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

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2025

  • Agustín, V. (2025). The Future as Origin.
  • Al-Jaberi, L. A. (2025). Exploring New Energy Solutions in Kongsvinger using AI Methods (Master's thesis, Oslo Metropolitan University).
  • 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.
  • Alkaher, D., Frenkel, M., Taylor, E., & Bengo, Y. Tug of War: Modeling Innovation Contagion and Resistance in Public Sector Organizations. Available at SSRN 5225828.
  • 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.
  • 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.
  • 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.
  • 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.
  • Araújo, E. (2025). Uso da modelagem baseada em agentes no estudo de sistemas complexos. Revista Brasileira de Ensino de Física, 47.
  • Assaad, L., Fuchs, R., Phillips, K., Schöppl, K., & Hahn, U. (2025). Capturing Argument in Agent-Based Models. Topoi, 1-19.
  • 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.
  • Banerjee, S. Towards a taxonomy of life-like systems: An information theoretic view of life.
  • Barnard, L. N. Using agent based modelling to determine behavioural rules in host location by Cotesia parasitoids (in environments with competitors and nonhosts).
  • 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, (Accepted Articles).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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. Emotional Dynamics and Engagement Cycles in Swiping Dating Apps: An Agent-Based Modeling Approach. Available at SSRN 5238593.
  • 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., & Šiljak, H. (2025). Strategies for decentralised UAV-based collisions monitoring in rugby. arXiv preprint arXiv:2503.22757.
  • 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).
  • Dawud, T., Gok, S., Wilensky, U., & Levy, S. T. (2025). Dynamic Equilibrium in Science Education: Cross-Disciplinary Perspectives and Representations in Israel and United States Standards and Textbooks. In Proceedings of the 19th International Conference of the Learning Sciences-ICLS 2025, pp. 1043-1051. International Society of the Learning Sciences.
  • de Tarlé, L. D., Michelini, M., Šešelja, D., & Straßer, C. (2025). Argumentative Agent-Based Models.
  • 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.
  • 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.
  • 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.
  • Etemadidavan, S., & Collins, A. J. (2025). Agent-based model for multiple team membership: implications for system design. International Journal of Organization Theory & Behavior.
  • Feliciani, T. (2025). Divided Spaces and Divided Opinions: Modeling the Impact of Residential Segregation on Opinion Polarization.
  • 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.
  • 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.
  • 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.
  • Gauvin, L., & Randon-Furling, J. (2025). Segregation in cities. Compendium of Urban Complexity, 137.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Johannessen, M., Larsen, K. D., & Andresen, M. L. (2025). Motivasjon i en cyberkrig-En litteraturstudie med simuleringsbasert analyse av forsvars-og angrepsaktører i CyberAIMs (Bachelor's thesis, NTNU).
  • 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
  • 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.
  • 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).
  • 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.
  • 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.
  • Li, H., Huan, J., & Xie, J. (2025). Research on the evolution law of supply chain disruption based on Complex Adaptive System. Flexible Services and Manufacturing Journal, 1-20
  • 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, 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.
  • Liu, L. (2025). 12 Agent-Based Modeling and. Urban Human Mobility: Practices, Analytics, and Strategies for Smart Cities, 138.
  • Liu, S., Li, C., He, Q., Men, B., Wu, D., Hai, R., ... & 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.
  • Luis, M., Harper, A., Monks, T., & Mustafee, N. NETLOGOPY: UNLOCKING ADVANCED SIMULATION AND INTEGRATION FOR NETLOGO USING PYTHON.
  • 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.
  • Macke, S., Sadiq, A. O., Lyons, M., & Swahn, M. (2025). Modeling Homelessness: An Agent-Based Simulation of Social Dynamics and Housing Instability.
  • 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.
  • Malik, M. M. S3. 2.4: Introduction to Computational and Data Science in the Social Sciences.
  • Manseau, J. (2025). Understanding The Effects Of AI Tasks And Social Deskilling: An Agent-Based Model.
  • Marisetty, V., van Heeswijk, W., & Narayanan, A. An Agent-Based Model of Rumor-Induced Volatility in Financial Markets. Available at SSRN 5375758.
  • 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.
  • Njegovanovic, A., & Flodin, S. (2025). A Comparison of the Crowd Simulators NetLogo and Pathfinder in Arena Evacuation Situations.
  • 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.
  • 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.
  • 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.
  • 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.
  • Özkök, M. K. (2025). What A Really Small World: A Simulation Model for the Formation and Patterns of Social Networks in Urban Environments.
  • 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.
  • 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.
  • 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, 115769.
  • Patten, S. B. (2025). Visualization of a dimensional model of depression epidemiology.
  • Pavanini, T. (2025). Rural Demand Responsive Transport: Current Developments and Analysis of a Case Study in an Italian Inner Area. Springer Nature.
  • 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.
  • Pleyer, J., & Fleck, C. (2025). cellular_raza: Cellular Agent-based Modeling from a Clean Slate. Journal of Open Source Software, 10(110), 7723.
  • 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.
  • 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).
  • Radisch, F. (2025). Accountability and Educational Improvement Arnoud Oude Groote Beverborg Tobias Feldhoff Katharina Maag Merki.
  • RAIMBAULT, J. (2025). Co-evolution in Urban Systems. Centralities and Hierarchies of Networks and Territories, 217.
  • Rana, V., & Singh, V. (2025). Designing Agent-Based Models: A Methodological Approach from Practical Insights and Lessons Learned from Case Study.
  • 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.
  • 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).
  • 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.
  • Ruesink, B., & Gronau, S. (2025). Forest resource management, refugee integration, and food security in rural Zambia: balancing sustainability and equity. Food Security, 1-27.
  • 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).
  • Sadeghi Foroushani, S. (2025). Harnessing sustainability mobile apps for carbon reduction: a multilevel approach (Doctoral dissertation, University of British Columbia).
  • Schnelle, C. K. (2025). AGENT-BASED SIMULATION OF ENERGY POVERTY: INSIGHTS INTO ENERGY JUSTICE AND EQUITY SOLUTIONS.
  • Schulz, A. W. (2025). Intraspecific economics: interpersonal utility comparisons, evolution and culture. Behavioural Public Policy, 1-22.
  • 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).
  • 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.
  • 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.
  • 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.
  • Souza, C. F. D. (2025). Ferramenta facilita o aprendizado de ciências para pessoas com deficiência visual. Jornal da Universidade, 15 maio 2025, n. 236.
  • Stephan, J., Fritzsch, S., & Weiss, G. N. (2025). Riding the Crypto Rollercoaster: Agent-Based Insights into Bitcoin’s Price Dynamics.
  • 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).
  • Thelin, M., & Helgesson, M. (2025). Comparative Analysis of Discrete-Event Simulation Programs.
  • 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.
  • 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.
  • 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 der Koogh, M. L. (2025). Dynamics of Charging.
  • 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.
  • 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.
  • 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, S., & Wang, Z. (2025). Modelling and algorithms of highway transportation network in urban agglomerations. Scientific Reports, 15(1), 28145.
  • 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.
  • 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.
  • 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.
  • Yousef, K., Yuce, B., & He, N. (2025). Renewable Energy Strategic Planning and Management: An Agent-Based Modeling and Simulation. Available at SSRN 5279357.
  • 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.
  • 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.
  • 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, 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.
  • 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.
  • 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.
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  • 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.
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2024

