, 27(1). [PDF]
  • Dilāns, G. (2011). RECONSTRUCTING SIMULATION: ACCESS TO HIGH-SCHOOL SCIENCE DISCOURSE THROUGH RESPONSE-FOCUSED PARTICIPATORY DIALOGUE1. ECONOMICS AND CULTURE, 317.
  • Dixon, D. S. (2011). "Preliminary results from as agent-based adaptation of friendship games." 86th Annual Conference of Western Economics. [PDF]
  • Dixon, D. S. (2011). An Agent-Based Adaptation of Friendship Games: Observations on Network Topologies. In Computational Social Sciences Society of the Americas: Second Annual Conference of the Computational Social Sciences Society of the Americas.
  • Doloswala, N., Winzar, H., Song, E., & Nakandala, D. J. (2011). The discovery of liars. In Australian and New Zealand Marketing Academy Conference (2011) (p. 4). ANZMAC2011 Conference.
  • Drucker, N., & Campbell, K. (2011). Examining the Relationships Between Education, Social Networks and Democratic Support With ABM.
  • Elementary, A., Ionkov, A., & Poston, D. (2011). Does Queen+ King= Checkmate?.
  • Fekir, A. & Benamrane, N. (2011). "Segmentation of medical image sequence by parallel active contour." Advances in Experimental Medicine and Biology, 696(6): 515-522. [PDF]
  • Ferreira, J. C., da Silva, A. R., & Afonso, J. L. (2011). Agent Based Approaches for Smart Charging Strategy for Electric Vehicles (No. 2011-39-7214). SAE Technical Paper.
  • Ferreira, J. C., Monteiro, V., Afonso, J. L., & Silva, A. (2011, June). Smart electric vehicle charging system. In 2011 IEEE Intelligent Vehicles Symposium (IV) (pp. 758-763). IEEE.
  • Ferreira, J. F., Mendes, A., Cunha, A., Baquero, C., Silva, P., Barbosa, L. S., & Oliveira, J. N. (2011, June). Logic training through algorithmic problem solving. In International Congress on Tools for Teaching Logic (pp. 62-69). Springer, Berlin, Heidelberg.
  • Filho, H. S. B., de Lima Neto, F. B., & Fusco, W. (2011). "Migration and Social Networks - An Explanatory Multi-evolutionary Agent-Based Model." Proceedings of the 2011 IEEE Symposium on Intelligent Agent (IA), 11-15 April 2011, pp. 1-7. [PDF]
  • Fong, M. (2011). Creating an Agent-based Model to Examine Spatial Behavior of Eriocheir Sinensis.
  • Frank, B.M.; Piccolo, J.J. & Baret, P.V. (2011). "A review of ecological models for brown trout: towards a new demogenetic model." Ecology of Freshwater Fish 20(2) 167-198.
  • Fuks, K., & Kawa, A. (2011, June). Virtual Organization Networking Strategies–Simulations Experiments. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 602-609). Springer, Berlin, Heidelberg.
  • Gabbreillini, S. (2011). Simulare meccanismi sociali con NetLogo: Una introduzione. Methodology and Techniques of Social Research. E-book [HTML].
  • Gabrielsen, P. J., Wilson, J. L., & Pullin, M. (2011). Agent-based modeling of hyporheic dissolved organic carbon transport and transformation. AGUFM, 2011, H51B-1208.
  • Gobert, J., O'Dwyer, L., Horwitz, P., Buckley, B., Levy, S.T., & Wilensky, U. (2011). Examining the relationship between students' epistemologies of models and conceptual learning in three science domains: Biology, Physics, & Chemistry. International Journal of Science Education, 33(5), 653-684.
  • Gong, C., Li, L., Zhu, K., & Gao, Y. (2011). Evolutionary Model of Coal Mine Water Hazards Based on Multi-Agent. Systems Engineering Procedia, 2, 358-365.
  • Guerram, T., & Dehimi, N. E. H. (2011). Multi agent Simulation: A Unified Framework for the analysis of viral infections within a bovine population. International Journal of Computer Science Issues (IJCSI), 8(5), 170.
  • Gui-sheng, Y., Ji-jie, W., Hong-bin, D., & Jia, L. (2011)"Intelligent Viral Marketing algorithm over online social network." Proceedings of 2011 Second International Converence on Networking and Distributed Computing (ICNDC), 21-24 Sept. 2011, pp. 319-323. [PDF]
  • Guoxiang, R. U. A. N., & Jing, R. P. S. (2011). The Evolutionary Game Simulation on Knowledge Sharing Among Innovation Network Members. Journal of Intelligence, 30(2), 100-104.
  • Haack, J. N., Fink, G. A., Maiden, W. M., McKinnon, A. D., Templeton, S. J., & Fulp, E. W. (2011, April). Ant-based cyber security. In 2011 Eighth International Conference on Information Technology: New Generations (pp. 918-926). IEEE.
  • Hachichi, H., Chelloug, S., & Athmouni, F. (2011, April). A virtual topology for routing in adhoc networks. In 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC) (pp. 1-6). IEEE.
  • Halverson, R., Blakesley, C., & Figueiredo-Brown, R. (2011). Video game design as a model for professional learning. Learning to play, 9-28.
  • HAN, Z. B., LI, Y., PING, Y., KOU, X. D., & WU, J. J. (2011). College Confidential Project Management Modeling Based on Multi-agent System. Microprocessors, (1), 14.
  • Hashem, K., & Mioduser, D. (2011). The contribution of learning by modeling (LbM) to students' understanding of complexity concepts. International Journal of e-Education, e-Business, e-Management and e-Learning, 1(2), 151-157.
  • Hong, X. S. Z. (2011). Analysis of Dynamics of Knowledge Transfer Based on Supernetwork [J]. Journal of Intelligence, 7.
  • Honwad, S., Hmelo-Silver, C., Jordan, R., Sinha, S., Eberbach, C., Goel, A., & Rugaber, S. (2011). Learning about Ecosystems in a Computer Supported Collaborative Learning Environment.
  • Huang, L., Deng, S., Li, Y., Wu, J., & Yin, J. (2011, July). Data-dependency aware trust evaluation for service choreography. In 2011 IEEE International Conference on Web Services (pp. 708-709). IEEE.
  • Huang, S., & Yu, S. (2011). Model and simulation for global supply chain enterprises to migrate to port cluster. Journal of Tongji University. Natural Science, 39(9), 1401-1406.
  • Hussain, T., Al-Mutib, K. N., Alghamdi, A. S. (2011)"Towards software engineering process for C4I systems." Proceedings of the 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), 27-29 May 2011, pp. 125-129. [PDF]
  • Inkoom, J. N. (2011). Simulating Spatial Growth Patterns in Developing Countries: an Agent Based Modelling Approach. A Case of Shama in the Western Region of Ghana. AGUFM, 2011, U22B-05.
  • Isaac, A. G. (2011). The ABM template models: A reformulation with reference implementations. Journal of Artificial Societies and Social Simulation, 14(2). doi.org/10.18564/jasss.1749
  • Ismail, A. R. (2011). Immune-inspired self-healing swarm robotic systems (Doctoral dissertation, University of York).
  • Israel, N., & Wolf-Branigin, M. (2011). Nonlinearity in social service evaluation: A primer on agent-based modeling. Social Work Research, 35(1), 20-24.
  • Jacobson, M. J., Kapur, M., So, H. J., & Lee, J. (2011). The ontologies of complexity and learning about complex systems. Instructional Science, 39(5), 763-783.
  • Jamshidnejad, A., & Mahjoob, M. J. (2011, December). Traffic simulation of an urban network system using agent-based modeling. In 2011 IEEE Colloquium on Humanities, Science and Engineering (pp. 300-304). IEEE.
  • Johnson, T. E., & Rosenberg-Kima, R. B.Development of a training effects algorithm for modeling the impact of training in IMPRINT for 21st century Air Force needs.. SpringSim (MMS), 86–91.
  • Joyner, D. A., Goel, A. K., Rugaber, S., Hmelo-Silver, C., & Jordan, R. (2011, July). Evolution of an integrated technology for supporting learning about complex systems. In 2011 IEEE 11th International Conference on Advanced Learning Technologies (pp. 257-259). IEEE.
  • Karlsson, C., Frey, C. L., Zarzhitsky, D. V., Spears, D. F., & Widder, E. A. (2011). Physicomimetics for distributed control of mobile aquatic sensor networks in bioluminescent environments. In Physicomimetics (pp. 145-191). Springer, Berlin, Heidelberg.
  • Kauffmann, P. D. (2011). Traffic Flow on Escalators and Moving Walkways: Quantifying and Modeling Pedestrian Behavior in a Continuously Moving System (Doctoral dissertation, Virginia Tech).
  • Kim, J-W., & Hanneman, R.A. (2011). A Computational Model of Worker Protest. Journal of Artificial Societies and Social Simulation (JASSS), 14 (3) 1. [HTML] (June 2011)
  • Kim, J. Y., Park, H. J., & Jun, Y. C. (2011). The Effects of Educational Contents Authoring in a Project-Based Learning using NetLogo for Pre-service Teachers' Creativity. Journal of Engineering Education Research, 14(4), 29-38.
  • Kottonau, J. (2011). An interactive computer model for improved student understanding of random particle motion and osmosis. Journal of Chemical Education, 88(6), 772-775.[HTML]
  • Kumarappan, S. (2011). "Spatial pricing patterns of cellulosic biomass under oligopsony - A multi-agent simulation model." Paper presented at the 2011 AAEA & NAREA Joint Annual Meeting.
  • Kurve, A. & Kesidis, G. (2011)"Sybil Detection via Distributed Sparse Cut Monitoring." Proceedings of the 2011 IEEE international Conference on Communications (ICC), 5-9 June 2011. [PDF]
  • Kwiatkowska, J. (2011). NetLogo-environment for simulation of logistics problems. Gospodarka Materiałowa i Logistyka, (12), 65-69.
  • Laclavík, M., Dlugolinský, Š., Šeleng, M., Kvassay, M., Schneider, B., Bracker, H., ... & Hluchý, L. (2011, May). Agent-based simulation platform evaluation in the context of human behavior modeling. In International Conference on Autonomous Agents and Multiagent Systems (pp. 396-410). Springer, Berlin, Heidelberg.
  • Lammoglia, A., Marilleau, N., & Josselin, D. (2011). How to assess the robustness of a flexible transport using an Agent Based Model?.
  • Lamy, F., Bossomaier, T., & Perez, P. (2011)"SimUse: Simulation of recreational poly-drug use." Proceedings of the 2011 IEEE Symposium on Artificial Life (ALIFE), 11-15 April 2011, pp. 170-177. [PDF]
  • Lamy, F., Perez, P., Ritter, A. & Livingston, M. (2011)"SimARC: An ontology-driven behavioural model of alcohol abuse." Proceedings of the Third International Conference on Advances in System Simulation, Oct. 23-29, pp. 128-133. [PDF]
  • Lauberte, I., & Ginters, E. (2011, May). Agent-based TemPerMod simulator cell architecture. In Proceedings of the 13th WSEAS International Conference on automatic control, modelling & simulation (ACMOS 11) (pp. 75-80).
  • Levin, J. A. (2011). Landmark representation and dynamic mediation. [HTML]
  • Levy, S. T. & Wilensky, U. (2011). Mining students inquiry actions for understanding of complex systems. ScienceDirect Alert: Computers & Education, Vol. 56, Iss. 3, 2011. pp. 556-573. [PDF]
  • Li, Y., Wu, J., Ping, Y., Kou, W., & Han, Z. (2011). "NetLogo-based simulation and analysis of college confidential project management." Computer Technology and Development.
  • Lima, D. V., da Rocha Costa, A. C., & Krusche, N. (2011, April). Net Logo and the Climate Change Model as a Tool for the Simulation of the Greenhouse Effect. In 2011 Workshop and School of Agent Systems, their Environment and Applications (pp. 104-106). IEEE.
  • Liu, D., & Chen, X. (2011, November). Rumor propagation in online social networks like twitter--a simulation study. In 2011 Third International Conference on Multimedia Information Networking and Security (pp. 278-282). IEEE.
  • Lubyansky, A. (2011). A system dynamics and agent-based simulation approach to test group-level theories of political violence. In The 29th International Conference of the System Dynamics Society (pp. 1-26).
  • Lytinen, S. L., & Railsback, S. F. (2011). Agent-based simulation platforms: An updated review. In European Meetings on Cybernetics and Systems Research.
  • Maharaj, S., McCaldin, T., Kleczkowski, A. (2011). A Participatory Simulation Model for Studying Attitudes to Infection Risk. In Proceedings of the Summer Computer Simulation Conference 2011, ACM Digital Library, July 2011. [PDF]
  • Maleš, L., & Žarnić, B. (2011, April). A spatiotemporal model of events within a BDI. In 2011 IEEE EUROCON-International Conference on Computer as a Tool (pp. 1-4). IEEE.
  • Mandava, V., Nimmagadda, P., Korrapati, T. R., & Anne, K. R. (2011, August). Knowledge Based Agent for Intelligent Traffic Light Control–An Indian Perspective. In International Conference on Intelligent Computing (pp. 491-501). Springer, Berlin, Heidelberg.
  • Manzo, G. (2011)“Relative Deprivation in Silico: Agent-based Models and Causality in Analytical Sociology.” in P. Demeulenaere (ed.), Analytical Sociology and Social Mechanisms, Cambridge, Cambridge University Press, ch. 13, 266-308 [PDF]
  • Marquez, B. Y., Espinoza-Hernandez, I., Castanon-Puga, M., Castro, J.R., & Suarez, E. D. (2011)"Distributed Agencies Applied to Complex Social Systems, a Multi-Dimensional approach." Proceedings of 2011 The 2nd International Conference on Next Generation Information Technology (ICNIT), 21-23 June 2011, pp. 213-219. [PDF]
  • Masiera, M. Fundamental and Technical Analysis to Comparison: Agents Simulation with NetLogo.
  • McCray, S. (2011). Simulating Belief Propagation Within a Population Via Agent Based Modeling Using NetLogo.
  • McDonnell, S. and M. Zellner (2011)."Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior." Transport Policy 18(6): 825-835.
  • Merico, D., & Bisiani, R. (2011, July). An agent-based data-generation tool for situation-aware systems. In 2011 Seventh International Conference on Intelligent Environments (pp. 129-134). IEEE.
  • Mölders, M., Fink, R. D., & Weyer, J. (2011). Modeling scientists as agents: How scientists cope with the challenges of the new public management of science. Journal of Artificial Societies and Social Simulation, 14(4). doi.org/10.18564/jasss.1831
  • Moreno, F., & Ospina, E. (2011). On Estimating the Position and Direction of a Leader of a Group of Entities. Mathematical Modelling in Engineering & Human Behaviour, 1-2.
  • Muscalagiu, I., Iordan, A., Muscalagiu, D. M., & Panoiu, M. (2011, April). The effect of synchronization of agents’ execution in randomly generated networks of constraints. In Proceedings of the 5th European conference on European computing conference (pp. 285-290). World Scientific and Engineering Academy and Society (WSEAS).
  • Nan, N. (2011). "Capturing bottom-up information technology use processes: A Complex adaptive systems model." Management Information Systems Quarterly, 35(2), 505-532.
  • Narang, V., Wong, S. Y., Leong, S. R., Abastado, J. P., & Gouaillard, A. (2011, August). Comparing mathematical models of cell adhesion in tumors. In 2011 Defense Science Research Conference and Expo (DSR) (pp. 1-4). IEEE.
