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Publication Library

This library intends to include as many key (must-read!) ABM related publications as possible, including journal articles, book chapters, book information, reports, and other types of papers. We will keep updating this page. Please send your input to abm17@complexities.org. Your contribution is highly appreciated!


  • Crabtree, S. A., R. K. Bocinsky,  P. L. Hooper, S. C. Ryan, and T. A. Kohler (2017). How to make a polity (in the central Mesa Verde region). American Antiquity 82(1): 71-95.
  • Edmonds, B. (in press). Different modelling purposes. In Edmonds, B. and Meyer, R. (eds.), Simulating Social Complexity: A Handbook. 2nd Edition. Springer.
  • Groeneveld, J., B. Muller, C. M. Buchmann, G. Dressler, C. Guo, N. Hase, F. Hoffmann, F. john, C. Klassert, T. Lauf, V. Liebelt, H. Nolzen, N. Pannicke, J. Schulze, H. Weise, and N. Schwarz (2017). Theoretical foundations of human decision-making in agent-based land use models – a review. Environmental Modelling & Software 87: 39-48.
  • Lee, J., and X. Ye (2017). Obesity Prevalence Simulator: An Open Source Spatiotemporal Model for Simulating Obesity Prevalence. In Thill, J. and Dragicevic, S. (eds.) Springer’s Series on Advances in Geographic Information Science.
  • Polhill, G. and D. Salt (in press). The importance of ontological structure: why validation by ‘fit-to-data’ is insufficient. In Edmonds, B. and Meyer, R. (eds.), Simulating Social Complexity: A Handbook. 2nd Edition. Springer.
  • Railsback, S., D. Ayllon, U. Berger, V. Grimm, S. Lytinen, C. Sheppard, and J. Thiele (2017). Improving execution speed of models implemented in Netlogo. Journal of Artificial Societies and Social Simulation 20(1): 3.
  • Schlüter, M., A. Baeza, G. Dressler, K. Frank, J. Groeneveld, W. Jager, M. A. Janssen, R. R. J. McAllister, B. Muller, K. Orach, N. Schwarz, and N. Wijermans (2017). A framework for mapping and comparing behavioral theories in models of social-ecological systems. Ecological Economics 131: 21-35.
  • Tian, Q. (2017). Chapter 7. The complex systems approach to policy analysis. In Rural sustainability: A complex systems approach to policy analysis. New York: Springer.
  • Wallentin, G. (2017). Spatial simulation: A spatial perspective on individual-based ecology – a review. Ecological Modelling 350: 30-41.
  • Ye, X., L. Dang, J. Lee, and M. Tsou (2017). Open source spatial meme diffusion simulation toolkit, In S. Shaw and D. Sui (eds.) Human Dynamics in the Changing World. Springer.


