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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 netlogo-refs@ccl.northwestern.edu.

Bold = Publications authored by the CCL

In Press 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999

In Press

  • Moore, J.C., R.B. Boone, A. Koyama, and K. Holfeder. (In Press). Enzymatic and detrital influences on the structure, function, and dynamics of spatially-explicit model ecosystems. Biochemistry.
  • Nogare, D. D., Chitnis, A.B. (In Press). NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium
  • Weisberg, M. (in press).Understanding the Emergence of Population Behavior in Individual-Based Models. Philosophy of Science.
  • Zu, C., Zeng, H., Zhou, X. (in press).Computational Simulation of Team Creativity: the Benefit of Member Flow. Frontiers in Psychology.

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.
  • 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.
  • 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.
  • 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.
  • 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).
  • Alm, J., Gerbrands, P., & Kirchler, E. (2020). Using "responsive regulation" to reduce tax base erosion. Regulation & Governance.
  • 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.
  • 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.
  • Al Shamsi, A. A. (2020). School Auditorium Evacuation Simulation. International Journal of Information Technology and Language Studies, 4(2).
  • 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., Perez, L., & Dragićević, S. (2020). Investigating the Effects of Panethnicity in Geospatial Models of Segregation. Applied Spatial Analysis and Policy, 1-23.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Arden, R., Ruseno, N., & Hidayat, Y. A. (2020). CARGO OPTIMIZATION IN AN AIRLINE USING AGENT–BASED MODELLING.
  • 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.
  • 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 accepted 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.
  • Badham, J., Kee, F., & Hunter, R. F. 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.
  • 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.
  • 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., DABHOLKAR, S., & WILENSKY, U. (2020). Confronting Frame Alignment in CT Infused STEM Classrooms. CoolThink@ JC, 91.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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., 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.
  • 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).
  • 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.
  • 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.
  • 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, 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.
  • 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.
  • Calabrò, G., Inturri, G., Le Pira, M., Pluchino, A., & Ignaccolo, M. (2020). Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization. Transportation Research Procedia, 45, 234-241.
  • Calabrò, G., Torrisi, V., Inturri, G., & Ignaccolo, M. (2020). Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization. European Transport Research Review, 12, 1-11.
  • Camargo, P., Mattos, S., & Goldenberg, C. (2020, February). Complexity and Collective Intelligence on Demand for a Sustainable Future. In 2020 IEEE 14th International Conference on Semantic Computing (ICSC) (pp. 347-349). IEEE.
  • Camp, J., Nelson, K., Philip, C. E., Moravec, M., Scheffler, D. W., & Johnson, P. (2020). Utilizing Agent-Based Modeling to Evaluate Operational Impacts of an Incident and Possible Alternatives on US Waterways. Transportation Research Record, 0361198120941504.
  • Camparotti, C. E. S. (2020). Analysis of industrial symbiosis through agent-based simulation: application in the agro-industrial sector.
  • Campos, R. F. D. A., Cunha, D. A. D., & Bueno, N. P. (2020). Information dissemination in socio-ecological systems: Analysis of a hybrid model of System Dynamics and Agent-Based Modeling. Nova Economia, 30(1), 257-286.
  • Caprioli, C., Bottero, M., & De Angelis, E. (2020). Supporting Policy Design for the Diffusion of Cleaner Technologies: A Spatial Empirical Agent-Based Model. ISPRS International Journal of Geo-Information, 9(10), 581.
  • Cardinot, M. (2020). Coevolutionary spatial game theory: The impact of abstention. Small, 4(4), 1-7.
  • Carney, M., & Davies, B. (2020). Agent-Based Modeling, Scientific Reproducibility, and Taphonomy: A Successful Model Implementation Case Study.
  • Carpente, M. S., Guijarro-Berdiñas, B., Alonso-Betanzos, A., Rodríguez-Arias, A., & Dimitru, A. (2020). An Agent-Based Model to Simulate the Spread of a Virus Based on Social Behavior and Containment Measures. In Multidisciplinary Digital Publishing Institute Proceedings (Vol. 54, No. 1, p. 22).
  • Carrella, E., Bailey, R., & Madsen, J. (2020). Calibrating agent-based models with linear regressions. Journal Of Artificial Societies and Social Simulation, 23(1).
  • Carr-Markell, M. (2020). Effects of native prairie forbs on the foraging choices and recruitment behavior of honey bees (Apis mellifera).
  • Ceballos, Y. F., Galarcio-Noguera, J. D., Maya-Duque, P. A., & Ramirez-Cordoba, G. L. (2020). Agent-based Model for Environmental Awareness and Extended Producer Responsibility in Developing Countries. Scientia et Technica, 25(3), 430-437.
  • Centorrino, P., Corbetta, A., Cristiani, E., & Onofri, E. (2020). Managing Crowded Museums: Visitors Flow Measurement, Analysis, Modeling, and Optimization. arXiv preprint arXiv:2006.16830.
  • Chakarov, A. G. (2020). Integrating Computational Thinking into Middle School Science Curriculum Using Programmable Sensor Technologies (Doctoral dissertation, University of Colorado at Boulder).
  • Challenge, S., Jansens, R., Kingston, M., Landess, M., Morrison, B., Dubey, M., & Guerin, S. (2020). It’s ‘Bout To Get Lit Up In Here.
  • Chappin, É. J., Nikolic, I., & Yorke-Smith, N. (2020). Agent-based modelling of the social dynamics of energy end use. In Energy and Behaviour (pp. 321-351). Academic Press.
  • Chattoe-Brown, E. (2020). Why questions like ‘do networks matter?’matter to methodology: how Agent-Based Modelling makes it possible to answer them. International Journal of Social Research Methodology, 1-14.
  • Chen, B., Chen, H., Ning, D., Zhu, M., Ai, C., Qiu, X., & Dai, W. (2020). A Two-Tier Partition Algorithm for the Optimization of the Large-scale Simulation of Information Diffusion in Social Networks. Symmetry, 12(5), 843.
  • Chen, H. C., Han, Q., & De Vries, B. (2020). Modeling the spatial relation between urban morphology, land surface temperature and urban energy demand. Sustainable Cities and Society, 102246.
  • Chen, S., He, Q., & Xiao, H. (2020). A study on cross-border e-commerce partner selection in B2B mode. Electronic Commerce Research, 1-21.
  • Chen, S., Wu, J., Pan, Y., Ge, J., & Huang, Z. (2020). Simulation and case study on residential stochastic energy use behaviors based on human dynamics. Energy and Buildings, 110182.
  • Chen, S., Zhang, H., Guan, J., & Rao, Z. (2020, March). Agent-based modeling and simulation of stochastic heat pump usage behavior in residential communities. In Building Simulation (pp. 1-19). Tsinghua University Press
  • Cheng, C., Luo, Y., & Yu, C. (2020). Dynamic mechanism of social bots interfering with public opinion in network. Physica A: Statistical Mechanics and its Applications, 124163.
  • Chennoufi, M., & Bendella, F. (2020). Fuzzy controller and emotional model for evacuation of virtual crowd behaviors. Intelligent Decision Technologies, 14(2), 199-214.
  • Chennoufi, M., Bendella, F., & Bouzid, M. (2020). Best A* discovery for multi agents planning. International Journal of Operational Research, 38(3), 343-363.
  • CHIRIȚĂ, N., & NICA, I. (2020). Analysis of the impact generated by COVID-19 in banking institutions and possible economic effects. Theoretical and Applied Economics, 22(3 (624), Autumn), 21-40.
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2019

  • 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., 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
  • 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
  • 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
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  • Shirkhodaie, G., & Rahman, A. (2019). Investigating the Effects of the Spread of Contagious Disease and Immunization of Population on Social Welfare Using Agent Based Modeling. Social Welfare Quarterly, 18(70), 181-208.
  • Shvarts, A., & Abrahamson, D. (2019). Dual-eye-tracking Vygotsky: A microgenetic account of a teaching/learning collaboration in an embodied-interaction technological tutorial for mathematics. Learning, Culture, and Social Interaction, 22, 100316. https://doi.org/10.1016/j.lcsi.2019.05.003
  • Silva, J., Varela, N., & Lezama, O. B. P. (2019, September). Optimizing Street Mobility Through a NetLogo Simulation Environment. In International Conference On Computational Vision and Bio Inspired Computing (pp. 48-55). Springer, Cham.
  • Smarzhevskiy, I. (2019). Behaviour in Hierarchy NetLogo Model. Description, ODD Documentation, Result. Description, ODD Documentation, Result (September 9, 2019).
  • Spitznagel, B., Weigal, J., & Rodriguez, J. (2019). Visualizing Viscous Flow and Diffusion in the Circulatory System. The Physics Teacher, 57(8), 529-532.
  • Sun, B. (2019). Technology Innovation Diffusion Mechanisms of Agricultural Machinery in an Innovation Ecosystem. Revista de la Facultad de Agronomia de la Universidad del Zulia, 36(5).
  • Swanson H., Anton G., Bain C., Horn M., Wilensky U. (2019) Introducing and Assessing Computational Thinking in the Secondary Science Classroom. In: Kong SC., Abelson H. (eds) Computational Thinking Education. Springer, Singapore
  • Swanson, H., & Wilensky, U. (2019, August). Engaging students in theory building in the science classroom. Poster to be presented at the biennial conference of the European Association for Research on Learning and Instruction, Aachen, Germany.
  • Taves, A. (2019). Modeling Theories and Modeling Phenomena: A Humanist’s Initiation. In Human Simulation: Perspectives, Insights, and Applications (pp. 83-94). Springer, Cham.
  • Thiel, S. (2019). An Agent-Based Ecological Model of West Nile Virus for Classroom Use.
  • Tofah, D. K. W. (2019). Production and Logistics Systems Improvements-Biim Ultrasound AS (Master's thesis, UiT Norges arktiske universitet).
  • Triastanto, A. N. D., & Utama, N. P. (2019, September). Model Study of Traffic Congestion Impacted by Incidents. In 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (pp. 1-6). IEEE.
  • Tubadji, A., Angelis, V., & Nijkamp, P. (2019). Micro-cultural preferences and macro-percolation of new ideas: A NetLogo simulation. Journal of the Knowledge Economy, 10(1), 168-185
  • Urbanová, P. (2019). Agent based modeling of fish shoal behaviour.
  • Valente, J. A. (2019). The Role of Debugging in Knowledge Construction. Constructivist Foundations, 14(3).
  • Vogelstein, L., & Brady, C. (2019). Taking the Patch Perspective: A Comparative Analysis of a Patch Based Participatory Simulation.
  • Walker, B., & Johnson, T.V.(2019) NetLogo and GIS: A Powerful Combination EPiC Series in Computing, vol 58, 257-264. [PDF]
  • Wang, H. H., Grant, W. E., Elliott, N. C., Brewer, M. J., Koralewski, T. E., Westbrook, J. K., ... & Sword, G. A. (2019). Integrated modelling of the life cycle and aeroecology of wind-borne pests in temporally-variable spatially-heterogeneous environment. Ecological modelling, 399, 23-38.
  • Warnke, T., & Uhrmacher, A. M. (2019, October). Reproducible parallel simulation experiments via pure functional programming. In 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT) (pp. 1-8). IEEE.
  • Weintrop, D., Bau, D., & Wilensky U. (2019). The Cloud is the Limit: A case study of programming on the web, with the web. International Jounal of Child-Computer Interaction. [PDF]
  • Weintrop, D., & Wilensky, U. (2019). Transitioning from introductory block-based and text-based environments to professional programming languages in high school computer science classrooms. Computers & Education, 142.
  • Wen, G., Huang, N., & Zhu, J. (2019, November). A node-centric network congestion estimation method considering average spatio-temporal scale. In Journal of Physics: Conference Series (Vol. 1345, No. 4, p. 042062). IOP Publishing.[HTML]
  • Witwytzkyj, J., Valle Filho, A., & Santana, A. L. M. (2019, November). Modelagem Bifocal Aplicada à Engenharia Mecânica: Desenvolvimento de um modelo computacional de condução térmica para uso educacional no ensino superior. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação (Vol. 8, No. 1, p. 1084).
  • Xanthopoulou, T. D., Prinz, A., & Shults, F. L. (2019, September). Generating Executable Code from High-Level Social or Socio-Ecological Model Descriptions. In International Conference on System Analysis and Modeling (pp. 150-162). Springer, Cham.
  • Xia, H., Zhang, S., Li, Y., Pan, Z., Peng, X., & Cheng, X. (2019) "An Attack-Resistant Trust Inference Model for Securing Routing in Vehicular Ad Hoc Networks," in IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 7108-7120, July 2019. doi: 10.1109/TVT.2019.2919681
  • Yang, T., Gao, W., & Zhang, F. (2019, September). Summary of Research on Power Boosting Technology of Distributed Mobile Energy Storage Charging Piles. In 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC) (pp. 1770-1776). IEEE.
  • Yee, G. Q. M. (2019). Self-assembly for supply chains.
  • Young, E. (2019). Prioritevac, An Adaptive Model for Evacuation: Agent Based Simulation of the Station Nightclub Fire (Doctoral dissertation, University of Delaware).
  • Yu, B., Guo, Z., Peng, Z., Wang, H., Ma, X., & Wang, Y. (2019). Agent-based simulation optimization model for road surface maintenance scheme. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04018065.
  • Yue, T., Long, R., Chen, H., Liu, J., Liu, H., & Gu, Y. (2019). Energy-Saving Behavior of Urban Residents in China: A Multi-Agent Simulation. Journal of Cleaner Production, 119623.[HTML]
  • Yuhong, Z. H. A. O., & Jie, C. H. E. N. (2019, October). A P2P trust model based on trust factor and feedback aggregation. In 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE) (pp. 214-219). IEEE.
  • Zhang, N., & Zheng, X. (2019). Agent-based simulation of consumer purchase behaviour based on quality, price and promotion. Enterprise Information Systems, 13(10), 1427-1441.
  • Zhou, Y., & Wu, H. (2019). Dynamic analysis and simulation study of knowledge flow under the perspective of industrial transfer. Expert Systems, 36(5), e12345.
  • Zhu, H. (2019, May). A Platoon-based Rolling Optimization Algorithm at Isolated Intersections in A Connected and Autonomous Vehicle Environment. In Proceedings of the 2019 5th International Conference on Computing and Data Engineering (pp. 41-46).
  • Zia, K., Saini, D. K., & Muhammad, A. (2019). Efficient evacuation in a multi-exit environment: an agent-based decision support model. International Journal of Information and Decision Sciences, 11(4), 355-375.
  • Zu, C., Zeng, H. & Zhou, X.(2019) Computational Simulation of Team Creativity: The Benefit of Member Flow. Frontiers in Psychology. [PDF]
  • Zulkarnay, I. (2019). Simulation modeling in the study of the optimal placement of systemically important universities across the country. Artificial Societies, 14(4).