  • Niazi, M. A.; Hussain, A. (2011). "A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments." Sensors Journal, IEEE, 11(2) doi: 10.1109/JSEN.2010.2068044. [PDF]
  • Niazi, M.A.K. (2011). "Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems." University of Stirling. [PDF]
  • NING, W. Z., & JIN, W. X. (2011). Study on the Influence of Bid Evaluation Criteria for Construction Industry Benefits——Based on MAS Behavior Theory. Technoeconomics & Management Research, (8), 5.
  • Noor, Talal H.; Sheng, Quan Z. (2011). "Trust as a Service: A Framework for Trust Management in Cloud Environments." Proceedings of the 2011 12th International Conference on Web Information System Engineering (WISE), 12-14 October 2011, pp. 314-321. [PDF]
  • Olson, I.C., & Horn, M. (2011). Modeling on the Table: Agent-Based Modeling in Elementary School with NetTango. Proceedings of 10th International Conference on Interaction Design and Children (short paper), Ann Arbor, MI. June, 2011
  • Olson, I.C., Leong, Z.A., Wilensky, U., & Horn, M.S. (2011). “It’s just a toolbar!” Using tangibles to help children manage conflict around a multi-touch tabletop. In Proc. of the fifth international conference on Tangible, Embedded and Embodied Interaction (TEI’11) , Funchal, Portugal. ACM New York. pp. 29-36.
  • Otcenaskova, T., Bures, V., & Cech, P. (2011). "Multi-agent simulations in decision support: Specifics of the biological incident management. " Mathematics and Computers in Biology, Business, and Acoustics. [PDF]
  • Owczarek, T. (2011, September). Collaboration and competition in complex environment—An agent-based approach. In Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems (Vol. 2, pp. 594-597). IEEE.
  • Pan, R. (2011, March). Rebellion on sugarscape: case studies for greed and grievance theory of civil conflicts using agent-based models. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (pp. 333-340). Springer, Berlin, Heidelberg.
  • Patarakin, E., Yarmakhov B., Burov V. (2011). "Agent based simulation activities within the wiki system." Educational Technology & Society. pp.407 - 422. (In Russian) [PDF]
  • Pawlewski, P., & Borucki, J. (2011). “Green” Possibilities of Simulation Software for Production and Logistics: A Survey. In Information Technologies in Environmental Engineering (pp. 675-688). Springer, Berlin, Heidelberg.
  • Pereda, M. & Zamarreno, J. M. (2011). "Agent-based modeling of an activated sludge process in a batch reactor." Proceedings of the 2011 19th Mediterranean Conference on Control & Automation (MED), 20-23 June 2011, pp. 1128-1133. [PDF]
  • Petreska, I., Kefalas, P., & Georghe, M. (2011). Population p systems with moving active cells. In Twelfth International Conference on Membrane Computing (CMC12) (pp. 421-432).
  • Petreska, I., Kefalas, P., & Gheorghe, M. (2011). Informal verification by visualisation of state-based agents models. In In Proceedings of the 6th Annual South East European Doctoral Student Conference.
  • Petreska, I., Kefalas, P., Gheorghe, M. (2011). "A framework towards the verification of emergent properties in spatial multi-agent systems." Proceedings of the Workshop on Applications of Software Agents, pp. 37-44.
  • Portugali, J. (2011). Complexity, Cognition and the City. Springer-Verlag.
  • Purnomo, H., Suyamto, D., Akiefnawati, R., Abdullah, L., & Harini, R. (2011). Harnessing the Climate Commons: An agent-based modelling approach to reduce carbon emission from deforestation and degradation.
  • Qingnan, L. I. U. X. L. I. R. L., & Xi, Z. H. A. O. (2011). Simulation of Passenger Flow at Urban Bus Station. Journal of Transport Information and Safety, (4), 5.
  • Railsback, S. F. & Grimm, V. (2011). Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press.
  • Rashid, S., Yoon, Y., & Kashem, S. B. (2011). Assessing the potential impact of Microfinance with agent-based modeling. Economic Modelling, 28(4), 1907-1913.
  • Rebaudo, F., Crespo-Pérez, V., Silvain, J-F., & Dangles, O. (2011). Agent-Based Modeling of Human-Induced Spread of Invasive Species in Agricultural Landscapes: Insights from the Potato Moth in Ecuador. Journal of Artificial Societies and Social Simulation (JASSS), 14 (3) 7. [HTML] (June 2011)
  • Rhee, J., & Iannaccone, P. Program/Abstract# 365 Understanding early stages of hematopoietic stem cell maturation during mouse embryogenesis.
  • Rodrigues, L. M., Dimuro, G. P., da Rocha Costa, A. C., & Emmendorfer, L. R. (2011, April). A Multiagent System-Based Solution for Shipment Operations with Priorities in a Container Terminal. In 2011 Workshop and School of Agent Systems, their Environment and Applications (pp. 28-36). IEEE.
  • Ruan, G., & Ruan, P. (2011, September). Knowledge Sharing within Innovation Network Members with Netlogo Simulation Platform. In 2011 International Conference of Information Technology, Computer Engineering and Management Sciences (Vol. 2, pp. 210-213). IEEE.
  • Rush, D. (2011). "A simulation of evolving sustainable technology through social pressure." Proceedings of the 2011 AAAI Fall Symposium, pp. 117-126. [PDF]
  • Sakellariou, I., Kefalas, P., & Stamatopoulou, I. (2011). An intelligent agents and multi-agent systems course involving NetLogo. In Multi-Agent Systems for Education and Interactive Entertainment: Design, Use and Experience (pp. 26-50). IGI Global.
  • Salamon, T. (2011)."Design of Agent-Based Models: Developing Computer Simulations for a Better Understanding of Social Processes." Repin, Czech Republic: Bruckner Publishing. [Website]
  • Sarah, B., Soumia, L., & Farida, M. An approach automata Game of Life-MAS of segmentation MRI brain.
  • Sengupta, P., & Wilensky, U. (2011). Lowering the Learning Threshold: Multi-Agent-Based Models and Learning Electricity. In Khine, M.S., & Saleh, I.M (Eds.). Dynamic Modeling: Cognitive Tool for Scientific Inquiry. Springer, New York, NY. [PDF]
  • Seo, Y. W., & Lee, K. C. (2011, August). Multi-Agent Simulation Approach for Investigating the Evolution Pattern Analysis of Digital Creativity Considering Task Diversity. In 2011 International Conference on Management and Service Science (pp. 1-4). IEEE.
  • Shelley, T., Lyons, L., Zellner, M. & Minor, E.(2011)"Evaluating the embodiment benefits of a paper-based tui for educational simulations." Proceedings of CHI Extended Abstracts 2011, pp. 1375-1380. [PDF]
  • Shi-shi, F., Ji-jun, Z., & Yun-tao, L. (2011). An Agent-Based Model of Technical Duffision in Industrail Clusters. Electronics Quality, (2), 11.
  • Shi, X. (2011). Organisational Innovativeness and Diffusion of Innovation (Doctoral dissertation, University of York).
  • Shvartsman, I., & Taveter, K. (2011, September). Agent-oriented knowledge elicitation for modeling the winning of “hearts and minds”. In 2011 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 605-608). IEEE.
  • Sinclair, M., Dauerty, H., & Finke, T. (2011). Biocomplexity in the high school classroom. MSTA Journal, 56, 31-35.
  • Singh, V. K., Modanwal, N., & Basak, S. (2011)"MAS coordination strategies and their application in disaster management domain." Proceedings of 2011 2nd International Conference on Intelligent Agent and Multi-Agent Systems (IAMA), 7-9 Sept. 2011, pp. 14-19. [PDF]
  • Sklar, E. (2011)."NetLogo, a multi-agent simulation environment." Artificial Life, 13(3): 303-311.
  • Soriano, G. C. & Urano, Y. (2011) "Replication with state using the self-organizing map neural network." Proceedings of the 2011 13th International Conference on Advanced Communication Technology (ICACT), 13-16 Feb. 2011, pp. 383-388. [PDF]
  • Spears, W. M. (2011). NetLogo and Physicomimetics. In Physicomimetics (pp. 55-92). Springer, Berlin, Heidelberg.
  • Stonedahl, F. & Wilensky, U. (2011).Finding Forms of Flocking: Evolutionary Search in ABM Parameter-Spaces. In Multi-Agent-Based Simulation XI, T. Bosse, A. Geller, & C. M. Jonker (Eds). Lecture Notes in Computer Science. Springer Berlin / Heidelberg. Vol. 6532. pp. 61-75. [PDF]
  • Suárez, J. L. & Sancho, F. (2011). A virtual laboratory for the study of history and cultural dynamics. Journal of Artificial Societies and Social Simulation, 14(4). doi.org/10.18564/jasss.1855
  • Taboada, M., Cabrera, E., & Luque, E. (2011). A Decision Support System for Hospital Emergency Departments Built Using Agent-Based Techniques. In Advances on Practical Applications of Agents and Multiagent Systems (pp. 247-253). Springer, Berlin, Heidelberg.
  • Taboada, M., Cabrera, E., Iglesias, M. L., Epelde, F., & Luque, E. (2011). An agent-based decision support system for hospitals emergency departments. Procedia Computer Science, 4, 1870-1879.
  • Tang, M., & Jia, H. (2011, December). An approach for calibration and validation of the social force pedestrian model. In Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE) (pp. 2026-2031). IEEE.
  • Thiruvathukal, G. K. (2011). An Exceptionally Useful Exploration. Computing in Science & Engineering, 13(1), 5-8.
  • Thompson, K., Kennedy-Clark, S., Markauskaite, L., & Southavilay, V. (2011). Capturing and analysing the processes and patterns of learning in collaborative learning environments.
  • Tivnan, B., Koehler, M., McMahon, M., Olson, M., Rothleder, N., & Shenoy, R. (2011). Adding to the regulator's toolbox: Integration and extension of two leading market models. arXiv preprint arXiv:1105.5439.
  • Turner, A., Balestrini-Robinson, S., & Mavris, D. (2011, December). Representation of humanitarian aid/disaster relief missions with an agent based model to analyze optimal resource placement. In Proceedings of the 2011 Winter Simulation Conference (WSC) (pp. 2649-2660). IEEE.
  • Ulbinaitė, A., Kučinskienė, M., & Le Moullec, Y. (2011). Integration of the decoy effect in an agentbased-model simulation of insurance consumer behaviour. In 2011 International Conference on Software and Computer Applications IPCSIT. Conference Proceedings (Vol. 9, pp. 152-157).
  • van Borkulo, C., Borsboom, D., Nivard, M. G., & Cramer, A. (2011). NetLogo Symptom Spread Mode.
  • Varnakovida, P. Transportation Development: Agent-Based Modeling and Landscape Prediction of Urban Environment.
  • Vattam, S. S., Goel, A. K., Rugaber, S. (2011). "Behavior Patterns: Bridging conceptual models and agent-based simulations in interactive learning environments." Proceedings of the 2011 IEEE 11th International Conference on Advanced Learning Technologies, pp.139-141
  • Vattam, S. S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R., Gray, S., & Sinha, S. (2011). Understanding complex natural systems by articulating structure-behavior-function models. Journal of Educational Technology & Society, 14(1), 66-81.
  • Vinatier, F., Lescurret, F., Duyck, P.-F., Martin, O., Senoussi, R., & Tixier, P. (2011).Should I Stay or Should I Go? A Habitat-Dependent Dispersal Kernel Improves Prediction of Movement. PLoS ONE, 6, e21115.
  • Wang, C., & Zhou, M. (2011, November). The Multi-Layer Network Structure and the Dynamic Mechanism of Proliferation of Cluster Risks. In 2011 International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 3, pp. 501-504). IEEE.
  • WANG, F., CHEN, L., & LU, H. Q. (2011). Research of CGF maneuver planning based on genetic algorithm. Journal of PLA University of Science and Technology (Natural Science Edition), 6.
  • Wanjugu, W. M. (2011). Urban Transport Real time tracking System (Doctoral dissertation, University of Nairobi).
  • Wen-ze, N. I. N. G., & Wei-xing, J. I. N. G. (2011). Research on Industrial Qualification Based on MAS in the Construction Industry. Journal of Engineering Management, 3.
  • Wen, K., & Shen, W. (2011). A study of wayfinding in Taipei metro station transfer: Multi-agent simulation approach. Proceedings of the 28th ISARC, Seoul, 875-879.
  • Wildman, W., & Sosis, R. (2011). Stability of Groups with Costly Benefits and Practices. Journal of Artificial Societies and Social Simulation (JASSS), 14 (3) 6. [HTML] (June 2011)
  • Wilkinson, I. F., Young, L., Marks, R., Bossomaier, T., & Held, F. P. (2011). Toward a business network agent-based modeling system. In International Conference on Complex Systems (pp. 680-691).
  • Xia, H., Jia, Z., Ju, L., Li, X., & Zhu, Y. (2011)."A subjective trust management model with multiple decision factors for MANET based on AHP and fuzzy logic rules." Proceedings of 2011 IEEE/ACM International Converence on Green Computing and Communications (GreenCom), 4-5 Aug. 2011, pp. 124-130. [PDF]
  • Xing-Chen, P., & Yun-Feng, W. (2011). Research of agent architecture based on BDI under emergency response. Microcomputer and Its Applications, 30(5), 81-84.
  • Xiong, H. Y., & So, W. H. (2011). NetLogo Extension Module for the Active Participatory Simulations with GoGo Board. The Journal of Korean Institute of Communications and Information Sciences, 36(11B), 1363-1372.
  • Yan-hui, N. I. E. (2011). A research of enterprise innovation knowledge transfer network econometric model on NetLogo simulation. Studies in Science of Science, (7), 20.
  • Yan, G. A. N. G., & Yuquan, D. U. (2011). Applying Simulation Model to Evaluation on Television Audience Ratings: Construction of Simulation Model Based on NetLogo. Video Engineering, 11.
  • Yu, L., & Duan, C. (2011, August). Evacuation modelling: An agent based approach. In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC) (pp. 7080-7083). IEEE.
  • Yu, Y. (2011). Simulation Study of Tacit Knowledge Sharing in the Learning Organization [J]. Information Studies: Theory & Application, 11.
  • Yu, Z. T., Yi, L. L., Yan, Q., Shu, H. Y., & Liang, C. (2011, May). Simulation analysis of the evolution of mobile internet industry chain. In 2011 International Conference on Business Management and Electronic Information (Vol. 3, pp. 533-536). IEEE.
  • Yuantao, J., Siqin, Y., & Zhifeng, X. (2011, August). Simulation of e-commerce diffusion model based on Netlogo. In Proceedings of the 2011 International Conference on Innovative Computing and Cloud Computing (pp. 99-102).
  • Zhao, C. X., Zhang, X. F., & Gong, W. N. (2011). Research on Simulation on Classroom Discipline Based on Complex System Theory with Scientific Teaching Materials. In Applied Mechanics and Materials (Vol. 63, pp. 736-739). Trans Tech Publications Ltd.
  • Zhao, M., & He, Q. (2011). A Simulation System of Social Economic. Computer and Information Science, 4(5), 97.