  • Colta, C. R. (2016). Social preferences, learning, and the dynamics of cooperation in networked societies: A dialogue between experimental and computational approaches. PhD dissertation.
  • Drogoul, A., N. Q. Huynh, and Q. C. Truong (2016). Coupling environmental, social and economic models to understand land-use change dynamics in the Mekong Delta. Frontiers in environmental Science 4: 19.
  • Grimm, V., and U. Berger (2016). Robustness analysis: Deconstructing computational models for ecological theory and applications. Ecological Modeling 326: 162-167.
  • Grimm, V., and U. Berger (2016). Structural realism, emergence, and predictions in nee-generation ecological modeling: Synthesis from a special issue. Ecological Modeling 326: 177-187.
  • Li, Y., J. Berenson, A. Gutiérrez, and J. A. Pagán (2016). Leveraging the food environment in obesity prevention: the promise of systems science and agent-based modeling. Current Nutrition Report 5(4): 245-254.
  • Li, Y., M. Lawley, D. S. Siscovick, D. Zhang, and J. A. Pagán (2016). Agent-based modeling of chronic diseases: A narrative review and future research directions. Preventing Chronic Disease 13:150561.
  • Li, Y., D. Zhang, and J. A. Pagán (2016). Social norms and the consumption of fruits and vegetables across New York City neighborhoods. Journal of Urban Health 93(2): 244-255.
  • O’Sullivan, D., T. Evans, S. Manson, S. Metcalf, A. Ligmann-Zielinska, and C. Bone (2016). Strategic directions for agent-based modeling: Avoiding the YAAWN syndrome. Journal of Land Use Science 11(2): 177-187.
  • Radinsky, J., D. Milz, M. Zellner, K. Pudlock, C. Witek, C. Hoch, and L. Lyons (2016). How planners and stakeholders learn with visualization tools: Using learning sciences methods to examine planning processes. Journal of Environmental Planning and Management (2016): 1-28.
  • Rooy, D. V., I. Wood, and E. Tran (2016). Modelling the emergence of shared attitudes from group dynamics using an agent-based model of social comparison theory. Systems Research and Behavioral Science 33: 188-204.
  • Schill, C., N. Wijermans, M. Schluter, and T. Lindahl (2016). Cooperation is not enough–Exploring social-ecological micro-foundations for sustainable common-pool resource use. PLOS ONE.
  • Shaw, S., M. Tsou, and X. Ye (2016). Human dynamics in the mobile and big data era. International Journal of Geographical Information Science 30(9): 1687-1693.
  • Sohn, D., and N. Geidner (2016). Collective dynamics of the spiral of silence: The role of ego-network size. International Journal of Public Opinion Research 28(1): 25-45.
  • Ten Broeke, G., G. van Voorn, and A. Ligtenberg (2016). Which sensitivity analysis method should I use for my agent-based model? Journal of Artificial Societies and Social Simulation 19(1): 5.
  • Tian, Q., J. H. Holland, and D. G. Brown (2016). Social and economic impacts of subsidy policies on rural development in the Poyang Lake region, China: Insights from an agent-based model. Agricultural Systems 148: 12-27.
  • Van Vliet, J., A. K. Bregt, D. G. Brown, H. van Delden, S. Heckbert, and P. H. Verburg (2016). A review of current calibration and validation practices in land-change modeling. Environmental Modeling & Software 82: 174-182.
  • Verburg, P. H., J. A. Dearing, J. G. Dyke, S. van der Leeuw, S. Seitzinger, W. Steffen, and J. Syvitski (2016). Methods and approaches to modeling the Anthropocene. Global Environmental Change 39: 328-340.
  • Walsh, S. J., and C. F. Mena (2016). Interactions of social, terrestrial, and marine sub-systems in the Galapagos Islands, Ecuador. PNAS 113(51): 14536-14543.
  • Ye, X., and J. Lee (2016). Integrating geographic activity space and social network space to promote healthy lifestyles. ACMSIGSPATIAL Health GIS, Newsletter 8(1): 24-33.
  • Ye, X., and Y. Mansury (2016). Behavior-driven agent-based models of spatial systems. Annals of Regional Science 57: 271-274.


  • Bell, A. R., D. T. Robinson, A. Malik, and S. Dewal (2015). Modular ABM development for improved dissemination and training. Environmental Modeling & Software 73: 189-200.
  • Crooks, A., A. Croitoru, X. Lu, S. Wise, J. M. Irvine, and A. Stefanidis (2015). Walk this way: Improving pedestrian agent-based models through scene activity analysis. ISPRS International Journal of Geo-Information 4(3): 1627-1656.
  • Ghorbani, A., G. Dijkema, and M. Schrauwen (2015). Structuring qualitative data for agent-based modeling. Journal of Artificial Societies and Social Simulation 18(1): 2.
  • Guillem, E. E., D. Murray-Rust, D. T. Robinson, A. Barnes, and M. D. A. Rounsevell (2015). Modeling farmer decision-making to anticipate tradeoffs between provisioning ecosystem services and biodiversity. Agricultural Systems 137: 12-23.
  • Hoch, C., M. Zellner, D. Milz, J. Radinsky, and L. Lyons (2015). Seeing is not believing: Cognitive bias and modelling in collaborative planning. Planning Theory & Practice 16(3): 319-335.
  • Lee, J., T. Filatova, A. Ligmann-Zielinska, B. Hassani-Mahmooei, F. Stonedahl, I. Lorscheid, A. Voinov, G. Polhill, Z. Sun, and D. C. Parker (2015). The coplexities of agent-based modeling output analysis. Journal of Artificial Societies and Social Simulation 18(4): 4.
  • Li, Y., N. Kong, M. Lawley, L. Weiss, and J. A. Pagán (2015). Advancing the use of evidence-based decision making in local health departments with systems science methodologies. American Journal of Public Health 105(s2): s217-s222.
  • Magliocca, N. R., J. van Vliet, C. Brown, T. P. Evans, T. Houet, P. Messerli, J. P. Messina, K. A. Nicholas, C. Ornetsmuller, J. Sagebiel, V. Schweizer, P. H. Verburg, and Q. Yu (2015). From meta-studies to modeling: Using synthesis knowledge to build broadly applicable process-based land change models. Environmental Modeling & Software 72: 10-20.
  • Malanson, G. P., and S. J. Walsh (2015). Agent-based models: Individuals interacting in space. Applied Geography 56: 95-98.
  • Rand, W., J. Herrmann, B. Schein, and N. Vodopivec (2015). An agent-based model of urgent diffusion in social media. Journal of Artificial Societies and Social Simulation 18(2): 1.
  • Stillman, R. A., S. F. Railsback, J. Giske, U. Berger, and V. Grimm (2015). Making predictions in a changing world: the benefits of individual-based ecology. BioScience 65(2): 140-150.
  • Sylvester, K. M., D. G. Brown, S. H. Leonard, E. Merchant, and M. Hutchins (2015). Exploring agentlevel calculations of risk and returns in relation to observed land-use changes in the US Great Plains, 1870-1940. Regional Environmental Change 15(2): 301-315.
  • Zellner, M., and S. D. Campbell (2015). Planning for deep-rooted problems: What can we learn from aligning complex systems and wicked problems? Planning Theory & Practice 16(4): 457-478.