2018

  • Adelt, F., Weyer, J., Hoffmann, S., & Ihrig, A. (2018). Simulations of the governance of complex systems (SimCo): Basic concepts and experiments on urban transportation. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3654
  • 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.
  • 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]
  • 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]
  • 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]
  • 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). Emergence of Communication in Socio-Biological Networks. Springer International Publishing.[PDF]
  • 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]
  • 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.
  • 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]
  • 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]
  • 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]
  • 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
  • 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]
  • Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High Performance Computer Simulations
  • 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]
  • 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]
  • Davis, B. (2018). Complexity as a Discourse on School Mathematics Reform. In Transdisciplinarity in Mathematics Education (pp. 75-88). Springer, Cham.[PDF]
  • Deshmukh, V. (2018). Modeling Human Migration Dynamics in Netlogo.
  • 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.
  • DeSoto, K. A. (2018). Modeling Evacuation of Population Centers Using NetLogo
  • 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
  • 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).
  • Dubovi, I., Dagan, E., Mazbar, O. S., Nassar, L., & Levy, S. T. (2018). Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study. Nurse education today, 61, 175-181.[PDF]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • Drezewski, R. (2018). The Agent-Based Model and Simulation of Sexual Selection and Pair Formation Mechanisms. Entropy 20(5), p. 342. [PDF]
  • Easter, C. (2018). An agent-based simulation for studying the effect of various parameters on the evolution of teaching.
  • Edali, M. & Yücel, G. (2018). Automated analysis of regularities between model parameters and output using support vector regression in conjunction with decision trees. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3876
  • El Hachami, K., & Tkiouat, M. (2018, April). An approach for modeling the economy as a complex system using agent-based theory. In Intelligent Systems and Computer Vision (ISCV), 2018 International Conference on (pp. 1-6). IEEE.[PDF]
  • Ene, N., Fernandez, M., Pinaud, B. (2018). A Graph Transformation Approach to the Modelling of Capital Markets. [PDF]
  • Flores-Parra, J. M., Castañón-Puga, M., Gaxiola-Pacheco, C., Palafox-Maestre, L. E., Rosales, R., & Tirado-Ramos, A. (2018). A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo. In Computer Science and Engineering—Theory and Applications (pp. 127-149). Springer, Cham.
  • Furtado, B. (2018). Policy space: agent based modeling. Rio de Janerio: Ipea.[PDF]
  • Feldman, T. (2018). Unwinding ZIRP: A simulation analysis. Finance Research Letters, 24, 278-288.[PDF]
  • Galán, S. F., & Mengshoel, O. J. (2018). Neighborhood beautification: Graph layout through message passing. Journal of Visual Languages & Computing, 44, 72-88.[PDF]
  • Gao, X., Li, K., & Chen, B. (2018). Invulnerability Measure of a Military Heterogeneous Network Based on Network Structure Entropy. IEEE Access, 6, 6700-6708.[PDF]
  • García-Magariño, I., Gray, G., Lacuesta, R., & Lloret, J. (2018). Survivability strategies for emerging wireless networks with data mining techniques: a case study with NetLogo and RapidMiner.IEEE Access.[PDF]
  • Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., Narasimhan, K. P., & Wilkinson, H. (2018). Computational modelling of public policy: Reflections on practice. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3669
  • Ginovart, M. (2018). How a multi-agent programmable modelling environment like NetLogo can help to deal with communities or assemblages of bacteria on surfaces?. Exploring Microorganisms: Recent Advances in Applied Microbiology, 256.
  • Gómez-Cruz, N. A., Loaiza Saa, I., & Ortega Hurtado, F. F. (2018). Agent-based simulation in management and organizational studies: a survey. European Journal of Management and Business Economics, 26(3), 313-328.[PDF]
  • Gonzales, G. V., dos Santos, E. D., Adamatti, D. F., & Neto, A. J. S. (2018). A Netlogo and Matlab Hybrid Approach for Constructal Design of the Double-T Shaped Cavity by Means of Simulated Annealing. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, 6(1).
  • Greco, A., & Pluchino, A. (2018). On the use of immune algorithms to determine seismic collapse conditions for frame structures. Computer Software and Media Applications.
  • Gunaratne, C., & Garibay, I. (2018). NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces. arXiv preprint arXiv:1808.03292.
  • Guo, Y. and Wilensky (2018a). NetLogo MTG 1 Equal Opportunities HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Guo, Y. and Wilensky (2018b). NetLogo MTG 2 Random Assignment HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Guo, Y. and Wilensky (2018c). NetLogo MTG 3 Feedback Loop HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Haeme, J., McCaw, K., Nguyen, T., & May, T. (2018). Project Whirligig: Modeling the Swarming Behavior of Whirligig Beetles.
  • Han, S., Huang, H., Luo, Z., & Foropon, C. (2018). Harnessing the power of crowdsourcing and Internet of Things in disaster response. Annals of Operations Research, 1-16.[PDF]
  • Haydari, S. (2018). Copyright Law and Knowledge Creation: A Study of Copyright Term Length Impact on Knowledge Creation and Learning. Northeastern University, ProQuest Dissertations Publishing[PDF]
  • Heath, K. N., Ryder, E. F., & Gegear, R. J. (2018). Investigating the effect of memory loss on pollinator-plant interactions through agent-based modeling. Gordon Research Conference: Unifying Ecology Across Scales, Biddeford, ME, United States.
  • Hébert, G. A., Perez, L., & Harati, S. (2018). An Agent-Based Model to Identify Migration Pathways of Refugees: The Case of Syria. In Agent-Based Models and Complexity Science in the Age of Geospatial Big Data (pp. 45-58). Springer, Cham.[PDF]
  • Head, B., & Wilensky, U. (2018). Agent cognition through micro-simulations: Adaptive and tunable intelligence with NetLogo LevelSpace. In: Morales A., Gershenson C., Braha D., Minai A., Bar-Yam Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham.[PDF]
  • Hokamp, S., Gulyas, L., Koehler, M., & Wijesinghe, S. (2018). Agent-Based Modeling of Tax Evasion. Wiley.
  • Huang, W. (2018). Exploring households’ weatherization adoptions: an agent-based approach (Doctoral dissertation, Iowa State University).[PDF]
  • Humann, J., Jin, Y., & Madni, A. M. (2018). Scalability in Self-Organizing Systems: An Experimental Case Study on Foraging Systems. In Disciplinary Convergence in Systems Engineering Research (pp. 543-557). Springer, Cham.[PDF]
  • Hunter, E., Mac Namee, B., & Kelleher, J. D. (2018). Using a socioeconomic segregation burn-in model to initialise an agent-based model for infectious diseases. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3870
  • Izquierdo, S. S. & Izquierdo, L. R. (2018). Mamdani fuzzy systems for modelling and simulation: A critical assessment. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3660
  • Jaxa-Rozen, M., & Kwakkel, J. H. (2018). PyNetLogo: Linking NetLogo with Python. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3668
  • Jianyu, Z., Baizhou, L., Xi, X., Guangdong, W., & Tienan, W. (2018). Research on the characteristics of evolution in knowledge flow networks of strategic alliance under different resource allocation. Expert Systems with Applications, 98, 242-256.[PDF]
  • Jing, L., Chen, B., Zhang, B., & Ye, X. (2018). Modeling marine oily wastewater treatment by a probabilistic agent-based approach. Marine pollution bulletin, 127, 217-224.[PDF]
  • Karanci, A., Velásquez-Montoya, L., Paniagua-Arroyave, J. F., Adams, P. N., & Overton, M. F. (2018). Beach Management Practices and Occupation Dynamics: An Agent-Based Modeling Study for the Coastal Town of Nags Head, NC, USA. In Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 373-395). Springer, Cham.[PDF]
  • Klein, I., Levy, N., & Ben-Elia, E. (2018). An agent-based model of the emergence of cooperation and a fair and stable system optimum using ATIS on a simple road network. Transportation research part C: emerging technologies, 86, 183-201.[PDF]
  • Koch, A. (2018). Dynamic Relationships Between Human Decision Making and Socio-natural Systems. In Trends in Spatial Analysis and Modelling (pp. 121-141). Springer, Cham.[PDF]
  • Köhler, J., de Haan, F., Holtz, G., Kubeczko, K., Moallemi, E., Papachristos, G., & Chappin, E. (2018). Modelling sustainability transitions: An assessment of approaches and challenges. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3629
  • Koli, V.N., Mirza, F., Baig, M.M., & Ullah, E.(2018). An Agent-Based Modelling Approach for Scheduling and Management of Elective Surgeries. SM Journal of Health and Medical Informatics[PDF]
  • Kotnik, K. (2018). Exploring Marine Population Dynamics with Agent Based Models.
  • Kurahashi, S. (2018, June). Agent-Based Gaming Approach for Electricity Markets. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 311-320). Springer, Cham.[HTML]
  • Laatabi, A., Marilleau, N., Nguyen-Huu, T., Hbid, H., & Ait Babram, M. (2018). ODD+2D: An ODD based protocol for mapping data to empirical ABMs. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3646
  • Lawall, M. L., & Graham, S. (2018). NETLOGO SIMULATIONS AND THE USE OF TRANSPORT AMPHORAS IN ANTIQUITY. Maritime Networks in the Ancient Mediterranean World, 163.
  • Lawlor, F., Collier, R., Nallur, V. (2018).Towards a Programmable Framework for Agent Game Playing. Adaptive Learning Agents Workshop.[HTML]
  • Lee, T. E. (2018). The thin blue line between protesters and their counter-protesters. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3676
  • Lemos, C. M. (2018). Model Exploration and Computer Experiments. In Agent-Based Modeling of Social Conflict (pp. 65-111). Springer, Cham.[HTML]
  • Lemos, C. M. (2018). ABM of Civil Violence: ODD Description. In Agent-Based Modeling of Social Conflict (pp. 51-63). Springer, Cham.[HTML]
  • Liew, C. W., Phuong, T., Jones, C. B., Evans, S., Hoot, J., Weedling, K., ... & Kurt, R. A. (2018). A computational approach to unraveling TLR signaling in murine mammary carcinoma. Computers in biology and medicine, 93, 56-65.[PDF]
  • Liukkonen, L., Ayllón, D., Kunnasranta, M., Niemi, M., Nabe-Nielsen, J., Grimm, V., & Nyman, A. M. (2018). Modelling movements of Saimaa ringed seals using an individual-based approach. Ecological Modelling, 368, 321-335.[PDF]
  • Lopez-Parez, A., Ruiz-Martin, C., Wainer, G. (2018). Formal Abstract Modeling of Dynamic Multiplex Networks. Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pp.61-72. Rome, Italy.[PDF]
  • Luna-Ramirez, W. A., & Fasli, M. (2018). Bridging the gap between abm and mas: A disaster-rescue simulation using jason and netlogo. Computers, 7(2), 24.
  • Maciel, M.V. (2018). Emergência de distribuições de posicionamentos ideológicos: uma abordagem computacional. Diss. Universidade de São Paulo.
  • Mailleret, L., Davtian, D., & Grognard, F. (2018). An individual based model to optimize natural enemies deployment in augmentative biological control.
  • Malishev, M., Bull, C. M., & Kearney, M. R. (2018). An individual‐based model of ectotherm movement integrating metabolic and microclimatic constraints. Methods in Ecology and Evolution, 9(3), 472-489.[PDF]
  • Martin, K. (2018, October). Multitouch NetLogo for museum interactive game. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 5-8).
  • Mayangsari, L., & Pasaribu, N. (2018). Understanding Unlawful Behavior in Music Industry Using Agent-Based Modelling and Simulation: A Complementary of Misconception in Value Co-Creation. World Academy of Science, Engineering and Technology, International Journal of Industrial and Manufacturing Engineering, 5(4).[HTML]
  • Menárguez, F. J. M., Hortal, J. C., Salazar, S. S., & Alarcón, A. A. (2018). Anatomizing NetLogo. Some advices on how to consider a programmable environment for designing inhabited landscapes. In EURAU18 Alicante: Retroactive Research: Congress Proceedings (pp. 423-428). Universitat d´ Alacant/Universidad de Alicante.
  • Moglia, M., Podkalicka, A., & McGregor, J. (2018). An agent-based model of residential energy efficiency adoption. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3729
  • Muelder, H. & Filatova, T. (2018). One theory - many formalizations: Testing different code implementations of the theory of planned behavior in energy agent-based models. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3855
  • Mueller, C., Klein, U., & Hof, A. (2018). An easy-to-use spatial simulation for urban planning in smaller municipalities. Computers, Environment and Urban Systems.[PDF]
  • Muhammad, A., Kashif, Z., & Saini, D. (2018). Agent-based Simulation of Socially-inspired Model of Resistance against Unpopular Norms Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) - Volume 1, pages 133-139.[PDF]
  • Nabe-Nielsen, J., van Beest, FM., Grimm, V., Sibly, PM.(2018). TRACE document: Disturbances and marine populations.[PDF]
  • Pei, C., Weintrop, D., & Wilensky, W. (in press). Cultivating computational thinking practices and mathematical habits of mind in Lattice Land. Mathematical Thinking and Learning.
  • Nie, J., Hu, H., Wang, X., & Xu, C. (2018). Simulation Study on Network Stability of Short Cycle Product Supply Chain Based on Netlogo. In CICTP 2018: Intelligence, Connectivity, and Mobility (pp. 409-418). Reston, VA: American Society of Civil Engineers.