2010

  • Blikstein, P., & Wilensky, U. (2010). MaterialSim: A constructionist agent-based modeling approach to engineering education. In M. J. Jacobson & P. Reimann, (Eds.), Designs for learning environments of the future: International perspectives from the learning sciences. New York: Springer.
  • Abrahamson, D. (2010). A tempest in a teapot is but a drop in the ocean: action-objects in analogical mathematical reasoning. In K. Gomez, L. Lyons & J. Radinsky (Eds.) Learning in the Disciplines: Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010) (Vol. 1 [Full Papers], pp. 492-499). International Society of the Learning Sciences: Chicago, IL.
  • Berryman, M. J., & Angus, S. D. (2010). Tutorials on agent-based modelling with NetLogo and network analysis with Pajek. In Complex physical, biophysical and Econophysical systems (pp. 351-375).
  • Berryman, M.J. & Angus, S.D. (2010). Software Tools for Analysis and Modelling of Complex Systems. In R. L. Dewar & F. Detering (Eds.) Complex Physical, Biophysical and Econophysical Systems: Proceedings of the 22nd Canberra International Physics Summer School. Sydney: World Scientific Publishing. [PDF]
  • Buzby, C. K., & Jona, K. (2010). EcoCasting: Using NetLogo models of aquatic ecosystems to teach scientific inquiry. AGUFM, 2010, ED21A-0650.
  • Cecconi, F., Campenni, M., Andrighetto, G., & Conte, R. (2010). What do agent-based and equation-based modelling tell us about social conventions: The clash between ABM and EBM in a congestion game framework. Journal of Artificial Societies and Social Simulation, 13(1). doi.org/10.18564/jasss.1585
  • Chu, C.J., Weiner, J., Maestre, F.T., Wang, Y.S., Morris, C., Xiao, S., Yuan, J.L., Du, G.Z. & Wang, G. (2010). Effects of positive interactions, competitive modes and abiotic stress on self thinning in simulated plant populations. Annals of Botany 106: 647-652
  • Cioffi-Revilla, C. (2010). A methodology for complex social simulations. Journal of Artificial Societies and Social Simulation, 13(1). doi.org/10.18564/jasss.1528
  • Cong, R., & Wei, Y. (2010). Potential impact of (CET) carbon emissions trading on China’s power sector: A perspective from different allowance allocation options. Energy, 35, p 3921-3931. [PDF]
  • Davilia, A. A., & An, G. (2010). An Agent Based Model of Liver Damage, Inflammation, and Repair: in Silico Translation of Cellular and Molecular Mechanisms to the Clinical Phenomena of Cirrhosis Using NetLogo. Journal of Surgical Research (ASC Abstracts Issue), 158(2):411.
  • Davilia, A. A., & An, G. (2010). An Agent Based Model of Liver Damage, Inflammation, and Repair: in Silico Translation of Cellular and Molecular Mechanisms to the Clinical Phenomena of Cirrhosis Using NetLogo. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • Edmonds, B. (2010). Bootstrapping knowledge about social phenomena using simulation models. Journal of Artificial Societies and Social Simulation, 13(1). doi.org/10.18564/jasss.1523
  • Georgeon, O., & Sakellariou, I. Designing Environment-Agnostic Agents. In Adaptive Learning Agents workshop (ALA2012), of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), pp. 25-32. Valencia, Spain 2012.2[HTML]
  • Gobattoni, F., Lauro, G., Leone, A., Monaco, R., & Pelorosso, R. (2010). A simulation method for the stability analysis of landscape scenarios by using a NetLogo application in GIS environment. EGUGA, 5769.
  • Hadzikadic, M., Carmichael, T., & Curtin, C. (2010). Complex Adaptive Systems and Game Theory: An Unlikely Union. Complexity. Vol 16, Issue 1, p34.
  • Hamill, L. (2010). Agent-Based Modelling: The Next 15 Years. Journal of Artificial Societies and Social Simulation (JASSS), 13 (4): 7. [HTML]
  • Holbert, N., & Wilensky, U. (2010). FormulaT Racing: Combining gaming culture and intuitive sense of mechanism for video game design. In K. Gomez & J. Radinsky (Ed.), Proceedings of the 9th International Conference of the Learning Sciences. Chicago, IL.
  • Holbert, N., Penney, L., & Wilensky, U. (2010). Bringing Constructionism to Action Gameplay. In J. Clayson & I. Kalas (Eds.), Proceedings of the Constructionism 2010 Conference. Paris, France, Aug 10-14.
  • Izquierdo, S.S., Izquierdo, L.R. & Vega-Redondo, F. (2010). The Option to Leave: Conditional Dissociation in the Evolution of Cooperation. Journal of Theoretical Biology Volume 267, Issue 1, pp. 76–84. [PDF]
  • Jang, S. M., Rhee, J. H., & Oh, S. H. (2010). Implementation of Microscopic Pedestrian Simulation by using NetLogo. Journal of The Korean Society of Civil Engineers, 30(2D), 119-125.
  • Johnson, T., Karaman, S., & Rosenberg-Kima, R. B. (2010, October). Computer modelling of teams learning: an agent based social simulation of team learning (SSTeL).. Paper presented at AECT (Association for Educational Communications and Technology), Anaheim, CA.
  • Johnson, T., Sikorski, E. G., Rosenberg-Kima, R. B., Novak, E., & Andrews, D. E. (2010, May). Development of a training effects algorithm for use within an agent-based modeling and simulation tool.. Paper presented at AERA (American Educational Research Association), Denver, Colorado.
  • Kahn, K. & Noble, H. (2010). The Modelling4All Project -- A web-based modelling tool embedded in Web 2.0. Paper presented at the Proceedings of Constructionism 2010, Paris, France
  • Kahn, K., & Noble, H. (2010). The BehaviourComposer 2.0: a web-based tool for composing NetLogo code fragments. Constructionist approaches to create learning, thinking and education: Lessons for the 21st century: Proceedings for Constructionism.
  • Kim, J-W. (2010). A Tag-Based Evolutionary Prisoner's Dilemma Game on Networks with Different Topologies. Journal of Artificial Societies and Social Simulation (JASSS), 13 (3): 2. [HTML] (June 2010)
  • Kleczkowski, A., Maharaj, S. (2010). Stay at home, Wash Your Hands: Epidemic Dynamics with Awareness of Infection. In proceedings of the SCS Summer Computer Simulation Conference, Ottawa, July 2010.
  • Kurahashi, C., & An, G. (2010). Examining the Spatial Dynamics of the Inflammatory Response with Topographical Metrics in an Agent-Based Computational Model of Inflammation and Healing. Journal of Surgical Research (ASC Abstracts Issue), 158(2):382.
  • Kurahashi, C., & An, G. (2010). Examining the Spatial Dynamics of the Inflammatory Response with Topographical Metrics in an Agent-Based Computational Model of Inflammation and Healing. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • Lerner, R., Levy, S.T., & Wilensky, U. (2010). Encouraging Collaborative Constructionism: Principles Behind the Modeling Commons. n J. Clayson & I. Kalas (Eds.), Proceedings of the Constructionism 2010 Conference. Paris, France, Aug 10-14.
  • Levy, S. T., Wilensky, U. (2010). Mining students' actions for understanding of complex systems: Students' explorations of gas models in the Connected Chemistry curriculum. Paper presented at AERA 2010, Denver, CO. [PDF]
  • Li, Y., Wang, W., Ji, L., & Zhu, J. (2010, October). Research on Muti-Agent simulation and emulation in battlefield based on NETLOGO. In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010) (Vol. 6, pp. V6-131). IEEE.
  • Maroulis, S., Guimera, R., Petry, H., Stringer, M., Gomez, L., Amaral, L., & Wilensky, U. (2010). A complex systems approach to Educational Policy Research. Science 1 October 2010: Vol. 330. no. 6000, pp. 38 .
  • Mastrangeli, M., Schmidt, M., & Lacasa, L. (2010). The Roundtable: An Abstract Model of Conversation Dynamics. Journal of Artificial Societies and Social Simulation 13 (4) 2 [HTML]
  • Miodownik, D., Cartrite, B., & Bhavnani, R. (2010). Between replication and docking: "Adaptive agents, political institutions, and civic traditions" revisited. Journal of Artificial Societies and Social Simulation, 13(3). doi.org/10.18564/jasss.1627
  • Monett, D., Janisch, R., Starroske, S. (2010). NL-Analyzer: Enhancing Simulation Tools to Assist Multiagent Systems' Teaching. In K. Gomez & J. Radinsky (Ed.), In W. van der Hoek, G.A. Kaminka, Y. Lespérance, M. Luck, S. Sen (eds.), Proceedings of the Workshop Multi-Agent Systems for Education and Interactive Entertainment (MASEIE), 9th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010, pp. 1-6, Toronto, Canada.
  • Muis, J. (2010). Simulating Political Stability and Change in the Netherlands (1998-2002): an Agent-Based Model of Party Competition with Media Effects Empirically Tested. Journal of Artificial Societies and Social Simulation (JASSS) 13 (2): 4 [HTML] (March 2010)
  • Niazi, M., Siddique, Q., Hussain, A., & Kolberg, M. (2010). Verification & Validation of an Agent-Based Forest Fire Simulation Model. Agent-Directed Simulation Symposium, SCS Spring Simulation Conference, April 2010, Orlando, FL.
  • Olsen, J & Jepsen, MR (2010). HPV transmission and cost-effectiveness of introducing quadrivalent HPV vaccination in Denmark. International Journal of Technology Assessment in Health Care , 26(2).
  • Olševičová, K., Bodnárová, A., & Čech, P. (2010, November). Managing Net-Logo Models in CLIPS. In Proceedings of the 9th WSEAS International Conference on Data Networks, Communications, Computers (DNCOCO 10) (pp. 150-152).
  • Olson, I., Horn, M., & Wilensky, U. (2010). NetLogo Tango: Supporting Student Programming with Tangible Objects and Multi-Touch Displays. In K. Gomez & J. Radinsky (Ed.), Proceedings of the 9th International Conference of the Learning Sciences. Chicago, IL.
  • Pluchino, A., Rapisarda, A. & Garofalo, C. (2010). Peter Principle Revisited: a Computational Study. Physica A: Statistical Mechanics and its Applications, 389(3), 467-472. [PDF]
  • Rabuzin, K., & Bakos, N. (2010). Agent-based simulation model of online auctions in NetLogo. In Central European Conference on Information and Intelligent Systems (p. 75). Faculty of Organization and Informatics Varazdin.
  • Rezayan, H., Delavar, M.R., Frank, A.U. & Mansouri, A. (2010). Spatial rules that generate urban patterns: Emergence of the power law in the distribution of axial line length. International Journal of Applied Earth Observation and Geoinformation. Volume 12, Issue 5, October 2010, pp. 317-330.
  • Rogers, C., Nguy, K. N. T., Amajor, O. A., Seaquist, T., Crawford, B., Mubayi, A., ... & Oyediran, O. (2010). Daily behavior of trypanosoma cruzi hosts and vectors in Texas: An agent-based modeling approach in Netlogo. Mathematics Department Technical Report 2010-18, University of Texas at Arlington, 2010. Online at http://www. uta. edu/math/preprint/rep2010 18. pdf.
  • Russell, E., Buzby, C., & Wilensky, U. (2010). Watershed Modeling For Education. Paper presented at the First International Conference for Geospatial Research & Application, Washington, DC.
  • Sakellariou, I. Agent based Modelling and Simulation using State Machines. SIMULTECH 2012. Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, pp 270-279, Rome, Italy, 28 - 31 July, 2012 [HTML]
  • Sakellariou, I. Turtles as State Machines - Agent Programming in NetLogo using State Machines. Proceedings of the 4th International Conference on Agents and Artificial Intelligence (ICAART 2012), Volume 2 - Agents, Joaquim Filipe, Ana L. N. Fred (Eds.), SciTePress 2012, ISBN 978-989-8425-96-6, pp. 375-378, Vilamoura, Algarve, Portugal, 6-8 February, 2012 [HTML]
  • Seal, J.B., Alverdy, J.C., & An, G. (2010). Mechanistic Computational Representation of Iron Metabolism in the Gut Milieu. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 3, 2010.
  • Seal, J.B., Alverdy, J.C., Zaborina, O., Zaborin, A., Babrowski, T., Romanowski, K., & An, G. (2010). Computational mechanistic representation of phosphate sensing and virulence activation in Pseudomonas aeruginosa in the gut milieu. Poster Presentation for the 39th Annual Critical Care Congress of the Society of Critical Care Medicine, Miami, FL, January 11, 2010.
  • Sengupta, P., & Wilensky, U. (2010). Balancing Electrons & Learning Electricity in 5th Grade: Emergence, Electric Current and Multi- Agent Based Models . Cognition and Instruction.
  • Sheth, K.R., & An, G. (2010). In Silico Translation of Cellular and Molecular Mechanisms to Clinical Phenomena in Atheroma Development with an Agent Based Model. Journal of Surgical Research (ASC Abstracts Issue), 158(2):382-3.
  • Sheth, K.R., & An, G. (2010). In Silico Translation of Cellular and Molecular Mechanisms to Clinical Phenomena in Atheroma Development with an Agent Based Model. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • Stamatopoulou, I., Sakellariou, I., & Kefalas, P. Formal Agent-Based Modelling and Simulation of Crowd Behaviour in Emergency Evacuation Plans. Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on , vol.1, no., pp.1133-1138, 7-9 Nov. 2012 [HTML]
  • Stonedahl, F. & Rand, W., & Wilensky, U. (2010). Evolving Viral Marketing Strategies. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. Portland, OR.
  • Stonedahl, F. & Rand, W., & Wilensky, U. (2010). Evolving Viral Marketing Strategies. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. Portland, OR.
  • Stonedahl, F. & Wilensky, U. (2010). Finding Forms of Flocking: Evolutionary Search in ABM Parameter-Spaces. Proceedings of the MABS workshop at the Ninth International Conference on Autonomous Agents and Multi-Agent Systems. Toronto, Canada.
  • Stonedahl, F., & Wilensky, U. (2010). Evolutionary Robustness Checking in the Artificial Anasazi Model. Proceedings of the AAAI Fall Symposium on Complex Adaptive Systems: Resilience, Robustness, and Evolvability. November 11-13, 2010. Arlington, VA.
  • Suhadolnik, N., Galimberti, J., & Da Silva, S. (2010). Robot traders can prevent extreme events in complex stock markets. Physica A: Statistical Mechanics and its Applications, 389(22): 5182-5192.
  • Thiele, J.C., & Grimm, V. (2010). NetLogo meets R: Linking agent-based models with a toolbox for their analysis. Environmental Modeling and Software. 25(8), 972-974.
  • Valbuena, D., Verburg, P.H., Veldkamp, A., Bregt, A.K. & Ligtenberg, A. (2010). Effects of farmers' decisions on the landscape structure of a Dutch rural region: An agent-based approach. Landscape and Urban Planning. 97(2), 98-110.
  • Wagh, A. & Wilensky, U. (2010). Agent-based and aggregate level reasoning elicited by problem scenarios and an agent-based model. Poster presented at the annual meeting of the American Education Research Association, Denver, CO, April 30-May 4.
  • Wandling, M., & An, G. (2010). Multi-Scale Dynamic Knowledge Representation of Pulmonary Inflammation with an Agent-Based Model: from Gene Regulation to Clinical Phenomenon. Journal of Surgical Research (ASC Abstracts Issue), 158(2):381.
  • Wandling, M., & An, G. (2010). Multi-Scale Dynamic Knowledge Representation of Pulmonary Inflammation with an Agent-Based Model: from Gene Regulation to Clinical Phenomenon. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • Wilensky, U., & Novak, M. (2010). Teaching and learning evolution as an emergent process: The BEAGLE project. Epistemology and science education: Understanding the evolution vs. intelligent design controversy, 213-243.
  • Wilensky, U., & Papert, S. (2010). Restructurations: Reformulations of Knowledge Disciplines through new representational forms. In J. Clayson & I. Kallas (Eds.), Proceedings of the Constructionism 2010 Conference. Paris, France.
  • Wilkerson-Jerde, M. & Wilensky, U. (2010). NetLogo HotLink Replay: A Tool for Exploring, Analyzing and Interpreting Mathematical Change in Complex Systems. Poster to be presented at ICLS 2010, Chicago, IL, Jun 29 - Jul 2.
  • Wilkerson-Jerde, M. & Wilensky, U. (2010). Qualitative Calculus of Systems: Exploring Students' Understanding of Rate of Change and Accumulation in Multiagent Systems. Paper accepted for presentation at AERA 2010, Denver, CO.
  • Wilkerson-Jerde, M. & Wilensky, U. (2010). Seeing Change in the World from Different Levels: Understanding the Mathematics of Complex Systems. In M. Jacobson (Org.), U. Wilensky (Chair), and Peter Reimann (Discussant), Learning about Complexity and Beyond: Theoretical and Methodological Implications for the Learning Sciences. To be presented at ICLS 2010, Chicago, IL, Jun 29 - Jul 2.
  • Wilkerson-Jerde, M., & Wilensky, U. (2010). Restructuring Change, Interpreting Changes:The DeltaTick Modeling and Analysis Toolkit. Paper to be presented at Constructionism 2010, Paris.
  • Yang, C.K. & Wilensky, U. (2010). Reinterpreting school effects from the bottom up: Merging statistical analysis and a complex systems perspective. Poster presented at the Constructionism conference. Paris, France. August 16-20, 2010.