  • An, L., A. Zvoleff, J. Liu, and W. Axinn (2014). Agent based modeling in coupled human and natural systems (CHANS): Lessons from a comparative analysis. Annals of Association of American Geographers 104(4): 723-745.
  • Andrei, A. L., K. Comer, and M. Koehler (2014). An agent-based model of network effects on tax compliance
    and evasion. Journal of Economic Psychology 40: 119-133.
  • Arneth, A., C. Brown, and M. D. A. Rounsevell (2014). Global models of human decision-making for land-based mitigation and adaptation assessment. Nature Climate Change 4(7): 550-557.
  • Chen, X., A. Viña, A. Shortridge, L. An, and J. Liu (2014). Assessing the effectiveness of payments for ecosystem services: An agent-based modeling approach. Ecology & Society 19(1): 7.
  • Cioffi-Revilla, C. (2014). Computation and social science. In Introduction to Computational Social Science (pp. 23-66). Springer London.
  • Epstein, J. M. (2014). Agent_Zero: Toward neurocognitive foundations fro generative social science. Princeton University Press.
  • Grimm, V., J. Augusiak, A. Focks, B. M. Frank, F. Gabsi, A. S. A. Johnston, C. Liu, B. T. Martin, M. Meli, V. Radchuk, P. Thorbek, and S. F. Railsback (2014). Towards better modeling and decision support: Documenting model development, testing, and analysis using TRACE. Ecological Modeling 280: 129-139.
  • Magliocca, N. R., D. G. Brown, V. McConnell, J. I. Nassauer, and S. E. Westbrook (2014). Effects of alternative developer decision-making models on the production of ecological subdivision designs: Experimental results from an agent-based model. Environment and Planning B 41(5): 907-927.
  • Magliocca, N. R., M. Shelley, and M. Smorul (2014). Agent-based virtual laboratories for a novel experimental approach to socio-environmental synthesis. in International Environmental Modeling and Software Society (iEMSs) 7th International Congress on Environmental Modeling and Software, San Diego, CA, USA, Ames, D. P., N. W. T. Quinn, and A. E. Rizzoli (Eds.).
  • Murray-Rust, D., C. Brown, J. van Vliet, S. J. Alam, D. T. Robinson, P. H. Verburg, and M. Rounsevell (2014). Combining agent functional types, capitals and services to model land use dynamics. Environmental Modeling and Software 59: 187-201.
  • Murray-Rust, D., D. T. Robinson, E. Guillem, E. Karali, and M. Rounsevell (2014). An open framework for agent based modeling of agricultural land use change. Environmental Modeling and Software 61: 19-38.
  • Rounsevell, M. D. A., A. Arneth, P. Alexander, D. G. Brown, N. de Noblet-Ducoudre, E. Ellis, J. Finnigan, K. Galvin, N. Grigg, I. Harman, J. Lennox, N. Magliocca, D. Parker, B. C. O’Neill, P. H. Verburg, and O. Young (2014). Towards decision-based global land use models for improved understanding of the Earth system. Earth System Dynamics 5: 117-137.
  • Sun, S., D. C. Parker, Q. Huang, T. Filatova, D. T. Robinson, R. L. Riolo, M. Hutchins, and D. G. Brown (2014). Market impacts on land-use change: An agent-based experiment. Annals of the Association of American Geographers 104(3): 460-484.
  • Weng, L. (2014). Information diffusion on online social networks (Doctoral dissertation, Indiana University).
  • Zvoleff, A., and L. An (2014). The effect of reciprocal connections between demographic decision making and land use on decadal dynamics of population and land use change. Ecology and Society 19(2): 31.