  • Ornelas, N. O. An Ecosystem: Computational Thinking, Project-Based Learning [PDF]Logo
  • Peng, Y., Li, Q. X., & Bao, H. J. (2018). Conflict Analysis of Concentrated Rural Settlement Development During Post-disaster Reconstruction in China: A Multi-agent Simulation. In Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate (pp. 491-502). Springer, Singapore.[PDF]
  • Pour, F. S. A., Tatar, U., & Gheorghe, A. (2018, April). Agent-based model of sand supply governance employing blockchain technology. In Proceedings of the Annual Simulation Symposium (p. 14). Society for Computer Simulation International.[PDF]
  • Proietti, C. & Franco, A. Social norms and the dominance of low-doers. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3524
  • Raimbault, J. An Urban Morphogenesis Model Capturing Interactions between Networks and Territories.[PDF]
  • Rajib, Md. Design Considerations for Intermittently Connected Energy Harvesting Wireless Sensor Networks. ProQuest.[PDF]
  • Ramírez-Ávila, G. M., Kurths, J., & Deneubourg, J. L. (2018). Fireflies: a paradigm in synchronization. In Chaotic, Fractional, and Complex Dynamics: New Insights and Perspectives (pp. 35-64). Springer, Cham.[PDF]
  • Rashid, K. I., Nan, D., Tahir, M., & Ahmed, A. An Adaptive Cruise Control Model based on PDLCA for Efficient Lane Selection and Collision Avoidance. International Journal of Advanced Computer Science and Applications (9).[PDF]
  • Reinhardt, O., Hilton, J., Warnke, T., Bijak, J., & Uhrmacher, A. M. (2018). Streamlining simulation experiments with agent-based models in demography. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3784
  • Rosales, C., Whipple, J. M., & Blackhurst, J. (2018). The Impact of Out-of-Stocks and Supain Design on Manufacturers: Insights from an Agent-Based Model. Transportation Journal, 57(2), 137-162.[PDF]
  • Rossetti, G., Milli, L., Rinzivillo, S., Sîrbu, A., Pedreschi, D., & Giannotti, F. (2018). NDlib: a python library to model and analyze diffusion processes over complex networks. International Journal of Data Science and Analytics, 5(1), 61-79.[PDF]
  • Santos, F., Nunes, I., & Bazzan, A. L. (2018). Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain. Simulation Modelling Practice and Theory, 83, 162-187.[PDF]
  • Schools, S. (2018). A Genetic Algorithm for Sorting Lists Implemented in the Programming Language Net Logo (Doctoral dissertation, Morgan State University)
  • Secchi, D. & Cowley, S. J. (2018). Modeling organizational cognition: The case of impact factor. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3628
  • Simpkins, C. E., Dennis, T. E., Etherington, T. R., & Perry, G. L. (2018). Assessing the performance of common landscape connectivity metrics using a virtual ecologist approach. Ecological Modelling, 367, 13-23.[PDF]
  • Snitker, G. (2018). Identifying natural and anthropogenic drivers of prehistoric fire regimes through simulated charcoal records. Journal of Archaeological Science, 95, 1-15.[PDF]
  • Sturley, C., Newing, A., & Heppenstall, A. (2018). Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours. The Ionternatinal Review of Retail, Distribution and Consumer Research, 28(1), 27-46.[HTML]
  • Sullivan, A., An, L., & York, A. (2018). Which perspective of institutional change best fits empirical data? An agent-based model comparison of rational choice and cultural diffusion in invasive plant management. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3611
  • Taherian, M., Mousavi, S. M., & Chamani, H. (2018). An agent-based simulation with NetLogo platform to evaluate forward osmosis process (PRO Mode). Chinese journal of chemical engineering, 26(12), 2487-2494.
  • Thakur, S., Bhautik, P., Sangore, V., Singh, K. (2018).A Review on Treatment of Sewage Water & Biogas Purification by Algae. Internatinal Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 1, Page No pp.639-642, January 2018, [PDF]
  • Thiriot, S. (2018). Word-of-mouth dynamics with information seeking: Information is not (only) epidemics. Physica A: Statistical Mechanics and its Applications, 492, 418-430.[PDF]
  • Tyson, M. (2018). Managing Distributed Information: Implications for Energy Infrastructure Co-production (Doctoral dissertation, Arizona State University).[PDF]
  • Paris, T., Ciarletta, L., & Chevrier, V. (2018, October). Co-simulation à base d'outils multi-agents: un cas d'étude avec NetLogo.
  • Pele, M., Deneubourg, J. L., & Sueur, C. (2018). Decision-making processes underlying pedestrian behaviours at signalised crossings: Part 2. Do pedestrians show cultural herding behaviour?. arXiv preprint arXiv:1805.11834.[PDF]
  • Polater, A. (2018). Managing airports in non-aviation related disasters: A systematic literature review. International Journal of Disaster Risk Reduction.[PDF]
  • PONZIANI, F., TINABURRI, A., & RICCI, V. (2018). A Multi Agent Approach To Analyse Shift In People Behaviour Under Critical Conditions. International Journal of Safety and Security Engineering, 8(1), 1-9.[PDF]
  • Proctor, C., Blikstein, P.(2018). Unfold.studio: Supporting critical literacies of text and code Stanford Graduate School of Education, (p. 1-35).[PDF]
  • Vanhée, L. & Dignum, F. (2018). Explaining the emerging influence of culture, from individual influences to collective phenomena. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3881
  • Vidaković, M., Ivanović, M., Stantić, D., & Vidaković, J. (2018, June). How Research Achievements Can Influence Delivering of a Course-Siebog Agent Middleware. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 110-120). Springer, Cham.[HTML]
  • Waight, N., & Abd-El-Khalick, F. (2018). Technology, Culture, and Values: Implications for Enactment of Technological Tools in Precollege Science Classrooms. In Cognition, Metacognition, and Culture in STEM Education (pp. 139-165). Springer, Cham.[PDF]
  • Walbert, H. J., Caton, J. L., & Norgaard, J. R. (2018). Countries as agents in a global-scale computational model. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3717
  • Wang, Y., Wen, S., Farnon Ellwood, M. D., Miller, A. D., & Chu, C. (2018). Temporal effects of disturbance on community composition in simulated stage‐structured plant communities. Ecology and evolution, 8(1), 120-127.[PDF]
  • Wang, Z., Zhang, H., Hu, M., Qiu, Q., & Liu, H. (2018). Analysis of safety characteristics of flight situation in complex low-altitude airspace. Advances in Mechanical Engineering, 10(5), 1687814018774656.[PDF]
  • 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]
  • Weintrop, D., & Wilensky, U. (2018). How block-based, text-based, and hybrid block/text modalities shape novice programming practices. International Journal of Child-Computer Interaction.[PDF]
  • Weisbuch, G. (2018). Lattice dynamics of inequality. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3635
  • West, T. A., Grogan, K. A., Swisher, M. E., Caviglia-Harris, J. L., Sills, E., Harris, D., ... & Putz, F. E. (2018). A hybrid optimization-agent-based model of REDD+ payments to households on an old deforestation frontier in the Brazilian Amazon. Environmental Modelling & Software, 100, 159-174.[PDF]
  • 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]
  • 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. 2017. A multi-level agent-based model of reinsurance. Journal of Applied Economic Sciences, Volume XII, Summer 3(49): 746– 752.[PDF]
  • 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
  • Yaşar, O. (2018). A new perspective on computational thinking. Communications of the ACM, 61(7), 33-39.
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • Abbott, R., & Hadžikadić, M. (2017).Complex Adaptive Systems, Systems Thinking, and Agent-Based Modeling. In Advanced Technologies, Systems, and Applications (pp. 1-8). Springer International Publishing. [PDF]
  • ACOSTA, C., BORGESIUS, F., & VAN HATTUM, J. E. S. S. I. E. Facilitating Collective Action for an Integrated Community Energy System.[PDF]
  • Alzaeemi, S. A. S., Sathasivam, S., & Adebayo, S. A. (2017). Analysis of Performance of Various Activation Functions for doing the logic programming in Hopfield Network. International Journal of Computational Bioinformatics and In Silico Modeling, 6(2), 911-921. [ PDF]
  • Alzahrani, E., Richmond, P., & Simons, A. J. (2017, August). A formula-driven scalable benchmark model for ABM, applied to FLAME GPU. In European Conference on Parallel Processing (pp. 703-714). Springer, Cham.[ PDF]
  • Ampatzidis, G., & Ergazaki, M. (2017). Toward an “Anti-Balance of Nature” Learning Environment for Non-Biology Major Students: Learning Objectives and Design Criteria. Natural Sciences Education, 46(1).[HTML]
  • Anderson, J. H., Downs, J. A., Loraamm, R., & Reader, S. (2017). Agent-based simulation of Muscovy duck movements using observed habitat transition and distance frequencies. Computers, Environment and Urban Systems, 61, 49-55. [PDF]
  • Aydin, M. E., & Fellows, R. (2017). A reinforcement learning algorithm for building collaboration in multi-agent systems. arXiv preprint arXiv:1711.10574.[PDF]
  • Auerbach, S., & Dix, R. (2017). Competition and spatial efficiency.[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]
  • 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]
  • Banati, H., Bhattacharyya, S., Mani, A., & Köppen, M. (Eds.). (2017). Hybrid Intelligence for Social Networks. Springer International Publishing.[PDF]
  • 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]
  • 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
  • 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]
  • 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]
  • Carver, S., & Quincey, D. (2017). A Conceptual Design of Spatio-Temporal Agent-Based Model for Volcanic Evacuation. Systems, 5(4), 53.[PDF]
  • à Campo, S. Agent-Based Modelling for Online Community Designers. Paper presented at the Conference on Humana Factors in Computing Systems.[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]
  • 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]
  • 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]
  • 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]
  • 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
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  • 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]
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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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  • Li, J., & He, J. (2017).Evolutionary Game and Simulation on the Internet of Things in Supply Chain. In Proceedings of the Fourth International Forum on Decision Sciences (pp. 271-279). Springer, Singapore. [ HTML]
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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
  • Ma, S., Jiang, Z., & Liu, W. (2017).Multi-variation propagation prediction based on multi-agent system for complex mechanical product design. Concurrent Engineering: Research and Applications, 1-15. [ PDF]
  • Malanson, G. P., Resler, L. M., & Tomback, D. F. (2017).Ecotone response to climatic variability depends on stress gradient interactions. Climate Change Responses, 4(1), 1. [HTML]
  • Malik, A., & Abdalla, R. (2017).Agent-based modelling for urban sprawl in the region of Waterloo, Ontario, Canada. Modeling Earth Systems and Environment, 3(1), 7. [PDF]
  • Markauskaite, L., Kelly, N., & Jacobson, M. J. (2017). Model-based knowing: How do students ground their understanding about climate systems in agent-based computer models? Research in Science Education, 1-25.[PDF]
  • Martin, P., Newton, A. C., Cantarello, E., & Evans, P. M. (2017).Analysis of ecological thresholds in a temperate forest undergoing dieback. PloS one, 12(12), e0189578.[PDF]
  • Mantese, G. C., & Amaral, D. C. (2017).Comparison of industrial symbiosis indicators through agent-based modeling. Journal of Cleaner Production, 140, 1652-1671. [PDF]
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  • Minjing, P., Xinglin, L., Ximing, L., Mingliang, Z., Xianyong, Z., Xiangming, D., & Mingfen, W. Recognizing intentions of E-commerce consumers based on ant colony optimization simulation. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-11. [HTML]
  • Mittal, A., 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]
  • Mittal, A. & Krejci, C. (2017). Integrating Consumer Preferences in Renewable Energy Expansion Planning Using Agent-based Modeling.[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]
  • 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]
  • 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]
  • Rani, G. A., Krishna, V. R., & Murthy, G. V. (2017). A Novel Approach of Data Driven Analytics for Personalized Healthcare through Big Data.[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
  • 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]
  • 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
  • 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]
  • 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]
  • 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]
  • 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).
  • 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
  • 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]
  • Velazquez, J. J., Su, E., Cahan, P., & Ebrahimkhani, M. R. (2017). Programming Morphogenesis through Systems and Synthetic Biology. Trends in biotechnology.[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]
  • 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]
  • 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]
  • 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, 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]
  • 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.
  • 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]
  • 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
  • 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]