2009

  • An, G., & Wilensky, U. (2009). From artificial life to in silico medicine: NetLogo as a means of translational knowledge representation in biomedical research. In A. Adamatzky & M. Komosinski (Eds.), Artificial Life Models in Software (2nd Ed.). Berlin: Springer-Verlag. [PDF]
  • Abrahamson, D. (2009). A student's synthesis of tacit and mathematical knowledge as a researcher's lens on bridging learning theory. In M. Borovcnik & R. Kapadia (Eds.), Research and developments in probability education [Special Issue]. International Electronic Journal of Mathematics Education, 4(3), 195 - 226.
  • Abrahamson, D. (2009). Embodied design: Constructing means for constructing meaning. Educational Studies in Mathematics, 70(1), 27-47. [PDF]
  • An, G. (2009). A model of TLR4 signaling and tolerance using a qualitative, particle–event-based method: Introduction of spatially configured stochastic reaction chambers (SCSRC). Mathematical biosciences, 217(1), 43-52.
  • Ang, C.S., & Zaphiris, P. (2009). Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design. In Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (INTERACT '09), Tom Gross, Jan Gulliksen, Paula Kotzé, Lars Oestreicher, Philippe Palanque, Raquel Oliveira Prates, and Marco Winckler (Eds.). Springer-Verlag, Berlin, Heidelberg, 443-456.
  • Bandini, S., Manzoni, S., & Vizzari, G. (2009). Agent based modeling and simulation: An informatics perspective. Journal of Artificial Societies and Social Simulation, 12(4).
  • BenDor, T., Westervelt, J., Aurambout, J. & Meyer, W. (2009). Simulating population variation and movement within fragmented landscapes: An application to the gopher tortoise (Gopherus polyphemus). Ecological Modelling 220(6), 867-878.
  • Bettge, T. (2009). System dynamics modeling of community sustainability in netlogo. TJHSST Computer Systems Lab Senior Research Project, 2008–2009, 1-8.
  • Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using multi-agent simulation. International Journal of Computers for Mathematical Learning, 14(1), 81-119. [PDF]
  • Blikstein, P., Wilensky, U., & Abrahamson, D. (2009, April). Towards a framework for cognitive research using agent-based modeling and complexity sciences. In M. Jacobson (Chair), M. Kapur (Organizer) & N. Sabelli (Discussant), Complexity, learning, and research: Under the microscope, new kinds of microscopes, and seeing differently. Symposium conducted at the annual meeting of the American Educational Research Association, San Diego, CA.
  • Carmichael, T., Hadzikadic, M.,Dréau, D., & Whitmeyer, J. (2009). Characterizing Threshold Effects Across Diverse Phenomena. In Z. Ras & W. Ribarsky (Eds), Advances in Information and Intelligent Systems. New York: Springer.
  • Castillo, L. F., Bedia, M. G., Uribe, A. L., & Isaza, G. (2009). A formal approach to test commercial strategies: Comparative study using Multi-agent based techniques. Journal of Physical Agents, 3(3), 25-30.(1), 27-47. [PDF]
  • Chu, C.J., Weiner, J., Maestre, F.T., Xiao, S., Wang, Y.S., Li, Q., Yuan, J.L., Zhao, L.Q., Ren, Z.W. & Wang, W. 2009.Positive interactions can increase size inequality in plant populations. Journal of Ecology 94: 1401-1407.
  • Crooks, A., Hudson-Smith, A., & Dearden, J. (2009). Agent street: An environment for exploring agent-based models in Second Life. Journal of Artificial Societies and Social Simulation, 12(4).
  • Drachsler, H., Hummel, H., van den Berg, B., Eshuis, J., Waterink, W., Nadolski, R., Berlanga, A., Boers, N. & Koper, R. (2009). Evaluating the Effectiveness of Personalised Recommender Systems in Learning Networks. In R. Koper (ed.), Learning Network Services for Professional Development (pp.95-113), Berlin:Springer-Verlag. [PDF]
  • Dréau, D., Stanimirov, D., Carmichael T., & Hadzikadic M. (2009). An agent-based model of solid tumor progression. Paper presented at the 1st International Conference on Bioinformatics and Computational Biology, New Orleans, LA., April 2009.
  • Filatova, T., Parker, D., & van der Veen, A. (2009). Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change. Journal of Artificial Societies and Social Simulation (JASSS), 12 (1): 3. [HTML]
  • Friedman, D. & Abraham, R. (2009). Bubbles and crashes: Gradient dynamics in financial markets. Journal of Economic Dynamics and Control 33(4), 922-937.
  • Fuks, K., & Kawa, A. (2009). Simulation of resource acquisition by e-sourcing clusters using netlogo environment. Agent and Multi-Agent Systems: Technologies and Applications, 687-696.[PDF]
  • Graham, S. (2009). Behaviour Space: Simulating Roman Social Life and Civil Violence. Digital Studies / Le Champ NuméRique, 1(2). [HTML]
  • Hamill, L. & Gilbert, N. (2009). Social circles: A simple structure for agent-based social network models. Journal of Artificial Societies and Social Simulation, 12(2).
  • Han, J. X., & Xue, H. F. (2009). Netlogo based modeling and simulation of oem enterprise knowledge management. Journal of Xi'an Technological University, 29, 192-199.
  • Handel, A., Yates, A., Pilyugin, S.S., & Antia, R. (2009). Sharing the burden: Antigen transport and firebreaks in immune responses. Journal of the Royal Society Interface 6, 447-454.
  • Hunt, C.A. , Ropella, G. E. P.,  Lam, T. N., Tang, J., Kim, S. H. J., Engelberg, J. A., & Sheikh-Bahaei, S. (2009). At the biological modeling and simulation frontier. Pharmaceutical Research 26(11), 2369-2400. [PDF]
  • Izquierdo, L. R., Izquierdo, S. S., Galán, J. M., & Santos, J. I. (2009). Techniques to Understand Computer Simulations: Markov Chain Analysis. Journal of Artificial Societies and Social Simulation (JASSS), 12 (1): 6 [HTML]
  • Janssen, M.A. (2009). Understanding Artificial Anasazi. Journal of Artificial Societies and Social Simulation (JASSS), 12 (4): 13.
  • Jepsen, MR , Simonsen, J & Ethelberg, S (2009). Spatio-temporal cluster analysis of the incidence of Campylobacter cases and patients with general diarrhea in a Danish county.International Journal of Health Geographics , 8(11), pp. 1-12.
  • Jiang, L., & Zhao, C. (2009, August). The Netlogo-Based Dynamic Model for the Teaching. In 2009 Ninth International Conference on Hybrid Intelligent Systems (Vol. 2, pp. 49-53). IEEE.
  • Johnson, B.R. (2009). A self-organizing model for task allocation via frequent task quitting and random walks in the honeybee. American Naturalist 174, 537-547.
  • Kimbrough, S. O., & Murphy, F. H. (2009). Learning to collude tacitly on production levels by oligopolistic agents. Computational Economics 33(1), 47-78. [PDF]
  • Kornhauser, D., Wilensky, U., & Rand, W. (2009). Design guidelines for agent based model visualization. Journal of Artificial Societies and Social Simulation, JASSS, 12(2), 1. [PDF]
  • Lansing, J.S., Cox, M.P., Downey, S.S., Janssen, M.A., & Schoenfelder, J.W. (2009). A robust budding model of Balinese water temple networks. World Archaeology 41(1), 112-133.
  • Lerner, R., Levy S. T., & Wilensky, U. (2009). Design of the Modeling Commons. Chais Conference, Tel Aviv, Israel.
  • Levin, J. A. (2009).Notes toward a landmark mediated geometry. [HTML]
  • Levy, S. T., & Wilensky, U. (2009). Crossing levels and representations: The Connected Chemistry (CC1) curriculum. Journal of Science Education and Technology, 18(3), 224-242. [PDF]
  • Levy, S. T., & Wilensky, U. (2009). Students' learning with the Connected Chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18(3), 243-254. [PDF]
  • Linard, C., Ponçon, N., Fontenille, D., & Lambin, E.F., (2009). A multi-agent simulation to assess the risk of malaria re-emergence in southern France. Ecological Modelling, 220(2): 160-174.
  • Maharaj, S., McCaig, C., & Shankland C. E. (2009). Studying the effects of adding spatiality to a process algebra model. 8th Workshop on Process Algebra and Stochastically Timed Activities :153-158, 2009. Edinburgh.
  • Manzo, G. (2009). “Boudon’s Model of Relative Deprivation Revisited.” In Cherkaoui, M. & Hamilton, P. (eds.) Raymond Boudon: A Life in Sociology, Oxford, Bardwell Press, vol. 3, part 3, ch. 46, 91-121. [PDF]
  • Nadolski, R. J., van den Berg, B., Berlanga, A. J., Drachsler, H., Hummel H. G. K., Koper, R., & Sloep, P. B. (2009). Simulating Light-Weight Personalised Recommender Systems in Learning Networks: a Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies. Journal of Artificial Societies and Social Simulation (JASSS), 12(1). [HTML]
  • New York Times Magazine. (2009). "Random Promotions" from the 9th Annual Year in Ideas. Retrieved March 23, 2010 from http://www.nytimes.com/projects/magazine/ideas/2009/#social_science-12. [HTML]
  • Niazi, M., and Hussain, A. (2009). Agent-Based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad Hoc, and Other Complex Networks. IEEE Communications Magazine, Vol.47 No.3. [HTML]
  • Niazi, M., Hussain, A., & Kolberg, M. (2009). Verification and Validation of Agent-Based Simulations using the VOMAS approach. Proceedings of the Third Workshop on Multi-Agent Systems and Simulation '09, as part of MALLOW 09, Sep 7-11, 2009, Torino, Italy.
  • Nikolai, C. & Madey, G. (2009). Tools of the trade: A survey of various agent based modeling platforms. Journal of Artificial Science and Social Simulation, 12(2).
  • Novak, M., Levy, S. T., & Wilensky, U. (2009). Playing in a particle sandbox and gaining a glass box perspective: The Connected Chemistry curriculum. Working paper.
  • O'Sullivan, D. (2009). "Changing neighborhoods—Neighborhoods Changing; A Framework for spatially explicit agent-based models of social systems." Sociological Methods Research, 37(4), 498-530. [HTML
  • Olševičová, K. (2009). Ambient Intelligence, Agents and NetLogo. In Ambient Intelligence Perspectives II: Selected Papers from the Second International Ambient Intelligence Forum 2009 (Vol. 5, p. 77). IOS Press.
  • Patarakin E., Yarmakhov B. (2009). "Modeling organizational relations with the NetLogo "links" Educational Technology & Society" Educational Technology & Society, No. 2. pp. 409-422. (In Russian) [PDF]
  • Rolón, M., Canavesio, M. & Martínez, E. (2009). Agent Based Modelling and Simulation of Intelligent Distributed Scheduling Systems. In Jezowski Jacek & Thullie Jan (Eds.), Proceedings of the 19th European Symposium on Computer Aided Process Engineering (Vol. 26, pp. 985-990). Amsterdam, Holland: Elsevier.
  • Rolón, M., Canavesio, M. & Martinez, E. (2009). Generative Modeling of Holonic Manufacturing Execution Systems for Batch Plants. In R.M. de Brito Alves, C.A.O. do Nascimento , & E.C. Biscaia Jr. (Eds.),10th International Symposium on Process Systems Engineering: Part A (Vol. 27, 795-800). Amsterdam, Holland: Elsevier.
  • Sakellariou, I., Kefalas, P., Stamatopoulou, I. (2009). MAS Coursework Design in NetLogo. In Proceedings of the Educational Uses of Multi Agent Systems, Budapest, Hungary. [PDF]
  • Sammons, P. J., & Page, J. (2009). Experimentation and validation of vehicle cluster simulator using NetLogo. Adelaide, SA, Australia.
  • Schultz, L., Koons, P. O., & Schauffler, M. (2009). Middle-School Understanding of the Greenhouse Effect using a NetLogo Computer Model. AGUFM, 2009, ED23A-0545.
  • Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21-50. [PDF]
  • Siddiqa, A., Niazi, M., Mustafa, F., Bukhari, H., Hussain A., Akram, N., Shaheen, S., Ahmed F., & Iqbal, S. (2009). A New Hybrid Agent-Based Modeling & Simulation Decision Support System For Breast Cancer Data Analysis, IEEE ICICT 09, IBA, Karachi.
  • Steiniger, S. & Hay, G.J. (2009). Free and open source geographic information tools for landscape ecology. Ecological Informatics 4(4), 183-195.
  • Stonedahl, F. (2009, May). NetLogo: Meditations on a Tool for Learning and Modeling. In International Workshop on the Educational Uses of Multi-Agent Systems (EduMAS) (p. 3).
  • Stonedahl, F., Wilkerson-Jerde, M., & Wilensky, U. (2009). Re-conceiving introductory computer science curricula through agent-based modeling. Paper presented at the Eighth International Conference on Autonomous Agents and Multi-agent Systems (AAMAS) - EduMAS Workshop, Budapest, Hungary. [PDF]
  • Sudhira, H.S. and Ramachandra, T.V. (2009). A Spatial Planning Support System for Managing Bangalore’s Urban Sprawl. In Stan Geertman and John Stillwell (Eds.), Planning Support Systems: Best Practice and New Methods. Springer.
  • Thompson, N. S. & Derr, P. (2009). Contra Epstein, good explanations predict. Journal of Artificial Societies and Social Simulation, 12(1).
  • Vattam, S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. & Jordan, R. (2009). From Conceptual Models to Agent-based Simulations: Why and How. Paper presented at the 14th International Conference on Artificial Intelligence in Education (AIED), 2009. [PDF]
  • Wang, B., Ji, F., & Zhou, M. (2009). Analyzed on the Forming and Its Evolving Of Complex Logistics Network Based On Netlogo. In Logistics: The Emerging Frontiers of Transportation and Development in China (pp. 519-526).
  • Weisberg, M., & Muldoon, R. (2009). Epistemic Landscapes and the Division of Cognitive Labor. Philosophy of Science, 76, 225-252
  • Wilkerson-Jerde, M., & Wilensky, U. (2009, April). Complementarity in agent-based and equation-based models. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.
  • Wilkerson, M. (2009). Agents with attitude: Exploring Coombs Unfolding technique with agent-based models. International Journal of Computers for Mathematical Learning, 14 (1), 51-60. [PDF]
  • Zhai, Z., Schoenharl, T., Chen, F., & Madey, G. (2009). Design and Implementation of an Agent-Based Simulation for Emergency Response and Crisis Management. Department of Computer Science and Engineering, University of Norte Dame, IN. [PDF]