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  • 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.
  • 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. Efficient and Sensitive Proteomic Based Electrochemical Immunosensor Using Nio Thin Film Electrode for Early Identification of a Biomarker Unique to Breast Cancer (Ca 15-3) and Agent-Based Modelling for Effective Treatment Strategy. Available at SSRN 5010009.
  • 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., 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. Available at SSRN 5002412.
  • 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)).
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2021

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  • 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.
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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.
  • Bampoh, D. K., Earl, J. E., & Zollner, P. A. (2020). Simulating the relative effects of movement and sociality on the distribution of animal-transported subsidies. Theoretical Ecology, 1-14.
  • 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.
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  • 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.
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  • 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.
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  • Vidal-Cordasco, M., & Nuevo-López, A. Resilience and vulnerability to climate change in the Greek Dark Ages. Journal of Anthropological Archaeology, 61, 101239.
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  • 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.
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  • 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.
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  • Wang, A., & Chan, E. H. (2020). The impact of power-geometry in participatory planning on urban greening. Urban Forestry & Urban Greening, 48, 126571.
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  • 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.
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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]
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  • 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
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  • 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
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  • 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]
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  • 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]
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  • 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]
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  • 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
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  • 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]
  • 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
  • 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.
  • 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.
  • 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.
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  • 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]
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  • 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.
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  • 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.
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  • 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]
  • 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).
  • 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
  • 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]
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  • 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)

2013

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  • 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).
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  • 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.
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  • 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.
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  • Caillou, P., Gil-Quijano, J., & Zhou, X. (2013). Automated observation of multi-agent based simulations: a statistical analysis approach.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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)
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  • 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.
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  • Fioretti, G. (2013). Romulus-Catalin Damaceanu: Agent-based computational economics using netlogo.
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  • 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.
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  • 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.
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  • 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]
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  • Guerin, A. J., Jackson, A. C., & Youngson, A. F. (2013). An agent-based model of Atlantic salmon migration in Scottish coastal waters.
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  • 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).
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  • 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).
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  • 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]
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  • 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.
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  • 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.
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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]
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  • 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]
  • 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.
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  • 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]
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  • 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).
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  • Shi, X. (2011). Organisational Innovativeness and Diffusion of Innovation (Doctoral dissertation, University of York).
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  • 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.
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  • Varnakovida, P. Transportation Development: Agent-Based Modeling and Landscape Prediction of Urban Environment.
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  • 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.
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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.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
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