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 ]
  • 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). 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]
  • 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 ]
  • 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]
  • 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., 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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
  • 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]
  • 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]
  • 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]
  • 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, 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]
  • 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]
  • Weintrop, D., Hjorth, A., & Wilensky, U. (2016). NetLogo Web: Bringing Turtles to the Cloud. Workshop at Constructionism 2016. Bangkok, Thailand.
  • 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]
  • Will, T. E. (2016) A Flocking Model of Human Organizing: Leadership Implications. Presented at the Annual Meeting of the Academy of Management, Anaheim, CA.
  • 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]
  • 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

  • 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
  • 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.
  • 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]
  • 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]
  • 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., Singh, A., & Goyal, R. (2015).A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems Computers 4, 2-23. [HTML]
  • 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]
  • 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
  • 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
  • Cowie, G., Hurd, M., Sevostianov, V., Cundiff, M. E., & Guerin, M. S. (2015). PAVL: Personal Assistance for the Visually Limited. [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]
  • 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]
  • 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]
  • 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]
  • 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]
  • Gordo, E., Khalaf, N., Strangeowl, T., Dolino, R., & Bennett, N. (2015). FACTORS AFFECTING SOLAR POWER PRODUCTION EFFICIENCY. [HTML]
  • 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 Proceedings of the 2015 Winter Simulation Conference (pp. 3122-3123). IEEE Press.[HTML
  • Hjorth, A., Brady, C., Head, B. and Wilensky, U. (2015).LevelSpaceGUI - Scaffolding Novice Modelers’ Inter-Model Explorations. In proceedings for Interaction Design & Children 2015. Boston, MA.
  • Hjorth, A., Brady, C., Head, B., Wilensky, U. (2015).Thinking Within and Between Levels: Exploring Reasoning with Multi-Level Linked Models. In T. Koschmann, P. Häkkinen, & P. Tchounikine (Eds.), "Exploring the material conditions of learning: opportunities and challenges for CSCL," the Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference Gothenburg, Sweden: ISLS.
  • Hmelo‐Silver, C. E., Liu, L., Gray, S., & Jordan, R. (2015). Using representational tools to learn about complex systems: A tale of two classrooms. Journal of Research in Science Teaching, 52(1), 6-35.[HTML]
  • Holbert, N., Brady, C., Soylu, F., Novak, M., & Wilensky, U. (2015).The Model Gallery: Supporting Idea Diffusion in Computational Modeling Activities. Poster presented at the AERA Annual Meeting, Chicago, IL: April, 2015.
  • 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]
  • Jeewan, A., & Hussain, R. (2015). Minimal Energy Consumption by WSN Nodes during Communication using LEACH and NetLogo in Intelligent Greenhouse. International Journal of Advanced Research in Computer Science 6.6 [HTML]
  • Jerry, K., Yujun, K., Kwasi, O., Enzhan, Z., & Parfait, T. (2015). NetLogo implementation of an ant colony optimisation solution to the traffic problem. Intelligent Transport Systems, IET, 9(9), 862-869. [HTML]
  • Jiang, G., Liu, X., & Wang, Y. (2015). An Agent-based Simulation System for Evolution of Interplay between E-Commerce Vendor and Consumers. International Journal of Hybrid Information Technology, 8(4), 81-88. [HTML]
  • Jiang, G., Wang, Y., & Zhang, N. (2015). Evolution of the Interplay Between E-Commerce Vendor and Consumers. In LISS 2014 (pp. 1243-1247). Springer Berlin Heidelberg.[HTML]
  • Jimenez-Romero, C., Sousa-Rodrigues, D., & Johnson, J.H. (2015).A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones. Proceedings of the UK Workshop on Computational Intelligence (UKCI 2015) Conference, at Exeter. [HTML]
  • 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]
  • 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.
  • 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
  • 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]
  • 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]
  • 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
  • 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.
  • 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
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • Polhill, G. (2015). Extracting OWL Ontologies from Agent-Based Models: A NetLogo Extension. Journal of Artificial Societies and Social Simulation, 18(2), 15. [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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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
  • 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
  • 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.
  • 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., 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]
  • 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
  • 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
  • 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]
  • 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]
  • 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]

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). 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. (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., Villamar, J., Brown, H. (2014). Using Agent-Based Modeling to Explore and Visualize the Effects of Prevention Implementation Strategies for Policy. In Computational and Technical Approaches to Improve the Implementation of Prevention Programs. Panel chaired by Dr. Hendricks Brown at 7th Annual Conference on the Science of Dissemination and Implementation. Bethesda, MD.
  • Horn, M., Brady, C., Hjorth, A., Wagh, A., & Wilensky, U. (2014). Frog Pond: A code first learning environment on natural selection and evolution. Proceedings of IDC 2014.
  • Horn, M.S., Weintrop, D., & Routman, E. (2014). Programming in the Pond: A Tabletop Computer Programming Exhibit. In Proceedings of the Extended Abstracts of the 32nd Annual ACM Conference on Human Factors in Computing Systems (pp. 1417-1422). New York, NY, USA: ACM.
  • Iwamura, T., Lambin, F.E., Silvius, M.K., Luzar, B.J & Fragoso, MV.J. (2014). Agent-based modeling of hunting and subsistence agriculture on indigenous lands: Understanding interactions between social and ecological systems. Environmental Modelling and Software 58(2014)109-127 [HTML]
  • Izquierdo, L.R., Izquierdo, S.S. & Vega-Redondo, F. (2014). Leave and let leave: A sufficient condition to explain the evolutionary emergence of cooperation. Journal of Economic Dynamics & Control 46, pp. 91–113. [PDF]
  • Jona, K., Wilensky, U., Trouille, L., Horn, M. S., Orton, K., Weintrop, D., & Beheshti, E. (2014). Embedding Computational Thinking in Science, Technology, Engineering, and Math (CT-STEM). Presented at the 2014 CE21 PI and Community Meeting, Orlando, FL.
  • Kautz, M., Muhammad, A.I., & Schopf, R. (2014). Individual traits as drivers of spatial dispersal and infestation patterns in a host-bark beetle system. Ecological Modelling, 273, 264-276. [HTML]
  • Knoeri, C., Nikolic, I., Althaus, H. J., & Binder, C. R. (2014). Enhancing recycling of construction materials: An agent based model with empirically based decision parameters. Journal of Artificial Science and Social Simulation, 17(3). doi.org/10.18564/jasss.2528
  • Labarbe, E., & Thiel, D. (2014). Information Sharing to Reduce Misperceptions of Interactions Among Complementary Projects: A Multi-Agent Approach. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 9. [HTML] (Jan 2014)

  • Lee, K., Lee, H., & Kim, C.O. (2014). Pricing and Timing Strategies for New Product Using Agent-Based Simulation of Behavioural Consumers. Journal of Artificial Societies and Social Simulation (JASSS), 17 (2): 1. [HTML] (March 2014)
  • Lee, T., Yao, R., & Coker, P. (2014). An analysis of UK policies for domestic energy reduction using an agent based tool. Energy Policy, 66, 267-279. [HTML] (March 2014)
  • León, F.J., Miguel, F.J., & Alcaide, V. (2014). The Production of Step-Level Public Goods in Structured Social Networks: An Agent-Based Simulation. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 4. [HTML] (Jan 2014)
  • Levin, J. A., Jacobson, M. J., & Markauskaite, L. (2014). Combining computational modeling, theory, and data: Steps toward a meta-model framework for the study of learning. Paper presented at the 2014 American Educational Research Association meetings. Philadelphia, PA. [HTML]
  • Lim, D., Lee, H., Zo, H., & Ciganek, A. (2014). Opinion Formation in the Digital Divide. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 13. [PDF] (Jan 2014)
  • Lucas, P., de Oliveira, A. C. M., & Banuri, S. (2014). The effects of group composition and social preference heterogeneity in a public goods game: An agent-based simulation. Journal of Artificial Science and Social Simulation, 17(3). doi.org/10.18564/jasss.2522
  • Lynch, S.C., & Ferguson, J. (2014). Reasoning about Complexity – Software Models as External Representations. Proceedings of the 25th Workshop of The Psychology of Programming Interest Group, Brighton, UK. June 2014. [PDF]
  • Maldos, J.P.A., & de Figueiredo, J.C.B. (2014). Projeto de Iniciação Científica: O Uso de Programação Multiagente no Estudo da Difusão de Inovações Tecnológicas. [HTML]
  • Maroulis, S., Bakshy, E., Gomez, L. & Wilensky, U. (2014). Modeling the Transition to Public School Choice. Journal of Artificial Societies and Social Simulation. /li>
  • Maroulis, S & Wilensky, U. (2014). Social and Task Interdependencies in the Street-Level Implementation of Innovation. Journal of Public Administration Research and Theory.
  • Medina, F. J. L., Quesada, F. J. M., & Lozano, V. A. (2014). The production of step-level public goods in structured social networks: An agent-based simulation. Journal of Artificial Science and Social Simulation, 17(1). doi.org/10.18564/jasss.2419
  • Nissen, V. & Saft, D. (2014). A practical guide for the creation of random number sequences from aggregated correlation data for multi-agent simulations. Journal of Artificial Science and Social Simulation, 17(4). doi.org/10.18564/jasss.2593
  • Oremland, M., & Laubenbacher, R. (2014). Optimization of Agent-Based Models: Scaling Methods and Heuristic Algorithms. Journal of Artificial Societies and Social Simulation (JASSS), 17 (2): 6. [HTML] (March 2014)
  • Pluchino, A., Garofalo, C., Inturri, G., Rapisarda, A., & Ignaccolo, M. (2014). Agent-Based Simulation of Pedestrian Behaviour in Closed Spaces: A Museum Case Study. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 16. [HTML] (Jan 2014)
  • Rhee, J.M. (2014). Promoting Convergence: The Phi Spiral in Abduction of Mouse Corneal Behaviors. In Complexity. [HTML]
  • Salgado, M., Marchione, E., & Gilbert, N. (2014). Analysing differential school effectiveness through multilevel and agent-based modelling. Journal of Artificial Science and Social Simulation, 17(4). doi.org/10.18564/jasss.2534
  • Shaker, N., Togelius, J., & Nelson, M.J. (2014). Procedural Content Generation in Games: A textbook and an overview of current research. Springer Publishing. [HTML]
  • Sie, R., Sloep, P.B., & Bitter-Rijpkema, M. (2014). If We Work Together, I Will Have Greater Power: Coalitions in Networked Innovation. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 3. [HTML] (Jan 2014)
  • Soylu, F., Brady, C., Holbert, N., Wilensky, U. (2014). The thinking hand: Embodiment of tool use, social cognition and metaphorical thinking and implications for learning design. Paper presented at the AERA Annual Meeting (SIG: Brain, Neurosciences, and Education), Philadelphia, PA: April, 2014
  • Stoica, V. I. & Flache, A. (2014). From Schelling to schools: A comparison of a model of residential segregation with a model of school segregation. Journal of Artificial Science and Social Simulation, 17(1). doi.org/10.18564/jasss.2342
  • Stroup, W., & Wilensky, U. (2014). On the Embedded Complementarity of Agent-Based and Aggregate Reasoning in Students' Developing Understanding of Dynamic Systems. Technology, Knowledge and Learning, 19(1-2).
  • Szilagyi, M.N. (2014). Solution of partial differential equations by agent-based simulation. European Journal of Physics 35, 018003, 1-4.
  • Thiele, J.C., Kurth, W., Grimm, V. (2014). Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and R. Journal of Artificial Societies and Social Simulation, 17 (3).[HTML]
  • Wagh, A. & Wilensky, U. (2014). EvoBuild: Programming models of evolutionary change using blocks. Poster presented at the 2014 Annual Meeting of the AERA, Philadelphia.
  • vWagh, A. & Wilensky, U. (2014). Seeing patterns of change: Supporting student noticing in building models of natural selection. Proceedings of 2014 Constructionism, Vienna, Aug 19-23.
  • Wilensky, U. (2014). Computational Thinking through Modeling and Simulation. Whitepaper presented at the summit on Future Directions in Computer Education. Orlando, FL. Jan 8-9, 2014. [PDF]
  • Wilensky, Brady & Horn (2014). Fostering Computational Literacy in Science Classrooms. Communications of the ACM.
  • Wilensky, U. & Jacobson, M. (In press). Complex Systems in the Learning Sciences. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd Edition). Cambridge, UK: Cambridge University Press.
  • Wilkerson-Jerde, M. H. & Wilensky, U. (2014). Patterns, probabilities, and people: Making sense of quantitative change in complex systems. Online First in Journal of the Learning Sciences. doi:10.1080/10508406.2014.976647[HTML]
  • Xu, B., Liu, R., & Liu, W. (2014). Individual Bias and Organizational Objectivity: An Agent-Based Simulation. Journal of Artificial Societies and Social Simulation (JASSS), 17 (2): 2. [HTML] (March 2014)
  • Zhang, H., & Li, Y. (2014). Agent-Based Simulation of the Search Behavior in China's Resale Housing Market: Evidence from Beijing. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 18. [HTML] (Jan 2014)