  • Agrawal, A., D. G. Brown, G. Rao, R. L. Riolo, D. T. Robinson, and M. Bommarito (2013). Interaction between organizations and networks in common-pool resource governance. Environmental Science and Policy 25: 138-146.
  • Brown, D. G., P. H. Verburg, R. G. Pontius, and M. D. Lange (2013). Opportunities to improve impact, integration, and evaluation of land change models. Current Opinion on Environmental Sustainability 5(5): 452-457.
  • Cockburn, D., S. Crabtree, Z. Kobti, T. A. Kohler, and R. K. Bocinsky (2013). Simulating social and economic specialization in small-scale agricultural societies. Journal of Artificial Societies and Social Simulation 16(4): 4.
  • Crooks, A. T., and S. Wise (2013). GIS and agent-based models for humanitarian assistance. Computers, Environment and Urban Systems 41: 100-111.
  • Guille, A., H. Hacid, C. Favre, and D. A. Zighed (2013). Information diffusion in online social networks: A survey. ACM SIGMOD Record, 42(2): 17-28.
  • Huang, Q., D. C. Parker, S. Sun, and T. Filatova (2013). Effects of agent heterogeneity in the presence of a land-market: A systematic test in an agent-based laboratory. Computers, Environment and Urban Systems 41: 188-203.
  • Ligmann-Zielinska, A. (2013). Spatially explicit sensitivity analysis of an agent-based model of land use change. International Journal of Geographical Information Science 27(9): 1764-1781.
  • Luus, K. A., D. T. Robinson, and P. J. Deadman (2013). Representing ecological processes in agent-based models of land use and cover change. Journal of Land Use Science 8(2): 175-198.
  • O’Sullivan, D., and G. L. W. Perry. 2013. Spatial Simulation: Exploring Pattern and Process. Wiley-Blackwell.
  • Railsback, S. F., and B. C. Harvey (2013). Trait-mediated trophic interactions: Is foraging theory keeping up? Trends in Ecology & Evolution 28(2): 119-125.
  • Robinson, D. T., S. Sun, M. Hutchins, R. L. Riolo, D. G. Brown, D. C. Parker, T. Filatova, W. S. Currie, and S. Kiger (2013). Effects of land markets and land management on ecosystem function: A framework for modeling exurban land-change. Environmental Modeling and Software 45: 129-140.
  • Wang, J., D. G. Brown, R. L. Riolo, S. E. Page, and A. Agrawal (2013). Exploratory analyses of local institutions for climate change adaptation in the Mongolian grasslands: An agent-based modeling approach. Global Environmental Change 23: 1266-1276.