2008

  • Wilkerson-Jerde, M., Sengupta, P., & Wilensky, U. (2008). Perceptual supports for sense-making: A case study using multi-agent based computational learning environments. In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the Eighth International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 151-152). Utrecht, The Netherlands: ISLS. [PDF]
  • Abraham, R., & Friedman, D. (2008). Bubbles and Crashes: a Simulation Approach.
  • Aktipis, A. (2008). A SIMPLE model for the evolution of movement and cooperation: Social dilemmas emerge from interactions with a shared environment. Paper presented at Swarmfest, 2008.
  • Amblard, F. & Jager, W. (2008). Advances in Complex Systems (ACS): Special Issue on Social Simulation (Vol. 11(2)). World Scientific Publishing.
  • An, G. (2008). Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theoretical Biology and Medical Modelling, 5(1), 11.
  • Arunachalam, S., Zalila-Wenkstern, R., & Steiner, R. (2008). Environment Mediated Multi Agent Simulation Tools - A Comparison. In Proceedings of the 2008 Second IEEE international Conference on Self-Adaptive and Self-Organizing Systems Workshops (57-62). Washington, DC: IEEE Computer Society. [PDF]
  • Blikstein, P., & Wilensky, U. (2008). Implementing multi-agent modeling in the classroom: Lessons from empirical studies in undergraduate engineering education. In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 266-273). Utrecht, The Netherlands: ISLS. [PDF]
  • Blikstein, P., Abrahamson, D., & Wilensky, U. (2008). The classroom as a complex adaptive system: An agent-based framework to investigate students' emergent collective behaviors. In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 312-313). Utrecht, The Netherlands: ISLS. [PDF]
  • Bonura, A., Capizzo, M. C., Fazio, C., & Guastella, I. (2008, May). Electric conduction in solids: a pedagogical approach supported by laboratory measurements and computer modelling environments. In AIP Conference Proceedings (Vol. 1018, No. 1, pp. 227-230). American Institute of Physics.
  • Bravo, G. (2008). Imitation and cooperation in different helping games. Journal of Artificial Societies and Social Simulation, 11(1).
  • Chu, C.J. Maestre, F.T., Xiao, S., Weiner, J., Wang, Y.S., Duan, Z.H. & Wang, G. (2008). Balance between facilitation and resource competition determines biomass-density relationships in plant populations. Ecology Letters 11: 1189-1197
  • Colosimo, A. (2008). Biological Simulations by Autonomous Agents: Two Examples Using the NetLogo Environment. Biophysics and Bioengineering Letters, 1(3).[PDF]
  • Crooks, A., Castle, C. & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems 32(6), 417-430.
  • Damaceanu, R. (2008). An agent-based computational study of wealth distribution in function of resource growth interval using NetLogo. Applied Mathematics and Computation, 201, 371-377.
  • Diappi, L. & Bolchi, P. (2008). Smith's rent gap theory and local real estate dynamics: A multi-agent model. Computers, Environment and Urban Systems 32(1), 6-18.
  • Earnest, D. C. (2008). Voting Complexity and Electoral Outcomes: An Agent-Based Model of Condorcet Social Choice Problems. Complexity and Policy Analysis: Decision Making in an Interconnected World. Kurt A. Richardson, Linda Dennard and Goktug Morcol, eds. Greenwich, CT: Information Age Publishing: 2008. 147-166.
  • Earnest, D.C. (2008). Coordination in Large Numbers: An Agent-Based Model of International Negotiations. International Studies Quarterly 52, 2: 363-382. [HTML]
  • Feldman, T., Friedmam, D., & Abraham R. (2008). Bubbles & Crashes: An Experimental Approach. Retrieved February 25, 2010 from http://www.vismath.org/research/landscapedyn/articles/vela2.pdf. [PDF]
  • Fioretti, G. & Lomi, A. (2008). An agent-based representation of the garbage can model of organizational choice. Journal of Artificial Societies and Social Simulation, 11(1).
  • Francisco, M. (2008). Designing classrooms with ABM. Paper presented at Swarmfest, 2008.
  • Gilbert G. (2008). Agent-based models. Sage Publications, Inc.
  • Graham, S., & Steiner, J. (2008). “Travellersim: Growing Settlement Structures and Territories with Agent-Based Modelling” in Jeffrey T. Clark and Emily M. Hagemeister (eds) Digital Discovery: Exploring New Frontiers in Human Heritage. CAA 2006. Computer Applications and Quantitative Methods in Archaeology. Proceedings of the 34th Conference, Fargo, United States, April 2006. Budapest: Archaeolingua.
  • Iozzi, F. (2008). A simple implementation of Schelling's segregation model in NetLogo. Dondena Centre for Research on Social Dynamics Dondena Working Paper, 15.
  • Isaac, A. G. (2008). Simulating evolutionary games: A Python-based introduction. Journal of Artificial Societies and Social Simulation, 11(3).
  • Janota, A. (2008). MAS Model of the Level Crossing. International Journal of ITS Research, Japan, Vol. 6, No. 2 (p. 111-116). ISSN 1348-8503
  • Janssen, M. A., Alessa, L. N., Barton, M., Bergin, S., & Lee, A. (2008). Towards a community framework for agent-based modelling. Journal of Artificial Societies and Social Simulation, 11(2).
  • Janssen, M.A. & Bushman, C. (2008). Evolution of cooperation and altruistic punishment when retaliation is possible. Journal of Theoretical Biology 254: 451-455.
  • Jolly, R., & Wakeland, W. W. (2008). Using Agent Based Simulation and Game Theory Analysis to Study Information Sharing in Organizations - The InfoScape. HICSS 2008: 335.
  • Jones, G.T. (2008). Dynamical Jurisprudence: Law as a Complex System. Georgia State University Law Review, 24(4), 873-883. [PDF]
  • Jovani, R., and V. Grimm. (2008). Breeding synchrony in colonial birds: from local stress to global harmony. Proceedings of the Royal Society of London B 275:1557-1563.
  • Kanarek, A., Lamberson, R., & Black, J. M. (2008). An individual-based model for traditional foraging behavior: investigating effects of environmental fluctuation. Natural Resource Modeling, Vol 21 (1), pp 93-116. [HTML]
  • Le, Q.B., Park, S.J., Vlek, P.L. & Cremers, A.B. (2008). Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system. I. Structure and theoretical specification. Ecological Informatics 3(2), 135-153.
  • Levy, S.T., & Wilensky, U. (2008). Inventing a "mid-level" to make ends meet: Reasoning through the levels of complexity. Cognition and Instruction, 26(1), 1-47.
  • Lysenko, M. & D'Souza, R. M. (2008). A framework for megascale agent based model simulations on graphics processing units. Journal of Artificial Societies and Social Simulation, 11(4).
  • Menezes, R., & Bullen, H. (2008). A study of terrain coverage models. Proceedings of the 2008 ACM symposium on Applied computing. [HTML]
  • Menke, N. (2008). Modeling the Effects of Trauma on Epidermal Wound Healing. Paper presented at Swarmfest, 2008.
  • Merlone, U., Sonnessa, M., & Terna, P. (2008). Horizontal and vertical multiple implementations in a model of industrial districts. Journal of Artificial Societies and Social Simulation, 11(2).
  • Millington, J., Romero-Calcerrada, R., Wainwright, J., & Perry, G. (2008). An agent-based model of Mediterranean agricultural land-use/cover change for examining wildfire risk. Journal of Artificial Societies and Social Simluation, 11(4).
  • Niazi, M. (2008). Self-organized customized content delivery architecture for ambient assisted environments. In Proceedings of the Third international Workshop on Use of P2p, Grid and Agents For the Development of Content Networks (Boston, MA, USA, June 23-23, 2008). HPDC, UPGRADE '08. ACM, New York, NY, 45-54.
  • Niazi, M., & Baig, A.R. (2008). Growth of Research Institutes in Developing Nations. International Research Conference 2008, West Visayas State University, La Paz Iloilo City, Philippines, Feb 27- 29, 2008.
  • Niazi, M., Hussain, A., Baig, A.R., & Bhatti, S. (2008). Simulation of the research process. in Winter Simulation Conference, Miami, FL, pp.1326-1334.
  • Okuyama, T. (2008). Intraguild predation with spatially structured interactions. Basic and Applied Ecology 9(2), 135-144.
  • Pathak, S.A., Jacobson, M.J., Kim, B., Zhang, B., & Feng, D. (2008). Learning the Physics of Electricity with Agent-Based Models: The paradox of productive failure. Paper presented at the 2008 International Conference on Computers in Education. [PDF]
  • Polhill, J. G., Parker, D., Brown, D., & Grimm, V. (2008). Using the ODD protocol for describing three agent-based social simulation models of land-use change. Journal of Artificial Societies and Social Simulation, 11(2).
  • Rahmani, A. T., Saberi, A., Mohammadi, M., Nikanjam, A., Mosabbeb, E. A., & Abdoos, M. (2008, May). SHABaN multi-agent team to herd cows. In International Workshop on Programming Multi-Agent Systems (pp. 248-252). Springer, Berlin, Heidelberg.
  • Rand, W., Blikstein, P., & Wilensky, U. (2008). GoGoBot: Group collaboration, multi-agent modeling and robots. In L. Padgham, D. Parkes, J. Müller & S. Parsons (Eds.), Proceedings of the 7th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS (Vol. 3, pp. 1717-1722). Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). [PDF]
  • Russell, E., & Wilensky, U. (2008, May). Consuming spatial data in NetLogo using the GIS Extension. Paper presented at the annual meeting of the Swarm Development Group, Chicago, IL.
  • Sakellariou, I., Kefalas, P., Stamatopoulou, I. (2008). Enhancing NetLogo to Simulate BDI Communicating Agents. In J. Darzentas et al. (Eds.), Proceedings of 5th Hellenic Conference on Artificial Intelligence, SETN 08, (pp. 263-275). Syros, Greece: Springer-Verlag. [PDF]
  • Sakellariou, I., Kefalas, P., Stamatopoulou, I. (2008). Teaching Intelligent Agents using NetLogo. Paper presented at the ACM-IFIP Informatics Education Europe III Conference, IEEIII 2008, Venice, Italy, December 4-5, 2008. [PDF]
  • Schumann, D., & Simon, A. (2008). Public acceptance of CO2 capture and storage (CCS): Simulating the impact of communication. Paper presented at ZUMA (Zentrum für Umfragen, Methoden und Analysen, Centre for surveys, methods and analyses) Advanced Simulation Workshop, April 8-11, Koblenz.
  • Sengupta, P., & Wilensky, U. (2008, March). Designing across ages: On the low-threshold-high-ceiling nature of NetLogo-based learning environments. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
  • Sengupta, P., & Wilensky, U. (2008, March). On the representational and epistemological affordances of NetLogo-based science curricula. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
  • Sengupta, P., & Wilensky, U. (2008). Learning activities as tools for formative assessment - Case study of a computational multi-agent based electricity curriculum (NIELS: NetLogo Investigations In Electromagnetism). In G. Kanselaar, J. van Merriëboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 383-391). Utrecht, The Netherlands: ISLS.
  • Sengupta, P., & Wilensky, U. (2008). On learning electricity in 7th grade with multi-agent based computational models (NIELS). In G. Kanselaar, J. van Merriëboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 123-125). Utrecht, The Netherlands: ISLS. [PDF]
  • Sengupta, P., & Wilensky, U. (2008). On the learnability of electricity as a complex system. In G. Kanselaar, J. van Merriëboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 258-264). Utrecht, The Netherlands: ISLS.
  • Stonedahl, F., Kornhauser, D., Russell, E., Brozefsky, C., Verreau, E., Tisue, S., & Wilensky, U. (2008, May). Tinkering with turtles: An overview of NetLogo's Extensions API. Paper presented at the annual meeting of the Swarm Development Group, Chicago, IL. [PDF]
  • Stonedahl, F., Rand, W., & Wilensky, U. (2008, July). CrossNet: A framework for crossover with network-based chromosomal representations. Paper presented at the 2008 Genetic and Evolutionary Computation Conference (GECCO), Atlanta, GA. [PDF]
  • Stonedahl, F., Rand, W., & Wilensky, U. (2008, May). Multi-agent learning with a distributed genetic algorithm: Exploring innovation diffusion on networks. Paper presented at the Seventh International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), Estoril, Portugal. [PDF]
  • Sudhira, H. S. (2008).Modelling the Strategic Interactions in Urban Governance. In Proceedings of Third International Conference on Public Policy and Governance, Centre for Public Policy, Indian Institute of Management - Bangalore, India.
  • Usher, C, Tilson, L, Olsen, J, Jepsen, M, Walsh, C, Barry, M & Jepsen, MR (2008). Cost-effectiveness of human papillomavirus vaccine in reducing the risk of cervical cancer in Ireland due to HPV types 16 and 18 using a transmission dynamic model. Vaccine , 26(44), pp. 5654-5661.
  • Voinov, A. A., Sven Erik, J., & Brian, F. (2008). Software. Encyclopedia of Ecology (pp. 3270-3277). Oxford: Academic Press.
  • Wang, B. J., Ji, F., & Zhou, M. (2008, September). Investigate on the forming and its evolving of complex diffusion network based on Netlogo. In 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings (pp. 236-242). IEEE.
  • Wang, J., Dam, G., Yildrim, S., Rand, W., Wilensky, U., & Houk, J. C. (2008). Reciprocity between the cerebellum and the cerbral cortex: Nonlinear dynamics in microscopic modules. Complexity, 14(2), 29-45. [PDF]
  • Weisberg, M., & Reisman, K. (2008). The Robust Volterra Principle. Philosophy of Science, 75, 106–131.
  • Whitmeyer, J., Carmichael, T., Eichelberger, C., Hadzikadic, M., Khouja, M., Saric, A., & Sun, M. (2008). A Computer Simulation Laboratory for Social Theories. Paper presented at the 2008 IEEE/WIC/ACM International Conference on Intelligence Agent Technology, Sydney, Australia, December 2008.
  • Will, O. & Hegselmann, R. (2008). A replication that failed: On the computational model in 'Michael W. Macy and Yoshimichi Sato: Trust, cooperation and market formation in the U.S. and Japan. Proceedings of the National Academy of Sciences, May 2002'. Journal of Artificial Societies and Social Simulation, 11(3).

2007

  • Abrahamson, D., & Wilensky, U. (2007). Learning Axes and Bridging Tools in a Technology-Based Design for Statistics. International Journal of Computers for Mathematical Learning. 12(1), 23-55.
  • Abrahamson, D., Blikstein, P., & Wilensky, U. (2007). Classroom Model, Model Classroom: Computer-Supported Methodology for Investigating Collaborative-Learning Pedagogy. Proceedings of the Computer-Supported Collaborative Learning conference, New Brunswick, NJ.
  • Abrahamson, D., Wilensky, U., & Levin, J. (2007). Agent-Based Modeling as a Bridge Between Cognitive and Social Perspectives on Learning. In D. Abrahamson (Org.), Learning Complexity: Agent-Based Modeling Supporting Education Research on Student Cognition in Social Contexts. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Albiero, F., Fitzek, F. H., & Katz, M. D. (2007). Analysis of Cooperative Power Saving Strategies with NetLogo. In Cognitive Wireless Networks (pp. 603-620). Springer, Dordrecht.
  • Albiero, F., Fitzek, F. H., & Katz, M. D. (2007). Introduction to NetLogo. In Cognitive Wireless Networks (pp. 579-602). Springer, Dordrecht.
  • Bakshy, E., & Wilensky, U. (2007). Turtle Histories and Alternate Universes; Exploratory Modeling with NetLogo and Mathematica. In M. J. North, C. M. Macal & D. L. Sallach (Eds.), Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence (pp. 147-158). IL: Argonne National Laboratory and Northwestern University. [PDF]
  • Blikstein, P., & Wilensky, U. (2007). Bifocal modeling: a framework for combining computer modeling, robotics and real-world sensing. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Blikstein, P., & Wilensky, U. (2007). Modeling manifold epistemological stances with agent-based computer simulation. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Blikstein, P., Abrahamson, D., & Wilensky, U. (2007). Multi-agent simulation as a tool for investigating cognitive-developmental theory. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Blikstein, P., Rand, W., & Wilensky, U. (2007). Examining group behavior and collaboration using ABM and robots. In M. J. North, C. M. Macal & D. L. Sallach (Eds.), Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence (pp. 159-172). IL: Argonne National Laboratory and Northwestern University. [PDF]
  • Blikstein, P., Rand, W., & Wilensky, U. (2007). Just a Cog in the Machine: Participatory Robotics as a Powerful Tool for Understanding Collaborative Learning. Computer Supported Collaborative Learning (CSCL), Rutgers University, Rutgers, NJ, USA. [PDF]
  • Bryson, J., Ando, Y., Lehmann, H. (2007). Agent-based modelling as scientific method: A case study analysing primate social behavior. Philosophical Transactions of the Royal Society, 362, pp.1685-1695. [PDF]
  • Capizzo, M.C. and Bonura, A. and Fazio, C. (2007). Characteristic Properties of Semiconductors Through Experiments and Modelling. Paper presented at GIREP EPEC Conference Frontiers of Physics Education, 26-31 August, 2007, Opatija, Croatia. [HTML]
  • Damaceanu, R-C. (2007). An agent-based computational study of wealth distribution in function of resource growth interval using NetLogo. Applied Mathematics & Computation, Vol 12 (43). [HTML]
  • Dekker, A. (2007). Studying organisational topology with simple computational models. Journal of Artificial Societies and Social Simulation, 10(4).
  • Doi, M. & Kawaguchi, I. (2007). Ecological impacts of umbrella effects of radiation on the individual members. Journal of Environmental Radioactivity 96(1-3), 32-38.
  • Fitzek, Frank H.P. & Katz, Marcos D. (2007). Cognitive Wireless Networks. Springer. [HTML]
  • Gilbert, N. (2007). Agent-Based Models. Sage Publications.
  • Gong, X. & Xiao, R. (2007). Research on multi-agent simulation of epidemic news spread characteristics. Journal of Artificial Societies and Social Simulation, 10(3).
  • Hmelo-Silver, C., Liu, L., Gray, S., Finkelstein, H., & Schwartz, R. (2007). Enacting things differently: Using NetLogo models to learn about complex systems. In biennial meeting of European Association for Research on Learning and Instruction.
  • Hundhausen, C.D. & Brown, J.L. (2007). What You See Is What You Code: A "live" algorithm development and visualization environment for novice learners. Journal of Visual Languages & Computing 18(1), 22-47.
  • Izquierdo, S.S., & Izquierdo, L.R. (2007). The impact of quality uncertainty without asymmetric information on market efficiency. Journal of Business Research, Volume 60, Issue 8, pp. 858-867. []
  • Johnson, I. D. (2007). Mathematical modeling with NetLogo: Cognitive demand and fidelity. [PDF]
  • Kahn, K. (2007). Building computer models from small pieces. In Proceedings of the 2007 Summer Computer Simulation Conference (San Diego, California, July 16 - 19, 2007). Summer Computer Simulation Conference (931-936). San Diego, CA: Society for Computer Simulation International. [PDF]
  • Kahn, K. (2007). Comparing Multi-Agent Models Composed from Micro-Behaviours. Retrieved February 25, 2010 from http://dfl.cetis.ac.uk/wiki/uploads/0/0d/M2M_2007_C2L_v4.pdf. [PDF]
  • Katzper, M. (2007). Roles for autonomous physiologic agents; an oxygen supply and demand example. Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come, pp 1483 - 1486. [HTML]
  • Koné, M., Berlanga, Sloep, P. B., Koper, R. (2007). A Learning Community Simulation. Retrieved February 25, 2010 from https://dspace.ou.nl/bitstream/1820/1247/1/KoneetalWBC2007.pdf. [PDF]
  • Kornhauser, D., Rand, W., & Wilensky, U. (2007). Visualization Tools for Agent-Based Modeling in NetLogo. Paper presented at Agent2007, Chicago, November 15-17. [PDF]
  • Lam, R. (2007). Agent-based simulations of service policy decisions. Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come. [PDF]
  • Lee, K. C. & Lee, N. (2007). CARDS: Case-based reasoning decision support mechanism for mutli-agent negotiation in mobile commerce. Journal of Artificial Societies and Social Simulation, 10(2).
  • Levy, S., & Wilensky, U. (2007). How do I get there...straight, oscillate or inch? High-school students' exploration patterns of Connected Chemistry. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Levy, S.T., & Wilensky, U. (2007). Action across levels (AAL): A multiple levels perspective on what it means to make sense of complex systems. Paper presented at the EARLI 2007 conference, Budapest, Hungary, September 2007. [PDF]
  • Miller, J.H. & Page, S.E. (2007). Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.
  • Momen, S., Amavasai, B.P., Siddique, N.H. (2007). Mixed Species Flocking for Heterogeneous Robotic Swarms. IEEE Eurcon 2007, The International Conference on 'computer as a tool,' pp: 2329-2336. [HTML]
  • Niazi, M., & Baif, A.R. (2007). Phased Approach to Simulation of Security Algorithms for Ambient Intelligent (AmI) Environments. Winter Simulation Conference 07 (WSC07), PhD Student Colloquium, December 7-11, 2007.
  • North, M. & Macal, C. (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simluation. Oxford University Press.
  • North, M. J, & Macal, C. M. (2007). Agent-based modeling and simulation: desktop ABMS. Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come. [HTML]
  • Ottino-Loffler, J., Rand, W., & Wilensky, U. (2007). Coevolution of Predators and Prey in a Spatial Model. Paper presented at the GECCO 2007 Conference. London, England. July 7-11. [PDF]
  • Rand, W., & Sondahl, F. (2007). The El Farol Bar Poblem and Computational Effort: Why People Fail to Use Bars Efficiently. In M. J. North, C. M. Macal & D. L. Sallach (Eds.), Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence (pp. 71-86). IL: Argonne National Laboratory and Northwestern University. [PDF]
  • Rand, W., & Wilensky, U. (2007). Full-Spectrum Modeling: From Simplicity to Elaboration and Realism in Urban Pattern Formation. Paper presented at the North American Association Computational Social and Organization Sciences conference (NAACSOS), Atlanta, GA. [PDF
  • Sengupta, P., Wilkerson, M. & Wilensky, U. (2007). On The Relationship Between Spatial Knowledge And Learning Electricity: Comparative Case Studies of Students Using 2D And 3D Emergent, Computational Learning Environments. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Sklar, E. (2007). Software Review: NetLogo, a Multi-agent Simulation Environment. Artificial Life, 13, 303-311.
  • Sondahl, F., & Rand, W (2007). Evolution of Non-Uniform Cellular Automata using a Genetic Algorithm: Diversity and Computation. Presented at the GECCO 2007 Conference, London, UK.
  • Sondahl, F., & Rand, W. (2007). Multi-agent Communication Disorders: Dynamic Breeding Networks in Genetic Algorithms. Presentation at Swarmfest 2007 Conference, DePaul University, Chicago, IL. July 2007.
  • Stamatopoulo I., Sakellariou, I., Kefala P., & Eleftheraki G. (2007). Formal Modelling for In-silico Experiments with Social Insect Colonies. Paper presented at the 11th Panhellenic Conference in Informatics, 18-20 May 2007. Patras, Greece. [PDF]
  • Sudhira, H.S., & Ramachandra, T.V. (2007). Integrated Spatial Planning Support System for Managing Urban Sprawl. Reviewed Paper #199, In Conference Proceedings of 10th International Conference on Computers in Urban Planning and Urban Management, Iguassu Falls, PR, Brazil.
  • Sudhira, H.S., & Ramachandra, T.V. (2007). Modelling the dynamics of urban sprawl using system dynamics and agent-based models. International Conference on Framing Land Use Dynamics – II, University of Utrecht, The Netherlands, 18-20 April 2007.
  • Tatara, E., North, M.J., Howe, T., Collier, N., & Parker, M. (2007). Building Models in Repast Symphony: A predator-prey example. Paper presented at NAACSOS 2007, Atlanta, GA.
  • Thompson, J.W., & Sorvig, K. (2007). Sustainable Landscape Construction: A Guide to Green Building Outdoors. 2nd edition. Island Press. [HTML]
  • Wilensky, U., & Centola, D. (2007). Simulated evolution: Facilitating students' understanding of the multiple levels of fitness through multi-agent modeling. Paper presented at the Evolution Challenges Conference. Phoenix, AZ. November 3, 2007.
  • Wilensky, U., & Rand, W. (2007). Making models match: Replicating agent-based models. Journal of Artificial Societies and Social Simulation (JASSS), 10(4). [PDF]
  • Zhang, T. & Zhang, D. (2007). Agent-based simulation of consumer purchase decision-making and the decoy effect. Journal of Business Research 60(8), 912-922.
  • Zhao C., Zhong N., & Hao Y. (2007). AOC-by-Self-discovery Modeling and Simulation for HIV. In S. Istrail, P. Pevzner, and M. Waterman (Eds.), Life System Modeling and Simulation (4689/2007, 462-469). Springer: Netherlands. [PDF]