2013

  • Abdollahian, M., Yang, Z., & Nelson H. (2013). Techno-Social Energy Infrastructure Siting: Sustainable Energy Modeling Programming (SEMPro). Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 6. [HTML] (June 2013)
  • Almudí, I., Fatás-Villafranca, F. & Izquierdo, L.R. (2013).Industry dynamics, technological regimes and the role of demand. Journal of Evolutionary Economics Vol. 23, Issue 5, pp 1073-1098. [PDF]
  • Arslan, M.O. & İcan, Ö. (2013)."An Agent-Based Analysis of Tax Compliance for Turkey." Anadolu University Journal of Social Sciences, 13(2), 143-152. [PDF]
  • Arslan, M.O. & İcan, Ö. (2013)."The Effects of Neighborhood on Tax Compliance Rates: Evidence from an Agent-Based Model." Journal of Cukurova University Institute of Social Sciences, 22(1), 337-350. [PDF]
  • Bajracharya, K. & Duboz, R. (2013). Comparison of Three Agent-Based Platforms on the Basis of a Simple Epidemiological Model (WIP). In the Proceedings of the Symposium on Theory of Modeling & Simulation, DEVS Integrative M&S Symposium, San Diego, CA. [PDF]
  • Bezirgiannis, N. (2013).Improving Performance of Simulation Software Using Haskell's Concurrency & Parallelism. Universiteit Utrecht. [HTML] (Sept 2013)
  • Biondo, A.E., Pluchino, A., & Rapisarda, A. (2013). Return Migration After Brain Drain: A Simulation Approach. Journal of Artificial Societies and Social Simulation (JASSS), 16(2), 11. [HTML]
  • Boone, Randall B., Moore, John C., Koyama, Akihiro, Holfelder, Kirstin (2013, November 21). "Soil microbe-predator model with enzymes" (Version 1). CoMSES Computational Model Library.
  • Borsboom, D, & Cramer, A. O. (2013).Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121.[HTML]
  • Bravo, R. and D.E. Axelrod. A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments. Theoretical Biology and Medical Modeling 10:66 (2013), DOI: 10.1186/1742-4682-10-66, [HTML]
  • Collard, P., Mesmoudi, S., Ghetiu, T., & Polack, F. (2013).Emergence of Frontiers in Networked Schelling Segregationist Models. Journal Complex Systems, 22(1). [HTML]
  • Collard, P., Verel, S. & Clergue, M. (2013).Systèmes Complexes: une introduction par la pratique [Complex Systems: An Introduction to Practice]. Presses Polytechniques et Universitaires Romandes. Publisher of the EPFL Press, Lausanne [France], 306 p. (ISBN: 978-2-88074-991-0)
  • Damaceanu, R-C. (2013).Agent-Based Computational Economics Using NetLogo. Bentham Science Publishers. [HTML]
  • Decker, W., Cromartie, J., Brandon, D., Warden, R., Ward, M. O., & Ryder, E. F. (2013). Simulation of embryonic development in C. elegans using agent-based modeling, International C. elegans Meeting, University of California, Los Angeles, CA, United States.
  • Delaney, L., Kleczkowski, A., Maharaj, S., Rasmussen, S., & Williams, L. (2013). Reflections on a Virtual Experiment Addressing Human Behavior During Epidemics. Summer Computer Simulation Conference. ACM Digital Library. [HTML]
  • Demarest, J., Pagsuyoin, S., Learmonth, G., Mellor, J., & Dillingham, R. (2013). Development of a Spatial and Temporal Agent-Based Model for Studying Water and Health Relationships: The Case Study of Two Villages in Limpopo, South Africa. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 3. [HTML] (Oct 2013)
  • Duarte Olson, I. (2013). Cultural Differences Between Favela and Asfalto in Complex Systems Thinking. Journal of Cognition and Culture, 13(1-2), 145-157. doi:10.1163/15685373-12342089 [PDF]
  • Duarte Olson, I. (2013). Inequality in Levels: Navigating Everyday Complexity. In Ojalehto,B., Medin, D. Conceptualizing Complex Systems: How People Navigate Social and Ecological Systems. Symposium accepted to the Society for Anthropological Sciences Meeting.
  • Duarte Olson, I. (2013). "It's like an epidemic, it catches on": Community Knowledge of Everyday Complex Phenomena. In C. Lee and G. Saxe. (Co-chairs). Capitalizing on Knowledge Co-Constructed via the Praxis of Historically Nondominant Groups. Symposium accepted to the American Educational Research Association Conference.
  • Duckham, M. (2013). Decentralized Spatial Computing: Foundations of Geosensor Networks.Springer. [HTML]
  • Dykstra, P., Elsenbroich, C., Jager W., de Lavalette, G. R., & Verbrugge, R. (2013). Put Your Money Where Your Mouth Is: DIAL, A Dialogical Model for Opinion Dynamics. Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 4. [HTML] (June 2013)
  • Frank, B.M. & Baret, P.V. (2013). "Simulating brown trout demogenetics in a river/nursery brook system: The individual-based model DemGenTrout." Ecological Modelling, 248, 184-202. [HTML]
  • Frey, S. & Goldstone, R.L. (2013). Cyclic game dynamics driven by iterated reasoning. PLOS ONE, 8(2): 2-11. [PDF]
  • Galic, N., G. M. Hengeveld, P. J. Van den Brink, A. Schmolke, P. Thorbek, E. Bruns, & H. M. Baveco. (2013). Persistence of Aquatic Insects across Managed Landscapes: Effects of Landscape Permeability on Re-Colonization and Population Recovery. PLoS ONE 8:e54584.
  • Ghorbani, A., Bots, P., Dignum, V. and Dijkema, G. (2013). MAIA: a Framework for Developing Agent-Based Social Simulations. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 9. [HTML]
  • Ginovart, M. (2013).Discovering the Power of Individual-Based Modelling in Teaching and Learning: The Study of a Predator-Prey System. Journal of Science Education and Technology (JSET). [HTML]
  • Gkiolmas, A., Karamanos, K., Chalkidis, A., Skordoulis, C., Papaconstantinou, M., & Stavrou, D. (2013). Using Simulations of NetLogo as a Tool for Introducing Greek High-School Students to Eco-Systemic Thinking. Advances In Systems Science and Applications, 13(3), 275-297. [PDF]
  • Grüter, C., Schürch, R., Farina, W.M. (2013). Task-partitioning in insect societies: Non-random direct material transfers affect both colony efficiency and information flow. J. Theor. Biol., 327, 23–33. doi:10.1016/j.jtbi.2013.02.013
  • Gulden, T. R. (2013). Agent-Based Modeling as a Tool for Trade and Development Theory. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 1. [HTML]
  • Guerrero, D.A., Jimenez, R.M., & Rodriguez-Colina, E. (2013). WSN simulation model with a complex systems approach. In Proceedings of the 2013 Summer Computer Simulation Conference (SCSC '13), Article No. 41.
  • Halter, C., & Levin, J. (2013). Multi-mediator models of new teacher learning through creating multimedia. Paper presented at the 2013 American Educational Research Association meetings. San Francisco, CA. [HTML]
  • Heckbert, S. (2013). MayaSim: An Agent-Based Model of the Ancient Maya Social-Ecological System. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 11. [HTML] (Oct 2013)
  • Hirsh, A., & Levy, S. T. (2013). Biking with particles: Junior triathletes’ learning about drafting through exploring agent-based models and inventing new tactics. Technology, Knowledge and Learning, 18(1-2), 9-37.
  • 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]
  • Jansson, F. (2013). Pitfalls in Spatial Modelling of Ethnocentrism: A Simulation Analysis of the Model of Hammond and Axelrod. Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 2. [HTML] (June 2013)
  • Kasmire, J., Nikolic, I., & Dijkema, G. (2013). Evolving Greenhouses: An Agent-Based Model of Universal Darwinism in Greenhouse Horticulture. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 7. [HTML] (Oct 2013)
  • Kim, Y., Zhong, W., and Chun, Y. (2013). Modeling Sanction Choices on Fraudulent Benefit Exchanges in Public Service Delivery. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 8. [HTML]
  • Lee, K., Kim, S., Kim, C. O., & Park, T. (2013). An agent-based competitive product diffusion model for the estimation and sensitivity analysis of social network structure and purchase time distribution. Journal of Artificial Socities and Social Simulation, 16(1). doi.org/10.18564/jasss.2080
  • Lee, M., Kim, S., Kim, C-O, Park, T. (2013). An Agent-Based Competitive Product Diffusion Model for the Estimation and Sensitivity Analysis of Social Network Structure and Purchase Time Distribution. Journal of Artificial Societies and Social Simulation, 16 (1): 3 [HTML]
  • Lee, T., Yao, R. (2013). Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis. Energy Policy, 52, 63–72. [PDF]
  • Levin, J., & Halter, C. (2013). Creating and using multimedia as artifacts for mediating learning.
  • Liu, C., Sibly, R.M., Grimm, V. & Thorbek, P. (2013). Linking pesticide exposure and spatial dynamics: an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes. Ecological Modelling, 248, 92-102. [PDF]
  • Ma, Y., Shen, Z., & Kawakami, M. (2013). Agent-Based Simulation of Residential Promoting Policy Effects on Downtown Revitalization. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 2. [HTML]
  • Malik, A.A., Crooks, A.T., & Root, H.L. (2013). “Can Pakistan have Creative Cities? An Agent Based Modeling Approach with Preliminary Application to Karachi.” Pakistan Strategy Support Program Working Paper 13, International Food Policy Research Institute (IFPRI), Washington, D.C. [HTML]
  • Manzo, G. (2013). “Educational Choices and Social Interactions: A Formal Model and A Computational Test.” Comparative Social Research, 30, 47-100. [PDF]
  • Martin, B.T., Jager, T., Preuss, T., Nisbet, R. & Grimm, V. (2013).Predicting population dynamics from the properties of individuals: a test of Dynamic Energy Budget theory. American Naturalist 18(4), 506-519 [PDF]
  • Mas, E., Adriano, B., & Koshimura S. (2013).An Integrated Simulation of Tsunami Hazard and Human Evacuation in La Punta, Peru. Journal of Disaster Research, 8(2), 285-295. [HTML]
  • Muscalagiu, I. (2013). MAS NetLogo Models-a. [HTML]
  • Muscalagiu, I., Popa, H.E., & Vidal, J. (2013). Clustered Computing with NetLogo for the evaluation of asynchronous search techniques. In proceedings of 12th IEEE International Conference on Intelligent Software Methodologies, Tools and Techniques (SOMET 2013) (pp. 115-120). Budapest. [HTML]
  • Muscalagiu, I., Popa, H.E., & Vidal, J. (2013). Large Scale Multi-Agent-Based Simulation using NetLogo for implementation and evaluation of the distributed constraints. In proceedings of IJCAI – DCR 2013, 23rd International Joint Conference on Artificial Intelligence - Workshop on Distributed Constraint Reasoning, Beijing.
  • Muscalagiu, I., Illes, C., & Popa, H.E. (2013). Large Scale Multi-Agent-Based Simulation using NetLogo for the multi-robot exploration problem. In IEEE 11th International Conference on Industrial Informatics, Bochum, Germany, pp. 225-230. [HTML]
  • Newman, S. D., & Soylu, F. (2013). The impact of finger counting habits on arithmetic in adults and children. Psychological research, 1-8. [HTML]
  • Passos, G.F., & Chamovitz, I. (2013). Modelo De Responsabilidade Organizacional, Aplicado Em Empresa Pública De Tecnologia Da Informação E Fundamentado Em Dinâmica De Sistemas. In: IX Congresso Nacional de Excelência em Gestão – CNEG 2013, 2013, Rio de Janeiro – RJ. [PDF]
  • Passos, G.F., Chamovitz, I., Theodoulidis, B. (2013).Organizational Responsibility Model: Dealing with Demand for Services Higher Than Installed Capacity. In: Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on. IEEE, 2013. p. 4415-4420. [HTML]
  • Pennisi, Marzio; Rajput, Abdul-Mateen; Pappalardo, Francesco; Toldo, Luca Pennisi, M.; Rajput, A.; Toldo, L.; Pappalardo, F. (2013). Agent based modeling of Treg-Teff cross regulation in relapsing-remitting multiple sclerosis. Bio Med Central [HTML]
  • Pinacho, P., Pau, I., Chacón, M., Sánchez, S. (2013). An ecological approach to anomaly detection: The EIA model. Artifical Immune Systems: Lecture Notes in Computer Science, Vol. 7597, 2012. [PDF]
  • Pontoppidan, M.B., & Nachman, G. (2013).Changes in behavioural responses to infrastructure affect local and regional connectivity - A simulation study on pond breeding amphibians. Nature Conservation, 5, 13-28. doi: 10.3897/natureconservation.5.4611
  • Pontoppidan, M.B., & Nachman, G. (2013).Effects of within-patch heterogeneity on connectivity in pond-breeding amphibians studied by means of an individual-based model. Web Ecol., 13, 21-29, doi:10.5194/we-13-21-2013
  • Pontoppidan, M.B., & Nachman, G. (2013).Spatial Amphibian Impact Assessment - A management tool for assessment of road effects on regional populations of Moor frogs (Rana arvalis). Nature Conservation, 5, 29-52. doi: 10.3897/natureconservation.5.4612
  • Ratamero, E. M. (2013). Modelling Peloton Dynamics in Competitive Cycling: A Quantitative Approach. In Sports Science Research and Technology Support (pp. 42-56). Springer International Publishing. (DOI 10.1007/978-3-319-17548-5_4).
  • Roman, B. (2013).An Agent-based Model for the Humanities. Digital Humanities Quarterly, 7 (1). [HTML]
  • Ryder, E. F., Boyd, J. R., Marsden, T. B., Sullender, M. E., Reider, D., & White, B. T. (2013). Modeling from molecules to moose: teaching students to develop agent-based simulations in biology, Supercomputing Conference 2013 (SC13), Denver, CO, United States.
  • Sengupta, P., Kinnebrew, J. S., et al. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. [HTML] (Jan 2013)
  • Shiba, N. (2013). Analysis of Asymmetric Two-Sided Matching: Agent-Based Simulation with Theorem-Proof Approach. Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 11. [HTML] (June 2013)
  • Squazzoni, F. Gandelli, C. (2013). Opening the Black-Box of Peer Review: An Agent-Based Model of Scientist Behaviour. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 3. [HTML]
  • Sun, Z., & Müller, D. (2013). A framework for modeling payments for ecosystem services with agent-based models, Bayesian belief networks and opinion dynamics models. Environmental modelling & software, 45, 15-28.[HTML]
  • Szilagyi, M.N. (2013).Determination of the potential distribution of electron optical systems by agent-based simulation. Optik 124, 3451-3452.
  • Tisue, S., & Wilensky, U. (2004, updated 2013).NetLogo: Design and implementation of a multi-agent modeling environment. In Proceedings of the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity and Emergence, Chicago, Illinois, October 2004. [PDF]
  • Tomasini, M., Zambonelli,, F., & Menezes, R. (2013).Using patterns of social dynamics in the design of social networks of sensors. Green Computing and Communications (GreenCom), IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing.
  • Tomasini, M., et al. (2013).Evaluating the Performance of Social Networks of Sensors under Different Mobility Models. Social Computing (SocialCom), International Conference on. IEEE.
  • Trouille, L., Beheshti, E., Horn, M., Jona, K., Kalogera, V., Weintrop, D., & Wilensky, U. (2013).Bringing Computational Thinking into the High School Science and Math Classroom. In American Astronomical Society, AAS Meeting #221, #201.09
  • Vinatier, F., Gosme, M., & Valantin-Morison, M. (2013).Explaining host-parasitoid interactions at the landscape scale: a new approach for calibration and sensitivity analysis of complex spatio-temporal models. Landscape Ecology, 28 (2): 217-231. [PDF]
  • Wagh. A. & Wilensky, U. (2013).Leveling the playing field: Making multi-level evolutionary processes accessible through participatory simulations. Proceedings of CSCL, Madison, Wisconsin, June 15-19
  • Wagner, N., Agrawal, V. (2013). An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster. Expert Systems with Applications, 41(6), 2807-2815. [HTML]
  • Waldeck, R. (2013). Segregated Cooperation. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 14. [HTML] (Oct 2013)
  • Weintrop, D, Hjorth, A, & Wilensky, U. (2013).Know Your Network: Learning Social Networks Analysis Through Meaningful Manipulation. Poster presented at InfoSocial 2013. Evanston, IL, USA.
  • Weisberg, M. (2013).Modeling Herding Behavior and Its Risks. Journal of Economic Methodology, 20, 6–18.
  • Weisberg, M. (2013).Simulation and Similarity: Using Models to Understand the World. Oxford University Press.
  • Westervelt, J. & Cohen, G. (2012). Ecologist-Developed Spatially-Explicit Dynamic Landscape Models. Springer.
  • White, D. & Levin, J. (2013).Navigating the Turbulent Waters of School Reform Guided by Complexity Theory. Paper presented at the meetings of the American Educational Research Association, San Francisco, California, USA. [PDF]
  • Wiens, J., & Monett, D. (2013).Using BDI-extended NetLogo Agents in Undergraduate CS Research and Teaching. In H.R. Arabnia, A. Bahrami, V.A. Clincy, L. Deligiannidis, and G. Jandieri (eds.), Proceedings of The 9th International Conference on Frontiers in Education: Computer Science and Computer Engineering, FECS 2013. CSREA Press U.S.A., pp. 396-402, Las Vegas, NV. (ISBN: 1-60132-243-7). [PDF]
  • Wilensky, U. (2001, updated 2013)Modeling nature's emergent patterns with multi-agent languages. Proceedings of EuroLogo 2001. Linz, Austria. [PDF]
  • Yang, S., Lu, M., & Ge, L. (2013, June). Bayesian Network Based Software Reliability Prediction by Dynamic Simulation. In 2013 IEEE 7th International Conference on Software Security and Reliability (pp. 13-20). IEEE [HTML]