2006

  • Sengupta, P. & Wilensky, U. (2006). NIELS: An agent-based modeling environment for learning electromagnetism. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Stondahl, F., Tisue, S. & Wilensky, U. (2006). Breeding faster turtles: Progress towards a NetLogo compiler. Paper presented at Agent 2006, Chicago, IL. [PDF]
  • Abrahamson, D., Berland, M.W., Shapiro, R. B., Unterman, J. W., & Wilensky, U. (2006). Leveraging epistemological diversity through computer-based argumentation in the domain of probability. For the Learning of Mathematics, 26(3), 39-55.
  • Abrahamson, D., Janusz, R. M. & Wilensky, U. (2006). There once was a 9-Block... -- A middle-school design for probability and statistics. Journal of Statistics Education, 14(1). [HTML]
  • Alessa, L. N., Laituri, M., & Barton, M. (2006). An "all hands" call to the social science community: Establishing a community framework for complexity modeling using agent based models and cyberinfrastructure. Journal of Artificial Societies and Social Simulation, 9(4).
  • An, G. (2006). Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling. Journal of critical care, 21(1), 105-110.
  • Andruss, C. (2006). Computational Modeling of the Interaction of the T. cruzi Parasite and its Environment. Final research report presented at the VCU Bio Informatics Institute, Virginia. [PDF]
  • Berland, M.& Wilensky, U. (2006).Constructionist collaborative engineering: Results from an Implementation of PVBOT. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Blikstein, P. , Abrahamson, D., & Wilensky, U. (2006). Minsky, mind, and models: Juxtaposing agent-based computer simulations and clinical-interview data as a methodology for investigating cognitive-developmental theory. Paper presented at the annual meeting on the Jean Piaget Society, Baltimore, MD, June 1-3. [HTML]
  • Blikstein, P. & Wilensky, U. (2006). 'Hybrid modeling': Advanced scientific investigation linking computer models and real-world sensing. Paper presented at the Proceedings of the Seventh International Conference of the Learning Sciences, Bloomington, IL, June 27-July 1. [PDF]
  • Blikstein, P. & Wilensky, U. (2006). A case study of multi-agent-based simulation in undergraduate materials science education. Paper presented at the Annual Conference of the American Society for Engineering Education, Chicago, IL, June 18-21.
  • Blikstein, P. & Wilensky, U. (2006). An atom is known by the company it keeps: A constructionist learning environment for Materials Science using multi-agent simulation. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA, April 7-11. [PDF]
  • Blikstein, P. & Wilensky, U. (2006). From inert to generative modeling: Case studies of multi-agent-based simulation in undergraduate engineering education. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. [PDF]
  • Blikstein, P., & Wilensky, U. (2006). The Missing Link: A Case Study of Sensing-and-Modeling Toolkits for Constructionist Scientific Investigation. Proceedings of the International Conference for Advanced Learning Technologies (ICALT 2006) (pp. 980-982). Kerkrade, The Netherlands. [PDF]
  • Blikstein, P., Rand, W. & Wilensky, U. (2006). Participatory, embodied, multi-agent simulation. Paper presented at AAMAS 2006. [PDF]
  • Bonura, A., Capizzo, M. C., & Fazio, C. (2006). A Pedagogical Approach to Modelling Electric Conduction in Solids. In Proceedings of the XXI GIREP International Conference “Modelling in Physics and Physics Education” (pp. 824-830). AMSTEL Institute, University of Amsterdam.
  • Bryson, J. J., Caulfield, T. J., & Drugowitsch, J. (2006). Integrating Life-Like Action Selection into Cycle-Based Agent Simulation Environments. in Proceedings of Agent 2005: Generative Social Processes, Models, and Mechanisms, Michael North, David L. Sallach and Charles Macal eds., pp. 67-81, Argonne National Laboratory 2006. [PDF]
  • Felsen, M., Watson, B., & Wilensky, U. (2006). Urban Complexity + Emergence: Procedural Modeling of City Activity and Form. In Surfacing Urbanisms: Recent Approaches to Metropolitan Design (pp. 261-265). Pasadena, CA: Woodbury University. [PDF]
  • Garofalo, M. (2006). Modeling the El Farol Bar Problem in NetLogo. Submitted for publication. [PDF]
  • Graham, S. (2006). Networks, Agent-Based Modeling, and the Antonine Itineraries. In The Journal of Mediterranean Archaeology 19.1: 45-64.
  • Hammond, R., & Axelrod, R. (2006). The Evolution of Ethnocentrism. Journal of Conflict Resolution, 50(6), 926-936. [HTML]
  • Hills, T. T. (2006). Building" ethical agent" based simulations: A case study of a pathological problem in altruistic punishment. In ALife Ethics Workshop Artificial Life X, Bloomington, IN, USA.
  • Izquierdo, L.R. and Polhill, J.G. (2006). Is your model susceptible to floating-point errors? Journal of Artificial Societies and Social Simulation 9(4)4. [PDF]
  • Jacobson, M. & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), pp. 11-34. [PDF]
  • Jepsen, MR , Leisz, S , Rasmussen, K , Jakobsen, J, Moller-Jensen, L & Christiansen, L (2006). Agent-based modelling of shifting cultivation field patterns, Vietnam. International Journal of Geographical Information Science , 20(9), pp. 1067-1085.
  • Joyce, D., Kennison, J., Densmore, O., Guerin, S., Barr, S., Charles, E., et al. (2006). My Way or the Highway: a More Naturalistic Model of Altruism Tested in an Iterative Prisoners' Dilemma. Journal of Artificial Societies and Social Simulation, 9(2).
  • Koehler, M. (2006). Using NetLogo in the Data Farming Environment.
  • Kumar, S. & Mitra, S. (2006). Self-organizing traffic at a malfunctioning intersection. Journal of Artificial Societies and Social Simulation, 9(4).
  • Lechner, T., Watson, B., Ren, P., Wilensky, U., Tisue, S. & Felsen, M. (2006). Procedural modeling of urban land use. ACM SIGGRAPH 2006 conference. [PDF]
  • Levy, S.T, Novak, M. & Wilensky, U. (2006). Students' foraging through the complexities of the particulate world: Scaffolding for independent inquiry in the connected chemistry (MAC) curriculum. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. [PDF]
  • Levy, S.T. & Wilensky, U. (2006). Emerging knowledge through an emergent perspective: High-school students' inquiry, exploration and learning in the Connected Chemistry curriculum. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. [PDF]
  • Levy, S.T. & Wilensky, U. (2006). Gas laws and beyond: Strategies in exploring models of the dynamics of change in the gaseous state. Paper presented at the annual meeting of the National Association for Research in Science Teaching, San Francisco, CA. [PDF]
  • Makowsky, M. (2006). An agent-based model of mortality shocks, intergenerational effects, and urban crime. Journal of Artificial Societies and Social Simulation, 9(2).
  • Maroulis, S., & Wilensky, U. (2006). Using agent-based modeling to understand the social dynamics of schools. Paper presented at the Teacher Networks conference, Northwestern University, Evanston, IL, November 8.
  • Muscalagiu, I., Jiang, H., & Emil, P. H. (2006, September). Implementation and Evaluation Model for the Asynchronous Search Techniques: From a Synchronously Distributed System to an Asynchronous Distributed System. In 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (pp. 209-216). IEEE.
  • Pugh, G. A. (2006, March). Agent-based simulation of discrete-event systems. In Proceedings of the 2006 Illinois-Indiana and North Central Joint Section Conferences,(Mar 31-Apr 1).
  • Railsback, S., Lytinen, S., & Jackson, S. (2006). Agent-based Simulation Platforms: Review and Development Recommendations. SIMULATION, 82(9), 609-623.
  • Rand, W. (2006). Machine Learning Meets Agent-Based Modeling: When Not to Go to a Bar. Paper presented at Agent 2006, Chicago, IL. [PDF]
  • Rand, W. & Wilensky, U. (2006). NetLogo 3.1: Low Threshold, No Ceiling. Paper presented at NAACSOS 2006, South Bend, IN, June 2006.
  • Rand, W. & Wilensky, U. (2006). Verification and Validation through Replication: A Case Study Using Axelrod and Hammond's Ethnocetnrism Model. Paper presented at NAACSOS 2006, South Bend, IN, June 2006. [PDF]
  • Rand, W., Blikstein, P. & Wilensky, U. (2006). Widgets, Planets, and Demons: the Case for the Integration of Human, Embedded, and Virtual Agents via Mediation. Paper presented at Swarmfest 2006, South Bend, IN, June 2006. [PDF]
  • Richiardi, M., Leombruni, R., Saam, N., & Sonnessa, M. (2006). A common protocol for agent-based social simulation. Journal of Artificial Societies and Social Simulation, 9(1).
  • Riggs, W. W. (2006, October). Agent-based modeling as constructionist pedagogy: An alternative teaching strategy for the social sciences. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1417-1423). Association for the Advancement of Computing in Education (AACE).
  • Roberts, B. (2006). Blocked exit syndrome: A serious risk in venue emergencies. Fire & Safety Magazine, Fall 2006.
  • Thorne, B., Bailey, A, Benedict, K., & Peirce-Cottler, S. (2006). Modeling blood vessel growth and leukocyte extravasation in ischemic injury: an integrated agent-based and finite element analysis approach. Journal of Critical Care, 21(4), 346. [HTML]
  • Unterman, J. & Wilensky, U. (2006). PANDA BEAR: Perimeter and area by embodied agent reasoning. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Wilensky, U. (2006). Complex systems and restructuration of scientific disciplines: Implications for learning, analysis of social systems, and educational policy. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA, April 7-11.
  • Wilensky, U. (2006). Promoting ABM literacy: implications for design, scientific content and education. Paper presented at Agent 2006, Chicago, IL.
  • Wilensky, U. & Abrahamson, D. (2006). Is a disease like a lottery?: Classroom networked technology that enables student reasoning about complexity. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Wilensky, U. & Reisman, K. (2006). Thinking Like a Wolf, a Sheep or a Firefly: Learning Biology through Constructing and Testing Computational Theories -- an Embodied Modeling Approach. Cognition & Instruction, 24(2), pp. 171-209. [PDF]
  • Wilensky, U., & Papert, S. (2006). Restructurations: Reformulations of Knowledge Disciplines through a change in representational forms. Unpublished working paper. Evanston, IL. Center for Connected Learning and Computer-Based Modeling. Northwestern University.
  • Xie, Q., & Tinker, R. (2006). Molecular Dynamics Simulations of Chemical Reactions for Use in Education. Journal of Chemical Education, 83(1), 77. [PDF]