2012

  • Abrahamson, D., Gutierrez, J.F., & Baddorf, A.K. (2012). Try to see it my way: the discursive function of idiosyncratic mathematical metaphor. Mathematical Thinking and Learning , 14(1), 55-80.
  • Almeida, J., Kokkinogenis, Z., & Rossetti, R. (2012). NetLogo implementation of an evacuation scenario. In the Proceedings of the 7th Iberian Conference on Information Systems and Technologies (CISTI).
  • Almudí, I., Fatás-Villafranca, F. & Izquierdo, L.R. (2012). Innovation, Catch-up and Leadership in Science-Based Industries. Industrial and Corporate Change, Volume 21, Number 2, pp. 345–375. [PDF]
  • Arsenault, A., Nolan, J., Schoney, R., & Gilchrist, D. (2012). Outstanding in the field: Evaluating auction markets for farmland using multi-agent simulation. Journal of Artificial Societies and Social Simulation, 15(1). doi.org/10.18564/jasss.1827
  • Barth, R., Meyer, M., & Spitzner, J. (2012). Typical pitfalls of simulation modeling - Lessons learned from armed forces and business. Journal of Artificial Societies and Social Simulation, 15(2). doi.org/10.18564/jasss.1935
  • Berkel, S. v., Turi, D., Pruteanu, A., & Dulman, S. (2012). Automatic discovery of algorithms for multi-agent systems. Paper presented at the Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, Philadelphia, Pennsylvania, USA. [PDF]
  • Bersini, H. (2012). UML for ABM. Journal of Artificial Societies and Social Simulation, 15(1). doi.org/10.18564/jasss.1897
  • Bragin, J. (2012). Book review of: Niazi, M.A., & Hussain, A. (2012). Cognitive Agent-Based Computing-I: a Unified Framework for Modeling Complex Adaptive Systems Using Agent-Based & Complex Network-Based Methods (SpringerBriefs in Cognitive Computation). Journal of Artificial Societies and Social Simulation. 16 (3) Reviews - 4 [HTML]
  • Collard, M., Collard, P., & Stattner, E. (2012). Mobility and Information Flow: Percolation in a Multi-Agent Model. International Conference on Ambient Systems, Networks and Technologies (ANT), ed. Elseiver. Procedia CS, Vol. 10, p. 22-29, 2012. [HTML]
  • Dyer, E.B. (2012, November). Exploring classroom discourse through an agent-based model. Poster presented at the 34th Annual Conference of PME-NA: Kalamazoo, MI: PME.
  • Ferguson, J. (2012). Semiotic Analysis of Students’ Use of Multi-Agent Based Modeling Software (NetLogo) in Making Meaning of Complex Systems in Science. Paper presented at the Proceedings of the Contemporary Approaches to Research in Mathematics, Science, Health and Environmental Education Symposium, Melbourne, Australia. [PDF]
  • Frenken, K., Izquierdo, L.R. & Zeppini,P. (2012). Branching innovation, recombinant innovation, and endogenous technological transitions. Environmental Innovation and Societal Transitions, Volume 4, pp. 25–35. [PDF]
  • Galic, N., H. Baveco, G. M. Hengeveld, P. Thorbek, E. Bruns, & P. J. van den Brink. (2012). Simulating population recovery of an aquatic isopod: Effects of timing of stress and landscape structure. Environmental Pollution 163:91-99.
  • Gargiulo, F., Lenormand, M., Huet, S., & and Baqueiro Espinosa, O. (2012). Commuting Network Models: Getting the Essentials. Journal of Artificial Societies and Social Simulation, 15(2). doi.org/10.18564/jasss.1964
  • Ginovart, M. & Prats, C. (2012). A bacterial individual-based virtual bioreactor to test handling protocols in a NetLogo platform. Paper presented at the Proceedings of the 7th Vienna International Conference on Mathematical Modelling, Vienna, Austria. [PDF]
  • Gkiolmas A., Chalkidis A., Karamanos K., Papaconstantinou M., Skordoulis C. (2012). A constructionist method for teaching teachers about basic properties of complex systems, using a NetLogo model. Paper presented at the Proceedings of the conference "CONSTRUCTIONISM 2012", Athens, Greece. [PDF]
  • Grüter, C., Schürch, R., Czaczkes, T.J., Taylor, K., Durance, T., Jones, S.M., Ratnieks, F.L.W. (2012). Negative feedback enables fast and flexible collective decision-making in ants. Public Libr. Sci. One 7, e44501. doi:10.1371/journal.pone.0044501zzzz
  • Hassani-Mahmooei, B. & Parris, B.W. (2012). Climate change and internal migration patterns in Bangladesh: an agent-based model. Environment and Development Economics, 17(6), 763-780. DOI: 10.1017/S1355770X12000290, Published online: 27 July 2012
  • Hassani-Mahmooei, B & Parris, B.W. (2012). Resource Scarcity, Effort Allocation and Environmental Security: An agent-based theoretical approach. Economic Modeling, 30, 183-192. DOI: 10.1016/j.econmod.2012.08.020
  • Hargrove, W. & Westerveldt, J. (2012). An Implementation of the Pathway Analysis Through Habitat (PATH) Algorithm Using NetLogo. Ecologist-developed Spatially-explicit Dynamic Landscape Models; Modeling Dynamic Systems; Springerlink pg. 211-222. [PDF]
  • Heckbert, S., Isendahl, C., Gunn, J., Brewer, S., Scarborough, V., Chase, A.F., Chase, D.Z., Costanza, R., Dunning, N., Beach, T., Luzzadder-Beach, S., Lentz D., & Sinclair, P. (2012). Growing the Maya social-ecological system from the bottom up: complex systems models of resilience. In: Handbook of Historical Ecology and Applied Archaeology, C. Isendahl & D. Stump, eds. Oxford University Press
  • Hickey, D.T., Soylu, F. (2012). Wikifolios, reflections, and exams for online engagement, understanding, and achievement. Journal of Teaching and Learning with Technology: 64-71. [PDF]
  • Hjorth, A. & Wilensky, U. (2012). Acting like a Turtle: A NetLogo Kinect Extension. Proceedings of the Constructionism 2012 Conference. Athens, Greece, Aug 21-25. [HTML]
  • Horn, M. S., Weintrop, D., Beheshti, E., & Olson, I. C. (2012). Spinners, Dice, and Pawns: Using Board Games to Prepare for Agent-Based Modeling Activities. Presented at the AERA, Vancouver, Canada. [PDF]
  • Horn, M.S. & Wilensky, U. (2012). NetTango: A mash-up of NetLogo and Tern. In Moher, T. (chair) and Pinkard, N. (discussant), When systems collide: Challenges and opportunities in learning technology mashups. Symposium presented at the annual meeting of the American Education Research Association,Vancouver, British Columbia.[PDF]
  • Janssen, M. A. & Rollins, N. (2012). Predicting behavior in new behavioral experiments: Outcomes of a modeling competition. Journal of Artificial Societies and Social Simulation, 15(3). doi.org/10.18564/jasss.2015
  • Kahn, K., Noble, H., Hjorth, A., & Sampaio, F.F. (2012). Three-minute Constructionist Experiences. Paper presented at the Proceedings of Constructionism 2012, Athens, Greece
  • Karamanos, K., Gkiolmas, A., Chalkidis, A., Skordoulis, C., Papaconstantinou, M., & Stavrou, D. (2012). Ecosystem food-webs as dynamic systems: Educating undergraduate teachers in conceptualizing aspects of food-webs’ systemic nature and comportment. Advances in Systems Science and Application 12 (4): 49-68 [PDF]
  • Kimbrough, S. (2012). Agents, Games, and Evolution. CRC Press, Boca Raton, Florida.
  • Kochanski, Tim. (2012). Toward Teaching Markets as Complex Systems: A Web Based Simulation Assignment Implemented in Netlogo. International Review of Economics Education, 11 (2), 102-114.[HTML]
  • Larrosa, Juan MC. (2012). Algoritmos evolutivos en juegos de formación de red. Una aplicación en NetLogo. [Evolutive Algorithms in Network Formation Games: A NetLogo Application]. Editorial Académica Española: Saarbrucker (Germany), 168 p. (ISBN-13: 978-3-659-05434-1)
  • Larrosa, Juan MC. (2012). Introducción a la economía computacional basada en agentes con aplicaciones NetLogo. [Introduction to Agent-Based Computational Economics with NetLogo Applications]. EdiUNS: Bahía Blanca (Argentina). 216 p. (ISBN: 978-987-1907-15-1)
  • Laver, M. & Sergenti, E. (2012). Party Competition: An Agent-Based Model. Princeton University Press.
  • Le Page, C., Becu, N., Bommel, P., & Bousquet, F. (2012). Participatory Agent-Based Simulation for Renewable Resource Management: The Role of the Cormas Simulation Platform to Nurture a Community of Practice. Journal of Artificial Societies and Social Simulation (JASSS), 15 (1), 10.
  • Levin, J., & Datnow, A. (2012). Multiple mediator models of educational reform: Organizational learning as persistent change. Paper presented at the 2012 American Educational Research Association meetings. Vancouver Canada.
  • Levin, J. & Datnow, A. (2012). "The Principal Role in Data Driven Decision Making: Using Case Study Data to Develop Multi-Mediator Models of Educational Reform." School Effectiveness and School Improvement. 23 (2), 179-201 [PDF]
  • Leykum, L., Kumar, P., Parchman, M., McDaniel, R. R., Lanham, H., & Agar, M. (2012). Use of an agent-based model to understand clinical systems. Journal of Artificial Societies and Social Simulation, 15(3). doi.org/10.18564/jasss.1905
  • Lin, Y., Berger, U., Grimm, V., Ji, Q-R. (2012). Differences between symmetric and asymmetric facilitation matter: exploring the interplay between modes of positive and negative plant interactions. Journal of Ecology. DOI: 10.1111/j.1365-2745.2012.02019.x
  • Liu, C., Sibly, R.M., Grimm, V. & Thorbek, P. (2012). Linking pesticide exposure and spatial dynamics: an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes. Ecological Modelling.
  • Lytinen, S. L. and S. F. Railsback. (2012). "The Evolution of Agent-based Simulation Platforms: A Review of NetLogo 5.0 and ReLogo." To appear in Proceedings of the Fourth International Symposium on Agent-Based Modeling and Simulation, at the 21st European Meeting on Cybernetics and Systems Research (EMCSR 2012), Vienna, Austria, April 2012.
  • Maharaj, S., Kleczkowski, A. (2012). Controlling Epidemic Spread by Social Distancing: Do it well or not at all. BMC Public Health 12:679 [HTML]
  • Martin, B.T., Jager, T., Preuss, T., Nisbet, R. & Grimm, V. (2012). Predicting population dynamics from the properties of individuals: a test of Dynamic Energy Budget theory. American Naturalist, 18(4), 506-519.
  • Martin, B.T., Zimmer, E.I., Grimm, V. & Jager, T. (2012). Dynamic Energy Budget theory meets individual-based modelling: a generic and accessible implementation. Methods in Ecology and Evolution, 3, 445-449.
  • Mialhe, F., Becu, N., & Gunnell, Y. (2012). An agent-based model for analyzing land use dynamics in response to farmer behaviour and environmental change in the Pampanga delta (Philippines). Agriculture, Ecosystems & Environment, 161, 55-69. [HTML]
  • Montes, G. (2012). Using Artifical Societies to Understand the Impact of Teacher Student Match on Academic Performance: The Case of Same Race Effects. Journal of Artificial Societies and Social Simulation, 15(4), 8 [HTML]
  • Muldoon, R., Smith, T., & Weisberg, M. (2012). Segregation That No One Seeks. Philosophy of Science, 79, 38–62.
  • Muscalagiu, I., Iordan, A., Osaci, M., & Panoiu, M. (2012). Modeling and simulation of the protein folding problem in DisCSP-Netlogo. AWERProcedia Information Technology and Computer, 2.
  • Niazi, M. A. & Hussain, A. (2012). Cognitive agent-based computing-I: A unified framework for modeling complex adaptive systems using agent-based & complex network-based methods. Springer-Verlag.
  • Patel, A., Crooks, A., & Koizumi, N. (2012). Slumulation: An Agent-Based Modeling Approach to Slum Formulations. Journal of Artificial Societies and Social Simulation (JASSS), 15 (4), 2. [HTML] (Oct 2012)
  • Pinacho, P., Pau, I., Chacón, M., & Sánchez, S. (2012). “An ecological approach to anomaly detection: the EIA model” in Artificial Immune Systems: Lecture Notes in Computer Science, 7597, 232-245. [HTML]
  • Purnomo H, Suyamto D, Irawati RH. (2012). Harnessing the climate commons: an agent-based modelling approach to making reducing emission from deforestation and degradation (REDD)+work. Mitigation and Adaptation Strategies for Global Change.
  • Ramírez, R., Harris, P.D., Bakke, T.A. (2012). An agent-based modelling approach to estimate error in gyrodactylid population growth. International Journal for Parasitology, 2(9), 809-17. doi: 10.1016/j.ijpara.2012.05.012 [PDF]
  • Rhee, J. M. & Iannaccone, P. M. (2012). Mapping mouse hemangioblast maturation from headfold stages. Developmental Biology, 365 (1), 1-13. doi:10.1016/j.ydbio.2012.02.023 [PDF]
  • Ross, J. (2012). A comparative study of simulation software for modeling stability operations. In the Proceedings of the 2012 Symposium on Military Modeling and Simulation. [PDF]
  • Schindler, J. (2012). Rethinking the Tragedy of the Commons: The Integration of Socio-Psychological Dispositions. Journal of Artificial Societies and Social Simulation (JASSS), 15 (1): 4.
  • Schwarz, N., Kahlenberg, D., Haase, D., & Seppelt, R. (2012). ABMland - A tool for agent-based model development on urban land use change. Journal of Artificial Societies and Social Simulation, 15(2). doi.org/10.18564/jasss.1875
  • Smojver, S. (2012). Analysis of banking supervision via inspection Game and agent-based modeling. In the Proceedings of the 2012 Central European Conference on Information and Intelligent Systems, Varaždin, Croatia, Sep 19 - 21, 2012: 355-362 [PDF]
  • Thiele, J., Kurth, W. & Grimm, V. (2012). Agent-based modelling: Tools for linking NetLogo and R. Journal of Artificial Societies and Social Simulation (JASSS), 15 (3): 8. [HTML]
  • Thiele, J. C., Kurth, W. & Grimm, V. (2012). RNetLogo: an R package for running and exploring individual-based models implemented in NetLogo. Methods in Ecology and Evolution, 3, 480-483.
  • Viale, R. (2012). Methodological Cognitivism: Vol 1: Mind, Rationality, and Society. Springer-Verlag.
  • 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]
  • 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]
  • 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. & Wilenky, U. (2012). Breeding birds to learn about artificial selection: Two birds with one stone? Proceedings of ICLS, Sydney, Australia, July 2-6.
  • Wagh, A. & Wilensky, U. (2012). Mechanistic Explanations of evolutionary change facilitated by agent-based models. Paper presented at AERA, Vancouver, April 13-17.
  • 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]
  • 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).
  • Yalvac, B., Ayar, M. C., & Soylu, F. (2012). Teaching Engineering with Wikis. International Journal of Engineering Education, 28(3), 701. [PDF]
  • Zeng, M. (2012). Based on NetLogo Simulation for Credit Risk Management. Advances in Computer Science and Engineering, 141, pp. 395-401. [PDF]