2005

  • Sengupta, P. & Wilensky, U. (2005). N.I.E.L.S: An emergent multi-agent based modeling environment for learning physics. Paper presented at the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), Utrecht, Netherlands.
  • Abraham, R., Miller, M., & Miller, J. (2005). Emerging 4D graphics for math and science education. Paper presented at the ACM SIGGRAPH 2005 Educators program, Los Angeles, California. [PDF]
  • Abrahamson, D. & Wilensky, U. (2005). Piaget? Vygotsky? I'm game: Agent-based modeling for psychology research. Paper presented at the annual meeting of the Jean Piaget Society. Vancouver, Canada, June 1-3. [PDF]
  • Abrahamson, D. & Wilensky, U. (2005). ProbLab goes to school: Design, teaching, and learning of probability with multi-agent interactive computer models. Paper presented at the Fourth Conference of the European Society for Research in Mathematics Education, San Feliu de Guixols, Spain. [PDF]
  • Abrahamson, D. & Wilensky, U. (2005). Understanding chance: From student voice to learning supports in a design experiment in the domain of probability. In G.M. Lloyd, M. Wilson, J. L. M. Wilkins & S.L. Behm (Eds.), Proceedings of the Twenty Seventh Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education.Roanoke, VA. [PDF]
  • Abrahamson, D., & Wilensky, U. (2005). Collaboration and equity in classroom activities using Statistics As Multi-Participant Learning-Environment Resource (S.A.M.P.L.E.R.). In W. Stroup and U. Wilensky (Chairs), & C. D. Lee (Discussant), "Patterns in group learning with next-generation network technology." Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 11 - 15. [PDF]
  • Abrahamson, D., & Wilensky, U. (2005). The stratified learning zone: Examining collaborative-learning design in demographically-diverse mathematics classrooms. In D. Y. White (Chair) & E. H. Gutstein (Discussant), "Equity and diversity studies in mathematics learning and instruction." Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 11 - 15
  • Abrahamson, D., Blikstein, P., Lamberty, K. K. & Wilensky, U. (2005). Mixed-media learning environments. Paper presented at the annual meeting of Interaction Design and Children 2005, Boulder, Colorado.
  • Agar, M. (2005). Agents in living color: Towards emic agent-based models. Journal of Artificial Societies and Social Simulation, 8(1).
  • Aldinger, T., Kopf, S., Scheele, N., & Effelsberg, W. (2005). Participatory Simulation of a Stock Exchange. [PDF]
  • Amos, M., & Wood, A. (2005). Effect of door delay on aircraft evacuation time. Arxiv preprint cs.MA/0509050. [PDF]
  • Banos, A. Godara, A., & Lassarre, S. (2005). Simulating pedestrians and cars behaviours in a virtual city: an agent-based approach. Paper presented at the European Conference on Complex Systems, Paris, 14-18 November. [PDF]
  • Barton, A. (2005). Modelling the Foraging Patterns of a Colony of Co-operating Army Ant Agents using NetLogo. NRC/ERB-1122. February 21, 2005. [PDF]
  • Beer, M. D., & Hill, R. (2005). Integrating Multi-Agent Systems into the Wider Computing Curriculum. Paper presented at the The AAMAS-2005 Workshop on Teaching Multi-Agent Systems - TeachMAS Utrecht, the Netherlands.
  • Berland, M., & Wilensky, U. (2005). Complex play systems -- Results from a classroom implementation of VBOT. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 11 - 15.
  • Blikstein, P. & Wilensky, U. (2005). Less is more: Agent-based simulation as a powerful learning tool in materials science. Paper presented at the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), Utrecht, Netherlands. [PDF]
  • Blikstein, P., Abrahamson, D. & Wilensky, U. (2005). NetLogo: Where we are, where we're going. Paper presented at the annual meeting of Interaction Design and Children. Boulder, Colorado. [PDF]
  • Cace, I., & Bryson, J. (2005). Agent Based Modelling of Communication Costs: Why information can be free. In Emergence and Evolution of Linguistic Communication C. Lyon, C. L Nehaniv and A. Cangelosi, eds., pp. 305-322, Springer 2007. [PDF]
  • Calvez, B., & Hutzler, G. (2005). Parameter Space Exploration of Agent-Based Models. In Knowledge-Based Intelligent Information and Engineering Systems, 3684, 633-639. Springer Berlin / Heidelberg. [PDF]
  • Capizzo, M. C., Fazio, C., & Sperandeo-Mineo, R. M. (2005). Object based modelling environments applied to science education. Methods and Technologies for Learning, 34, 193.
  • Da Costa, L. E., & Terhesiu, D. (2005). A simple model for the diffusion of ideas. Research Project for the Complex Systems Summer School, SFI (Santa Fe, New Mexico). [PDF]
  • de Marchi, S. (2005). Computational and Mathematical Modeling in the Social Sciences. Cambridge University Press.
  • Forsyth, A. J., Horne, G. E., Upton, S. C., & Center, A. T. (2005). Marine Corps applications of data farming. Paper presented at the Proceedings of the 2005 Winter Simulation Conference. [PDF]
  • Gershenson, C. (2005). Self-Organizing Traffic Lights. Complex Systems, 16(1):29–53. [PDF]
  • Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist (second ed.). Milton, UK: Open University Press.
  • Gobert, J., Buckley, B., Dede, C., Levy, S., Slotta, J., & Wilensky, U. (2005). Technology features that support research through logging of student interactions with models. Paper presented at the Winter Text Conference, Jackson Hole, WY, January 20-23.
  • Gulyas, L., Bartha, S., Kozsik, T., Szalai, R., Korompai, A., & Tatai, G. (2005). The Multi-Agent Simulation Suite (MASS) and the Functional Agent-Based Language of Simulation (FABLES). Paper presented at Swarmfest2005, Torino, Italy, June 5-7, 2005. [PDF]
  • Hoffer, L. (2005). Using Agent-based Modeling to Better Understand Local Heroin Dealing. Organizations and Drug Markets, Washington University.
  • Janota, A., Rastocny, K., Zahradnik, J. (2005). Multi-agent approach to traffic simulation in NetLogo environment - Level Crossing model. Paper presented at the 5th International Conference Transport Systems Telematics TST '05, Silesian University of Technology. [PDF]
  • Janota, A., Spalek, J., & Hrbče J. (2005). NetLogo – prostredie na tvorbu multiagentových systémov a jeho využitie na simuláciu riadenia železničného priecestia. AT&P Journal, PLUS7.
  • Keshtkar, F., Gueaieb, W., & White, A. (2005). An Agent-based Model for Image Segmentation. Paper presented at the 13th Multi-disciplinary Iranian Researchers Conference in Europe (IRCE'2005), Leeds, United Kingdom, July 2005.
  • Khouadjia, M., Khanfouf, H., & Meshoul, S. (2005). Une Approche adaptative pour la segmentation dÕimages: Implementation sur la plate-forme Multi-agents NetLogo. Working Paper, Laboratoire LIRE, Université Mentouri, Constantine, Algérie.
  • Koehler, M., Tivnan, B., & Bloedorn, E. (2005). Generating Fraud: Agent Based Financial Network Modeling. Paper presented at the NAACSOS Conference 2005, South Bend, IN, June 26-28. [PDF]
  • Koehler, M., Tivnan, B., & Upton, S. (2005). Clustered Computing with NetLogo and Repast J: Beyond Chewing Gum and Duct Tape. Paper presented at the Agent2005 conference, Chicago, IL. [PDF]
  • Koper, R. (2005). Increasing learner retention in a simulated learning network using indirect social interaction. Journal of Artificial Societies and Social Simulation, 8(2).
  • Kopf, S., Scheele, N., Winschel, L., & Effelsberg, W. (2005). Improving Activity and Motivation of Students with Innovative Teaching and Learning Technologies. International Conference on Methods and Technologies for Learning (ICMTL). [PDF]
  • Kuhl, M. E., Steiger, N. M., Armstrong, F. B., & Joines, J. A. (2005). MARINE CORPS APPLICATONS OF DATA FARMING.
  • Lauberte, I. (2005). Using of cellular automata in agent-based simulation for regional development. Paper presented at the 6th Conference on Baltic Studies in Europe, Valmiera, June 17-19, pp. 99-104. [HTML]
  • Laver, M. (2005). Policy and the dynamics of political competition. American Political Science Review, 99(2), 263-281. [PDF]
  • Le, Q.B. (2005). Multi-agent system for simulation of land-use and land-cover change: a theoretical framework and its first implementation for an upland watershed in the Central Coast of Vietnam. Ecology and Development Series 29. Göttingen: Cuvillier Verlag.
  • Levy, S.T., & Wilensky, U. (2005). Students' patterns in exploring NetLogo models, embedded in the Connected Chemistry curriculum. In J. Gobert (Chair) and J. Pellegrino (Discussant), "Logging students' learning in complex domains: Empirical considerations and technological solutions." Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 11 - 15. [PDF]
  • Maroulis, S., & Wilensky, U. (2005). Modeling school districts as complex adaptive systems: A simulation of market-based reform. Paper presented at the 3rd Lake Arrowhead Conference on Human Complex Systems. Lake Arrowhead, CA, May 18-22.
  • Merks, R. & Glazier, J. (2005). A cell-centered approach to developmental biology. Physica A. 352(1), 1 July 2005, 113–130. [HTML]
  • Michel, F., Beurier, G., & Ferber, J. (2005). The TurtleKit Simulation Platform: Application to Multi-Level Emergence. First International Conference on Signal-Image Technology & Internet-Based Systems (Workshop Sessions), Hilton Hotel, Yaoundé, Cameroon, November 27th - December 1st, 2005. [PDF]
  • Morrison, D., & Dennis, B. (2005). MetaLab: supporting social grounding and group task management in CSCL environments through social translucence. Proceedings of the Proceedings of the 2005 conference on Diversity in computing (pp. 20-22). Albuquerque, New Mexico, USA: ACM. [PDF]
  • Muscalagiu, I., Horia-Emil, P., & Panoiu, M. (2005). Determining the Number of Messages Transmitted for the Temporary Links in the Case of ABT Family Techniques. Paper presented at the 7th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05), Timisoara, Romania September 25-29, 2005.
  • Nan, N., Johnston, E., Olson, J., & Bos, N. (2005). Beyond being in the lab: using multi-agent modeling to isolate competing hypotheses. Paper presented at the Conference on Human Factors in Computing Systems, Portland, OR. [HTML]
  • Ohshima, Y. (2005). Kedama: A GUI-Based Interactive Massively Parallel Particle Programming System. Visual Languages and Human-Centric Computing, 2005 IEEE Symposium on, 91-98.
  • Pereira, G. M. (2005). The effects of functional diversity in spatially distributed geographic domains. Paper presented at the Geocomputation 2005 conference. [PDF]
  • Rand, W., Brown, D., Riolo, R. & Robinson, D. (2005). Toward a graphical ABM toolkit with GIS integration. Paper presented at the Agent2005 Conference, Chicago, IL, October 13-14. [PDF]
  • Robertson, D. A. (2005). Agent-Based Modeling Toolkits NetLogo, RePast, and Swarm. Academy of Management Learning and Education, 4(4), 525-527. [PDF]
  • Schellinck, J., & White, T. (2005). Use of Netlogo as a rapid prototyping tool for the creation of more rigorous spatially explicit individual-based biological models. In First Open International Conference on Modeling & Simulation.
  • Shaker, A., Reeves, D.S. (2005). Self-Stabilizing Structured RingTopology P2P System. Paper presented at the Fifth IEEE International Conference on Peer-to-Peer Computing (P2P'05). [PDF]
  • Stieff, M., Bateman Jr., R. C., Uttal, D. (2005). Teaching and Learning with Three-dimensional Representations. In J. K. Gilbert (Ed.) Models and Modeling in Science Education (Vol 1, 93-120). Springer: Netherlands. [PDF]
  • Stirling, D. (2005). Modeling complex systems. Paper submitted to the Advanced International Colloquium on Building the Scientific Mind. [PDF]
  • Vidal, J. M. (2005). NetLogo for Building Prototype Multiagent Systems. [PDF]
  • Wheeler, S. (2005). It pays to be popular: A study of civilian assistance and guerilla warfare. Journal of Artificial Societies and Social Simulation, 8(4).
  • Wheeler, S. (2005). On the Suitability of NetLogo for the Modelling of Civilian Assistance and Guerrilla Warfare. DSTO Science Systems Laboratory. [PDF]
  • Xie, C. (2005). Molecular Dynamics Simulations Beyond the Lennard-Jones Particles. Submitted to the American Journal of Physics. [PDF]

2004

  • Buckley, B.C., Gobert, J.D., Kindfield, A., Horwitz, P., Tinker, R., Gerlits, B., Wilensky, U., Dede, C., & Willett, J. (2004). Model-based Teaching and Learning with BioLogica™: What do they learn? How do they learn? How do we know? Journal of Science Education and Technology, 13(1), 23-41. [PDF]
  • Levy, S.T., & Wilensky, U. (2004). Making sense of complexity: Patterns in forming causal connections between individual agent behaviors and aggregate group behaviors. In U. Wilensky (Chair) and S. Papert (Discussant), "Networking and complexifying the science classroom: Students simulating and making sense of complex systems using the HubNet networked architecture." Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12 - 16.
  • Levy, S.T., Kim, H., & Wilensky, U. (2004). Connected Chemistry - A study of secondary students using agent-based models to learn chemistry. In J. Gobert (Chair) and N. H. Sabelli (Discussant), "Modeling Across the Curriculum (MAC): Technology, Pedagogy, Assessment, & Research." Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12 - 16. [PDF]
  • Abrahamson, D. (2004). Embodied spatial articulation: A gesture perspective on student negotiation between kinesthetic schemas and epistemic forms in learning mathematics. In D. E. McDougall and J. A. Ross (Eds.), Proceedings of the Twenty Sixth Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education Vol. 2 (pp. 791 - 797). Windsor, Ontario: Preney. [PDF]
  • Abrahamson, D., & Wilensky (2004). ProbLab: A computer-supported unit in probability and statistics. In M.J. Hoines & A.B. Fuglestad (Eds.), Proceedings of the 28th Annual Meeting of the International Group for the Psychology of Mathematics Education Vol. 1 (p. 369). Bergen: Bergen University College. [PDF]
  • Abrahamson, D., & Wilensky, U. (2004). S.A.M.P.L.E.R.: Statistics As Multi-Participant Learning-Environment Resource. In U. Wilensky (Chair) and S. Papert (Discussant), "Networking and complexifying the science classroom: Students simulating and making sense of complex systems using the HubNet networked architecture." Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12 - 16. [PDF]
  • Abrahamson, D., & Wilensky, U. (2004). SAMPLER: Collaborative interactive computer-based statistics learning environment. In the Proceedings of the 10th International Congress on Mathematical Education, Copenhagen, July 4 - 11, 2004. http://www.icme-organisers.dk/tsg11/. [PDF]
  • Abrahamson, D., Berland, M.W., Shapiro, R.B., Unterman, J.W., & Wilensky, U. (2004). Leveraging epistemological diversity through computer-based argumentation in the domain of probability. In Y. B. Kafai, W. A. Sandoval, N. Enyedy, A. S. Nixon, & F. Herrera (Eds.), Proceedings of The Sixth International Conference of the Learning Sciences (pp. 28 - 35). Mahwah NJ: Lawrence Erlbaum Associates. [PDF]
  • Achorn, E. (2004). Integrating Agent-Based Models with Quantitative and Qualitative Research Methods. [PDF]
  • Agar, M. (2004). Agents in Living Color: Towards Emic Agent-Based Models. Journal of Artificial Societies and Social Simulation (JASSS), 8 (1): 4. [HTML]
  • Agar, M. (2004). An Anthropological Problem, A Complex Solution. Human Organization, 63(4), 411-418. [PDF]
  • An, G. (2004). In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling. Critical care medicine, 32(10), 2050-2060.
  • Aschwanden, P. (2004). Spatial Simulation Model for Infectious Viral Diseases with Focus on SARS and the Common Flu. In Proceedings of the 37th Hawaii International Conference on System Sciences, January 2004. [PDF]
  • Barry, P., & Koehler, M. (2004). Simulation in context; using data farming for decision support. Simulation Conference, 2004. Proceedings of the 2004 Winter, 1. [PDF]
  • Beauchemin, C., & Liao, L. (2004). Tutorial on agent-based models in NetLogo.
  • Berland, M., & Wilensky, U. (2004). Virtual robotics in a collaborative constructionist learning environment. In U. Wilensky (Chair) and S. Papert (Discussant), "Networking and complexifying the science classroom: Students simulating and making sense of complex systems using the HubNet networked architecture." Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12 - 16.
  • Blikstein, P., & Wilensky, U. (2004). MaterialSim: an agent-based simulation toolkit for Materials Science learning. Paper presented at the International Conference on Engineering Education, Gainesville, Florida. [PDF]
  • Bloomquist, K. (2004). Modeling Taxpayers Response to Compliance Improvement Alternatives. Paper presented at the Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), Pittsburgh, PA.
  • Bourjot, C., & Chevrier, V. (2004). A Platform for the analysis of artificial self-organized systems. 2004 IEEE International Conference on Advances in Intelligent Systems-Theory and Applications-AISTA. [PDF]
  • Bryson, J. (2004). Action Selection and Individuation in Agent Based Modelling. [PDF]
  • Cacciaguerra, S., Roccetti, M., Roffilli, M., & Lomi, A. (2004). A Wireless Software Architecture for Fast 3D Rendering of Agent-Based Multimedia Simulations on Portable Devices. Paper presented at the First Consumer Communications and Networking Conference (CCNC), IEEE Communications Society, Las Vegas, Nevada (USA), January 2004. [PDF]
  • Chitnis, A.B. & Itoh, M. (2004). Exploring alternative models of rostral-caudal patterning in the zebrafish neurectoderm with computer simulations. Current Opinion in Genetics & Development 14(4), 415-421.
  • Densmore, O. (2004). NetLogo 2.0: Graphs, Nodes, and Edges Oh My! January 12, 2004, O'Reilly.com.
  • Friedman, D., & Abraham, R. (2004). Landscape Dynamics and Conspicuous Consumption. Paper presented at the 2004 Proceedings of the Society for Dynamic Games. [PDF]
  • Goldstone, R. L. (2004). The complex systems see-change in education. Journal of the Learning Sciences, 15 (1), pp 35 - 43. [PDF]
  • Gross, T. S., Poliachik, S. L., Ausk, B. J., Sanford, D. A., Becker, B. A., & Srinivasan, S. (2004). Why rest stimulates bone formation: a hypothesis based on complex adaptive phenomenon. Exerc Sport Sci Rev, 32(1), 9-13. [PDF]
  • Henein, C.M., & White, T. (2004). Agent-based modelling of forces in crowds. In Lecture Notes in Computer Science Vol. 3415, 2005. [PDF]
  • Ingalls, R. G., Rossetti, M. D., Smith, J. S., & Peters, B. A. (2004). Simulation in context: Using data farming for decision support. Paper presented at the 2004 Winter Simulation Conference. [PDF]
  • Kottonau, J. (2006, based on 2004 paper). Simulation einer Ameisenstraße mit NetLogo. Lehrer-online. [HTML]
  • Le, Q. B., Park, S., & Vlek, P. L. G. (2004). Simulating Spatial Patterns of Land-use and Land-cover Change: A Multi-agent Model and its Application to an Upland Watershed in Central Vietnam. In K. J. Peters, D. Kirschke, W. Manig, A. Bürkert, R. Schultze-Kraft, L. Bharati, C. Bonte-Friedheim, A. Deininger, N. Bhandari, H. Weitkamp (Eds.), "Rural Poverty Reduction through Research for Development and Transformation" - Proceedings of the Deutscher Tropentag 2004 (p. 67). Berlin: Humboldt-Universität. [PDF]
  • Lechner, T., Watson, B., Wilensky, U., & Felsen, M. (2004). Procedural modeling of land use in cities. Technical report NWU-CS-04-38. Evanston, IL: Northwestern University, Computer Science department. [PDF]
  • Miller, J.H., & Page, S.E. (2004). The Standing Ovation Problem. Complexity 9(5), pp 8-16. [HTML]
  • Newman, Chris. "Revolution by Osmosis: A Case Study of West Florida, Texas, California and Hawaii." Proceedings of the Fifth Annual International Conference on Complex Systems, May, 2004.
  • Prodromou, T. (2004). Distribution as emergent phenomena. Proceedings of the British Society for Research into Learning Mathematics, 24(1), 49-54. [PDF]
  • Sallez, Y., Berger, T., & Tahon, C. (2004). Simulating intelligent routing in flexible manufacturing systems using NetLogo. Industrial Technology, 2004 IEEE International Conference.
  • Sancho-Royo, A., Pelta, D. A., Verdegay, J. L., & Gonzalez, J. R. (2004). Evaluation of Cooperative Strategies in Optimization Problems. [PDF]
  • Schellinck, J., & White, T. (2004). Use of NetLogo as a rapid prototyping tool for the creation of more rigorous spatially explicit individual-based biological models. First Open International Conference on Modeling & Simulation.
  • Singh, A., & Haahr, M. (2004). Topology Adaptation in P2P Networks Using Schelling's Model. Proceedings of the Workshop on Games and Emergent Behaviors in Distributed Computing Environments (Birmingham, UK, Sept. 2004). [PDF]
  • Solberg, J. (2004). Assemble time for self-assembling square tiles. Project for MECH 448. [PDF]
  • Tisue, S., & Wilensky, U. (2004). NetLogo: A simple environment for modeling complexity. Paper presented at the International Conference on Complex Systems, Boston, May 16 - 21. [PDF]
  • Tisue, S., & Wilensky, U. (2004). NetLogo: Design and implementation of a multi-agent modeling environment. Paper presented at SwarmFest, Ann Arbor, MI, May 9 - 11. [PDF]
  • Tisue, S., & Wilensky, U. (2004). NetLogo: Design and Implementation of a Multi-Agent Modeling Environment. Paper presented at the Agent2004 Conference, Chicago, IL. (This is a combined, revised, and updated version of our ICCS and SwarmFest papers from earlier this year.) [PDF]
  • Tobias, R. & Hofmann, C. (2004). Evaluation of free Java-libraries for social-scientific agent based simulation. Journal of Artificial Societies and Social Simulation, 7(1).
  • Vag, A. (2004). First generation multi-agent models and their upgrades. Interdisciplinary Description of Complex Systems, 2(1), 95-103. [PDF]
  • Vidal, J. M., Buhler, P., & Goradia, H. (2004). The Past and Future of Multiagent Systems. Paper presented at the AAMAS Workshop on Teaching Multi-Agent Systems. [PDF]
  • Winschel, L., & Kopf, S. (2004). Entwicklung einer Börsensimulation mit der multiagentenbasierten Entwicklungsumgebung NetLogo. Universität Mannheim / Institut für Informatik. [PDF]
  • Wokoma, I., Sacks, L., & Marshall, I. (2004). A Self-Organising Clustering Algorithm for Wireless Sensor Networks. University College, London. [DOC]