2011

  • 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).
  • 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]
  • 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]
  • 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
  • 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]
  • 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]
  • 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.
  • 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]
  • 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.]
  • 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]
  • Dixon, D. S. (2011)."Preliminary results from as agent-based adaptation of friendship games." 86th Annual Conference of Western Economics. [PDF]
  • 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]
  • 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]
  • 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.
  • Gabbreillini, S. (2011).Simulare meccanismi sociali con NetLogo: Una introduzione. Methodology and Techniques of Social Research. E-book [HTML].
  • 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.
  • 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]
  • 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]
  • 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
  • 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.
  • 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)
  • Kottonau, J. (2011). An interactive computer model for improved student understanding of random particle motion and osmosis. Journal of Chemical Education, 88(6), 772-775.[HTML]
  • Kumarappan, S. (2011)."Spatial pricing patterns of cellulosic biomass under oligopsony - A multi-agent simulation model." Paper presented at the 2011 AAEA & NAREA Joint Annual Meeting.
  • Kurve, A. & Kesidis, G. (2011)"Sybil Detection via Distributed Sparse Cut Monitoring." Proceedings of the 2011 IEEE international Conference on Communications (ICC), 5-9 June 2011. [PDF]
  • Lamy, F., Bossomaier, T., & Perez, P. (2011)"SimUse: Simulation of recreational poly-drug use." Proceedings of the 2011 IEEE Symposium on Artificial Life (ALIFE), 11-15 April 2011, pp. 170-177. [PDF]
  • Lamy, F., Perez, P., Ritter, A. & Livingston, M. (2011)"SimARC: An ontology-driven behavioural model of alcohol abuse." Proceedings of the Third International Conference on Advances in System Simulation, Oct. 23-29, pp. 128-133. [PDF]
  • Levin, J. A. (2011). Landmark representation and dynamic mediation. [HTML]
  • Levy, S. T. & Wilensky, U. (2011).Mining students inquiry actions for understanding of complex systems. ScienceDirect Alert: Computers & Education, Vol. 56, Iss. 3, 2011. pp. 556-573. [PDF]
  • Li, Y., Wu, J., Ping, Y., Kou, W., & Han, Z. (2011)."NetLogo-based simulation and analysis of college confidential project management." Computer Technology and Development.
  • Maharaj, S., McCaldin, T., Kleczkowski, A. (2011).A Participatory Simulation Model for Studying Attitudes to Infection Risk. In Proceedings of the Summer Computer Simulation Conference 2011, ACM Digital Library, July 2011. [PDF]
  • Manzo, G. (2011)“Relative Deprivation in Silico: Agent-based Models and Causality in Analytical Sociology.” in P. Demeulenaere (ed.), Analytical Sociology and Social Mechanisms, Cambridge, Cambridge University Press, ch. 13, 266-308 [PDF]
  • Marquez, B. Y., Espinoza-Hernandez, I., Castanon-Puga, M., Castro, J.R., & Suarez, E. D. (2011)"Distributed Agencies Applied to Complex Social Systems, a Multi-Dimensional approach." Proceedings of 2011 The 2nd International Conference on Next Generation Information Technology (ICNIT), 21-23 June 2011, pp. 213-219. [PDF]
  • McDonnell, S. and M. Zellner (2011)."Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior." Transport Policy 18(6): 825-835.
  • Mölders, M., Fink, R. D., & Weyer, J. (2011). Modeling scientists as agents: How scientists cope with the challenges of the new public management of science. Journal of Artificial Societies and Social Simulation, 14(4). doi.org/10.18564/jasss.1831
  • Nan, N. (2011). "Capturing bottom-up information technology use processes: A Complex adaptive systems model." Management Information Systems Quarterly, 35(2), 505-532.
  • Niazi, M.A.K. (2011). "Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems." University of Stirling. [PDF]
  • Niazi, M. A.; Hussain, A. (2011). "A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments." Sensors Journal, IEEE, 11(2) doi: 10.1109/JSEN.2010.2068044. [PDF]
  • Noor, Talal H.; Sheng, Quan Z. (2011). "Trust as a Service: A Framework for Trust Management in Cloud Environments." Proceedings of the 2011 12th International Conference on Web Information System Engineering (WISE), 12-14 October 2011, pp. 314-321. [PDF]
  • Olson, I.C., & Horn, M. (2011). Modeling on the Table: Agent-Based Modeling in Elementary School with NetTango. Proceedings of 10th International Conference on Interaction Design and Children (short paper), Ann Arbor, MI. June, 2011
  • Olson, I.C., Leong, Z.A., Wilensky, U., & Horn, M.S. (2011). “It’s just a toolbar!” Using tangibles to help children manage conflict around a multi-touch tabletop. In Proc. of the fifth international conference on Tangible, Embedded and Embodied Interaction (TEI’11) , Funchal, Portugal. ACM New York. pp. 29-36.
  • Otcenaskova, T., Bures, V., & Cech, P. (2011). "Multi-agent simulations in decision support: Specifics of the biological incident management. " Mathematics and Computers in Biology, Business, and Acoustics. [PDF]
  • Patarakin, E., Yarmakhov B., Burov V. (2011). "Agent based simulation activities within the wiki system." Educational Technology & Society. pp.407 - 422. (In Russian) [PDF]
  • Pereda, M. & Zamarreno, J. M. (2011). "Agent-based modeling of an activated sludge process in a batch reactor." Proceedings of the 2011 19th Mediterranean Conference on Control & Automation (MED), 20-23 June 2011, pp. 1128-1133. [PDF]
  • Petreska, I., Kefalas, P., Gheorghe, M. (2011). "A framework towards the verification of emergent properties in spatial multi-agent systems." Proceedings of the Workshop on Applications of Software Agents, pp. 37-44.
  • Portugali, J. (2011). Complexity, Cognition and the City. Springer-Verlag.
  • Railsback, S. F. & Grimm, V. (2011). Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press.
  • Rebaudo, F., Crespo-Pérez, V., Silvain, J-F., & Dangles, O. (2011). Agent-Based Modeling of Human-Induced Spread of Invasive Species in Agricultural Landscapes: Insights from the Potato Moth in Ecuador. Journal of Artificial Societies and Social Simulation (JASSS), 14 (3) 7. [HTML] (June 2011)
  • Rush, D. (2011)."A simulation of evolving sustainable technology through social pressure." Proceedings of the 2011 AAAI Fall Symposium, pp. 117-126. [PDF]
  • Salamon, T. (2011)."Design of Agent-Based Models: Developing Computer Simulations for a Better Understanding of Social Processes." Repin, Czech Republic: Bruckner Publishing. [Website]
  • Sengupta, P., & Wilensky, U. (2011).Lowering the Learning Threshold: Multi-Agent-Based Models and Learning Electricity. In Khine, M.S., & Saleh, I.M (Eds.). Dynamic Modeling: Cognitive Tool for Scientific Inquiry. Springer, New York, NY. [PDF]
  • Shelley, T., Lyons, L., Zellner, M. & Minor, E.(2011)"Evaluating the embodiment benefits of a paper-based tui for educational simulations." Proceedings of CHI Extended Abstracts 2011, pp. 1375-1380. [PDF]
  • 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]
  • 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]
  • 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]
  • 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
  • Vattam, S. S., Goel, A. K., Rugaber, S. (2011)."Behavior Patterns: Bridging conceptual models and agent-based simulations in interactive learning environments." Proceedings of the 2011 IEEE 11th International Conference on Advanced Learning Technologies, pp.139-141
  • Vinatier, F., Lescurret, F., Duyck, P.-F., Martin, O., Senoussi, R., & Tixier, P. (2011).Should I Stay or Should I Go? A Habitat-Dependent Dispersal Kernel Improves Prediction of Movement. PLoS ONE, 6, e21115.
  • Wildman, W., & Sosis, R. (2011). Stability of Groups with Costly Benefits and Practices. Journal of Artificial Societies and Social Simulation (JASSS), 14 (3) 6. [HTML] (June 2011)
  • Xia, H., Jia, Z., Ju, L., Li, X., & Zhu, Y. (2011)."A subjective trust management model with multiple decision factors for MANET based on AHP and fuzzy logic rules." Proceedings of 2011 IEEE/ACM International Converence on Green Computing and Communications (GreenCom), 4-5 Aug. 2011, pp. 124-130. [PDF]
  • Zhao, M. (2011)."A simulation system of social economic." Computer and Information Science, 4(5). [PDF]

2010

  • 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). 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]
  • 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).
  • 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.
  • 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. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • 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.
  • 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., 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.
  • 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.
  • 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 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.
  • 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
  • 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.
  • 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.
  • 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).
  • 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.
  • Stonedahl, F. & Rand, W., & Wilensky, U. (2010). Evolving Viral Marketing Strategies. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. Portland, OR.
  • 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., & 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.
  • 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.
  • 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). Understanding evolution as an emergent process: learning with agent-based models of evolutionary dynamics. In R.S. Taylor & M. Ferrari (Eds.), Epistemology and Science Education: Understanding the Evolution vs. Intelligent Design Controversy. New York: Routledge.
  • 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). Restructuring Change, Interpreting Changes:The DeltaTick Modeling and Analysis Toolkit. Paper to be presented at Constructionism 2010, Paris.
  • 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.
  • 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

  • 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.
  • 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]
  • 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.
  • 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.
  • 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
  • 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.
  • 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]
  • Schultz, L., Koons, P. O., & Schauffler, M. (2009). Middle-School Understanding of the Greenhouse Effect using a NetLogo Computer Model. AGUFM, 2009, ED23A-0545.
  • 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, 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]
  • 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.
  • 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

  • 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
  • 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.
  • Colosimo, A. (2008). Biological Simulations by Autonomous Agents: Two Examples Using the NetLogo Environment. Biophysics and Bioengineering Letters, 1(3).[PDF]
  • 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. & Bushman, C. (2008). Evolution of cooperation and altruistic punishment when retaliation is possible. Journal of Theoretical Biology 254: 451-455.
  • 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).
  • 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). 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). 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 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.
  • 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]
  • 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). 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]
  • 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]
  • 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]
  • 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]
  • 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.
  • 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

  • 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). 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). '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). 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). 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). 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). 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]
  • 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]
  • 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). 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. & Wilensky, U. (2006). NetLogo 3.1: Low Threshold, No Ceiling. Paper presented at NAACSOS 2006, South Bend, IN, June 2006.
  • 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.
  • 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]
  • 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

  • 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., 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.
  • 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). 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). 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. & 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]
  • 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.
  • 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.
  • Shaker, A., Reeves, D.S. (2005). Self-Stabilizing Structured RingTopology P2P System. Paper presented at the Fifth IEEE International Conference on Peer-to-Peer Computing (P2P'05). [PDF]
  • Stieff, M., Bateman Jr., R. C., Uttal, D. (2005). Teaching and Learning with Three-dimensional Representations. In J. K. Gilbert (Ed.) Models and Modeling in Science Education (Vol 1, 93-120). Springer: Netherlands. [PDF]
  • Stirling, D. (2005). Modeling complex systems. Paper submitted to the Advanced International Colloquium on Building the Scientific Mind. [PDF]
  • Vidal, J. M. (2005). NetLogo for Building Prototype Multiagent Systems. [PDF]
  • Wheeler, S. (2005). It pays to be popular: A study of civilian assistance and guerilla warfare. Journal of Artificial Societies and Social Simulation, 8(4).
  • Wheeler, S. (2005). On the Suitability of NetLogo for the Modelling of Civilian Assistance and Guerrilla Warfare. DSTO Science Systems Laboratory. [PDF]
  • Xie, C. (2005). Molecular Dynamics Simulations Beyond the Lennard-Jones Particles. Submitted to the American Journal of Physics. [PDF]

2004

  • 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). 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., & 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., 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). An Anthropological Problem, A Complex Solution. Human Organization, 63(4), 411-418. [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]
  • 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]
  • 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]
  • Cacciaguerra, S., Roccetti, M., Roffilli, M., & Lomi, A. (2004). A Wireless Software Architecture for Fast 3D Rendering of Agent-Based Multimedia Simulations on Portable Devices. Paper presented at the First Consumer Communications and Networking Conference (CCNC), IEEE Communications Society, Las Vegas, Nevada (USA), January 2004. [PDF]
  • Chitnis, A.B. & Itoh, M. (2004). Exploring alternative models of rostral-caudal patterning in the zebrafish neurectoderm with computer simulations. Current Opinion in Genetics & Development 14(4), 415-421.
  • Densmore, O. (2004). NetLogo 2.0: Graphs, Nodes, and Edges Oh My! January 12, 2004, O'Reilly.com.
  • Friedman, D., & Abraham, R. (2004). Landscape Dynamics and Conspicuous Consumption. Paper presented at the 2004 Proceedings of the Society for Dynamic Games. [PDF]
  • Goldstone, R. L. (2004). The complex systems see-change in education. Journal of the Learning Sciences, 15 (1), pp 35 - 43. [PDF]
  • Gross, T. S., Poliachik, S. L., Ausk, B. J., Sanford, D. A., Becker, B. A., & Srinivasan, S. (2004). Why rest stimulates bone formation: a hypothesis based on complex adaptive phenomenon. Exerc Sport Sci Rev, 32(1), 9-13. [PDF]
  • Henein, C.M., & White, T. (2004). Agent-based modelling of forces in crowds. In Lecture Notes in Computer Science Vol. 3415, 2005. [PDF]
  • Ingalls, R. G., Rossetti, M. D., Smith, J. S., & Peters, B. A. (2004). Simulation in context: Using data farming for decision support. Paper presented at the 2004 Winter Simulation Conference. [PDF]
  • Kottonau, J. (2006, based on 2004 paper). Simulation einer Ameisenstraße mit NetLogo. Lehrer-online. [HTML]
  • Le, Q. B., Park, S., & Vlek, P. L. G. (2004). Simulating Spatial Patterns of Land-use and Land-cover Change: A Multi-agent Model and its Application to an Upland Watershed in Central Vietnam. In K. J. Peters, D. Kirschke, W. Manig, A. Bürkert, R. Schultze-Kraft, L. Bharati, C. Bonte-Friedheim, A. Deininger, N. Bhandari, H. Weitkamp (Eds.), "Rural Poverty Reduction through Research for Development and Transformation" - Proceedings of the Deutscher Tropentag 2004 (p. 67). Berlin: Humboldt-Universität. [PDF]
  • Lechner, T., Watson, B., Wilensky, U., & Felsen, M. (2004). Procedural modeling of land use in cities. Technical report NWU-CS-04-38. Evanston, IL: Northwestern University, Computer Science department. [PDF]
  • 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]
  • 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: 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]
  • 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]
  • 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). Decision Chemistry Part II: The Emergence of Routines. [HTML]
  • Lomi, A., & Cacciaguerra, S. (2003, April). Organizational decision chemistry on a lattice. In proc. of the 7th Annual Swarm Users/Researchers Conference.
  • 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). Participatory Simulations guide for Computer-HubNet. Evanston, IL, Center for Connected Learning and Computer Based Modeling, Northwestern University. (Updated 2004, 2005) [PDF]
  • 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.

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.
  • 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: 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]
  • 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)

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). 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]
  • 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]

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., 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.
  • 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]
  • 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. (2000) Modeling Emergent Phenomena with StarLogoT. @CONCORD.org, Winter 2000.

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. 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]