2003

  • Abrahamson, D., & Wilensky, U. (2003). The quest of the bell curve: A constructionist approach to learning statistics through designing computer-based probability experiments. In M. A. Mariotti (Ed.), Proceedings of the Third Conference of the European Society for Research in Mathematics Education. Pisa, Italy: University of Pisa. Retrieved June 1, 2009, from http://www.dm.unipi.it/didattica/CERME3/proceedings/Groups/TG5/TG5_abrahamson_cerme3.pdf. [PDF]
  • Agar, M. (2003). My Kingdom for a Function: Modelling Misadventures of the Innumerate. Journal of Artificial Societies and Social Simulation, 6(3). [HTML]
  • Ausk, B. J., Poliachik, S. L., Gross, T. S., & Srinivasan, S. (2003). Synchronous signaling within the osteocyte cell network underlies the osteogenic potency of rest-inserted loading. In 27th Meeting of the American Society for Biomechanics, Toledo, OH.
  • Bouquet, P., Busetta, P., Adami, G., Bonifacio, M., & Palmieri, F. (2003). K-Trek: A Peer-to-Peer Infrastructure for Distributing and Using Knowledge in Large Environments. Paper presented at the WOA 2003: dagli Oggetti agli Agenti Sistemi Intelligenti e Computazione Pervasiva, Cagliari, Italy. [PDF]
  • Driscoll, M.P. (2003). How people learn (and what technology might have to do with It). ERIC Digest. [PDF]
  • Evans, D., Heuvelink, A., & Nettle, D. (2003). The evolution of optimism: A multi-agent based model of adaptive bias in human judgement. Proceedings of the AISB'03 Symposium on Scientific Methods for the Analysis of Agent-Environment Interaction, University of Wales, pp.20-25. [PDF]
  • Fell, A. (2003). A Study of Modeling Crowd Dynamics. Final year project in Compter Science, Carleton University. [PDF]
  • Gobert, J., Horwitz, P., Tinker, R., Buckley, B., Wilensky, U., Levy, S. T. & Dede, C. (2003). Modeling across the curriculum: Scaling up modeling using technology. Paper presented at the Twenty-Fifth Annual Meeting of the Cognitive Science Society, Boston, MA, July 31 - August 2. [PDF]
  • Goldstone, R. (2003). "Complex Adaptive Systems" psychology course at Indiana University. Spring 2003. Syllabus: [HTML]
  • Hills, L. (2003). A Thin Red Line Between Red and Green: Emerging Patterns in Group Preference an Exploration at Bryn Mawr College. Senior Thesis, Dartmouth College.
  • Horwitz, P., Gobert, J., Wilensky, U., & Dede, C. (2003). MAC: A longitudinal study of modeling technology in science classrooms. Paper presented at the National Educational Computing Conference (NECC), Seattle, WA.
  • InfoWorld. (2003). Experts: U.S. needs better terror models. Investigative report. [HTML]
  • Kline, G. (2003). Campers at UI have fun, G.A.M.E.S. News-Gazette. August 23, 2003. [HTML]
  • Korman, M. J. (2003). LiveLetters: Writing with Emergence. [HTML]
  • Lomi, A., & Cacciaguerra, S. (2003, April). Organizational decision chemistry on a lattice. In proc. of the 7th Annual Swarm Users/Researchers Conference.
  • Lomi, A., & Cacciaguerra, S. (2003). Decision Chemistry Part II: The Emergence of Routines. [HTML]
  • Longo, D., Peirce, S., Skalak, T., Marsden, M., Davidson, L., Dzamba, B., et al. (2003). Computational automata simulation of blastocoel roof thinning in the Xenopu laevis embryo. Systems and Information Engineering Design Symposium, 2003 IEEE, 127-131. [PDF]
  • Manhart, K. (2003). Schildkrštenwelten - Multi-Agenten-Simulationen mit NetLogo und StarLogo. C'T, 2003(25), 232-237.
  • Stieff, M., & Wilensky, U. (2003). Connected Chemistry - incorporating interactive simulations into the chemistry classroom. Journal of Science Education and Technology, 12(3), 285-302. [HTML]
  • Turcsanyi-Szabo, M. (2003). Practical Teacher Training Through Implementation of Capacity Building Internet Projects. Proceedings of Society for Information Technology and Teacher Education Conference, Albuquerque, 2003, 1564-1571. [PDF]
  • Wilensky, U. (2003). Statistical mechanics for secondary school: The GasLab modeling toolkit. International Journal of Computers for Mathematical Learning, 8(1), 1-41 (special issue on agent-based modeling). [PDF]
  • Wilensky, U., & Shapiro, B. (2003). Networked Participatory Simulations: Classroom Collaboration in Exploring the Dynamics of Complex Systems. Proceedings of the International Conference of the Learning Sciences, Oslo, Norway.
  • Wilensky, U., & Stroup, W. (2003). Embedded complementarity of object-based and aggregate reasoning in students developing understanding of dynamic systems. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL, April 1-5.
  • Wilensky, U., & Stroup, W. (2003). Participatory Simulations guide for Computer-HubNet. Evanston, IL, Center for Connected Learning and Computer Based Modeling, Northwestern University. (Updated 2004, 2005) [PDF]

2002

  • Abrahamson, D., & Wilensky, U. (2002). Statistics as situated probability: The design and implementation of S.A.M.P.L.E.R. Unpublished manuscript.
  • Abrahamson, D., Berland, M., Shapiro, R.B., Unterman, J., & Wilensky, U. (2002). Collaborative interpretive argumentation as a phenomenological-mathematical negotiation: A case of statistical analysis of a computer simulation of complex probability. Unpublished manuscript. [PDF]
  • Bull, G., Bell, R., Garofalo, J., & Sigmon, T. (2002). Learner-Based Tools: The Case for Open Source Educational Software. Learning & Leading with Technology, 30(2), 10-17.
  • Fazio, C., Sperandeo-Mineo, R. M., & Tarantino, G. (2002). Mathematical representation of real systems: Two modelling environments involving different learning strategies. [PDF]
  • Gilbert, N. (2002). Varieties of Emergence In M. J. North, C. M. Macal & D. L. Sallach (Eds.), Proceedings of the Agent 2002 Conference on Complex Interaction and Social Emergence (pp. 41-50). IL: Argonne National Laboratory and Northwestern University. [PDF]
  • Gilbert, N. a. B., S. (2002). Platforms and methods for agent-based modeling Paper presented at the National Academy of Sciences of the United States of America.
  • Impullitti, G., & Rebmann, C. M. (2002). An agent-based model of wealth distribution (No. 2002-15). Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
  • Johnson, N. L. (2002). The development of collective structure and its response to environmental change.
  • Lehmann, H., Wang, J., & Bryson, J. (2002). Tolerance and sexual attraction is despotic societies: A replication and analysis of Hemelrijk. Artificial models of natural Intelligence. UK. [PDF]
  • MSCP Project Summary. [HTML]
  • North, M.J., & Burkhart, R.M. (2002). Agent-Based Methods, Toolkits, and Techniques In M. J. North, C. M. & D. L. Sallach (Eds.), Proceedings of the Agent 2002 Conference on Complex Interaction and Social Emergence (pp. 3-10). IL: Argonne National Laboratory and University of Chicago. [PDF]
  • Ratto, M., Shapiro, R.B., Truong, T., & Griswold, W. (2002). The ActiveClass Project: Experiments in encouraging classroom participation. Unpublished manuscript.
  • Stieff, M., & Wilensky, U. (2002). ChemLogo: An emergent modeling environment for teaching and learning chemistry. Paper presented at the Fifth Biannual International Conference of the Learning Sciences, Seattle, WA, October. [HTML]
  • Stroup, W., Kaput, J., Ares, N., & Wilensky, U. (2002). The nature and future of classroom connectivity: The dialectics of mathematics in the social space. Paper presented at the Psychology of Mathematics Education conference, Atlanta, GA, October. [PDF]
  • Wilensky, U., & Stroup, W. (2002). Participatory Simulations guide for Calculator-HubNet. Evanston, IL, Center for Connected Learning and Computer Based Modeling, Northwestern University. (Updated 2003, 2004, 2005)
  • Wilensky, U., & Stroup, W. (2002). Participatory Simulations: Envisioning the networked classroom as a way to support systems learning for all. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA, April 13. [PDF]
  • Yang, C. K., & Wilensky, U. (2002). School Effects Reinterpreted from the Bottom-Up: Merging Statistical Methods and Agent-based Modeling.

2001

  • An, G. (2001). Agent-based computer simulation and sirs: building a bridge between basic science and clinical trials. Shock, 16(4), 266-273.
  • Kahn, K. (2001). ToonTalk and Logo. Paper presented at Eurologo 2001. [PDF]
  • Neuwirth, E. (2001). Turtle Ballet: Simulating Parallel Turtles in a Nonparallel LOGO Version. Paper presented at the Eurologo 2001. [PDF]
  • Tam, P-W. (2001). "E-Commerce (A Special Report): The Classroom - Tools of the future: Thanks to Technology, K-12 will never look the same." Wall Street Journal (Eastern edition). New York, New York. March 12, 2001. p. R28.
  • Wilensky, U. (2001). Embodied Learning: Students Enacting Complex Dynamic Phenomena with the HubNet Architecture. Paper presented at the annual meeting of the American Educational Research Association. Seattle, WA. [PDF]
  • Wilensky, U. (2001). Emergent Entities and Emergent Processes: Constructing Emergence through Multi-agent programming. Paper presented at the annual meeting of the American Educational Research Association. Seattle, WA. [HTML]
  • Wilensky, U. (2001). Modeling nature's emergent patterns with multi-agent languages. Paper presented at EuroLogo 2001. Linz, Austria. [HTML]

2000

  • Wilensky, U., Hazzard, E., & Longenecker, S. (2000). A Bale of Turtles: A Case Study of a middle school science class studying complexity using StarLogoT. Paper presented at the meeting of the Spencer Foundation, New York, New York, October 11-13, 2000.
  • Centola D., McKenzie E., & Wilensky U. (2000). Survival of the groupiest: Facilitating students' understanding of multi-level evolution through multi-agent modeling - The EACH Project. In Proceedings of the Fourth International Conference on Complex Systems. Nashua, NH: New England Complex Systems Institute, and InterJournal Complex Systems, 377 [PDF]
  • Centola D., Wilensky U., & McKenzie E. (2000). A hands-on modeling approach to evolution: Learning about the evolution of cooperation and altruism through multi-agent modeling - The EACH Project. Proceedings of the Fourth Annual International Conference of the Learning Sciences, Ann Arbor, MI, June 14-17. [PDF]
  • Gilbert, N. T., P. (2000). How to build and use agent-based models in social science. Mind and Society, 1(1), 57-72.
  • Wilensky, U. (2000) Modeling Emergent Phenomena with StarLogoT. @CONCORD.org, Winter 2000.
  • Wilensky, U., & Centola, D. (2007, based on 2000 paper). Simulated Evolution: Facilitating Students' Understanding of the Multiple Levels of Fitness through Multi-Agent Modeling. Paper presented at the Evolution Challenges conference, Phoenix, AZ, November 1-4. [PDF]
  • Wilensky, U., Stroup, W. (2000). Networked gridlock: Students enacting complex dynamic phenomena with the HubNet architecture. Proceedings of the Fourth Annual International Conference of the LearningSciences, Ann Arbor, MI, June 14 - 17. [HTML]

1999

  • Berland, M., & Charniak, E. (1999). Finding parts in very large corpora. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL-99), College Park, MD, 57-64.
  • Stroup, W., & Wilensky, U. (1999). Assessing learning as emergent phenomena: Moving constructivist statistics before the individual and beyond the Bell-curve. In A.E. Kelly & R. Lesh (Eds.), Research in Mathematics and Science Education. Englewood, NJ: Lawrence Erlbaum Associates.
  • Wilensky, U. (1999). GasLab: An extensible modeling toolkit for exploring micro- and macro- views of gases. In N. Roberts, W. Feurzeig, & B. Hunter (Eds.), Computer modeling and simulation in science education (pp. 151-178). Berlin: Springer Verlag. [HTML]
  • Wilensky, U., & Resnick, M. (1999). Thinking in Levels: A Dynamic Systems Perspective to Making Sense of the World. Journal of Science Education and Technology, 8(1). [PDF]
  • Wilensky, U., & Stroup, W. (1999). Learning through participatory simulations: Network-based design for systems learning in classrooms. Proceedings of Computer Supported Collaborative Learning (CSCL'99). Stanford, CA, December 12 - 15. [PDF]
  • Wilensky, U., & Stroup, W. (1999). Participatory Simulations: Networked-based Design for Systems Learning in Classrooms. In R. Nikolov, E. Sendova, & I. Nikolova (Eds.). Presented at the PI meeting of the National Science Foundation, EHR division, June 3 - 4. [PDF]
  • Wilensky, U., Hazzard, E & Froemke, R. (1999). GasLab: An Extensible Modeling Toolkit for Exploring Statistical Mechanics. Paper presented at the Seventh European Logo Conference - EUROLOGO '99, Sofia, Bulgaria. [PDF]
Products
NetLogo DesktopNetLogo WebTurtle UniverseNetTangoHubNet Web
Documentation
NetLogo User ManualNetLogo DictionaryBeginner's Interactive NetLogo DictionaryNetLogo Source Code
Learn
TutorialsVideo TutorialsArticles and GuidesTextbook by creator of NetLogo - An Introduction to Agent-based Modeling
Community
NetLogo ForumNetLogo Google GroupModeling CommonsGithubPublications using NetLogoOther Community and ResourcesNetLogo Conference 2026
About
About NetLogoContactFAQ