  • An, L. (2012). Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecological Modelling 229(24): 25-36.
  • Chen, X., F. Lupi, L. An, R. Sheely, A. Viña, and J. Liu (2012). Agent-based modeling of the effects of social norms on enrollment in payments for ecosystem services. Ecological Modeling 229(24): 16-24.
  • Grimm, V., and S. F. Railsback (2012). Pattern-oriented modelling: A ‘multi-scope’ for predictive systems ecology. Philosophical Transactions of the Royal Society B 367: 298-310.
  • Heppenstall, A. J., A. T. Crooks, L. M. See, and M. Batty (2012). Agent-based models of geographical systems. Springer, New York, NY.
  • Hughes, H. P. N., C. W. Clegg, M. A. Robinson, and R. M. Crowder (2012). Agent-based modelling and simulation: The potential contribution to organizational psychology. Journal of Occupational and Organizational Psychology 85: 487-502.
  • Kohler, T. A., R. K. Bocinsky, D. Cockburn, S. A. Crabtree, M. D. Varien, K. E. Kolm, S. Smith, S. G. Ortman, and Z. Kobti (2012). Modeling prehispanic Pueblo societies in their ecosystems. Ecological Modeling 241: 30-41.
  • Lorscheid, L., B.-O. Heine, and M. Meyer (2012). Opening the ‘black box’ of simulations: Increased transparency and effective communication through the systematic design of experiments. Computational and Mathematical Organization Theory 18(1): 22-62.
  • Millington, J .D. A., D. O’Sullivan, and G. L. W. Perry (2012). Model histories: Narrative explanation in generative simulation modelling. Geoforum 43: 1025-1034.
  • O’Sullivan, D., J. Millington, G. Perry, and J. Wainwright (2012). Agent-Based Models – Because They’re Worth It? Agent-Based Models of Geographical Systems. eds. A. J. Heppenstall, A. T. Crooks, L. M. See, and M. Batty, 109–123. Springer Netherlands http://dx.doi.org/10.1007/978-90-481-8927-4_6.
  • Van Berkel, D. B., and P. H. Verburg (2012). Combining exploratory scenarios and participatory backcasting: Using an agent-based model in participatory policy design for a multi-functional landscape. Landscape Ecology 27(5): 641-658.


  • Evans, T. P., K. Phanvilay, J. Fox, and J. Vogler (2011). An agent-based model of agricultural innovation, land-cover change and household inequality: The transition from swidden cultivation to rubber plantations in Laos PDR. Journal of Land Use Science 6(2-3): 151-173.
  • Kelley, H., and T. Evans (2011). The relative influences of land-owner and landscape heterogeneity in an agent-based model of land-use. Ecological Economics 70(6): 1075-1087.
  • Railsback, S. F., and M. D. Johnson (2011). Pattern-oriented modeling of bird foraging and pest control in coffee farm. Ecological Modeling 222(18): 3305-3319.
  • Railsback, S. F., and V. Grimm (2011). Agent-based and individual-based modeling: A practical introduction. Princeton University Press, Princeton, New Jersey.
  • Rand, D. G., S. Arbesman, and N. A. Christakis (2011). Dynamic social networks promote cooperation in experiments with humans. PNAS 108(48): 19193-19198.
  • Schreinemachers, P., and T. Berger (2011). An agent-based simulation model of human–environment interactions in agricultural systems. Environmental Modelling & Software 26(7): 845–859.
  • Smajgl, A., D. G. Brown, D. Valbuena, and M. G. A. Huigen (2011). Empirical characterization of agent behaviors in socio-ecological systems. Environmental Modelling and Software 26(7): 837–844.
  • Tang, W., and D. A. Bennett (2011). Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units. Ecological Modelling 222: 3605-3615.
  • Tang, W., S. Wang, D. A. Bennett, and Y. Liu (2011). Agent-based modeling within a cyberinfrastructure environment: A service-oriented computing approach. International Journal of Geographical Information Science 25(9): 1323-1346.


  • An, L., and J. Liu (2010). Long-term effects of family planning and other determinants of fertility on population and environment: Agent-based modeling evidence from Wolong Nature Reserve, China. Population and Environment 31(6): 427–459.
  • Grimm, V., U. Berger, D. L. DeAngelis, J. G. Polhill, J. Giske, and S. F. Railsback (2010). The ODD protocol: A review and first update. Ecological Modeling 221(23): 2760-2768.
  • Hedstrom, P., and P. Ylikoski (2010). Causal mechanisms in the social sciences. Annual Review of Social Sciences 36: 49-67.
  • Saito, K., M. Kimura, K. Ohara, and H. Motoda (2010). Selecting information diffusion models over social networks for behavioral analysis. Machine Learning and Knowledge Discovery in Databases 180-195.
  • Schmolke, A., P. Thorbek, D. L. DeAngelis, and V. Grimm (2010). Ecological models supporting environmental decision making: A strategy for the future. Trends in Ecology & Evolution 25(8): 479-486.
  • Torrens, P. M. (2010). Agent-based models and the spatial sciences. Geography Compass 4(5): 428-448.


  • Farmer, J. D., and D. Foley (2009). The economy needs agent-based modeling. Nature 460(7256): 685-686.
  • Gotts, N., and J. G. Polhill (2009). When and how to imitate your neighbors: Lessons from and for FEARLUS. Journal of Artificial Societies and Social Simulation 12(3).
  • Organization for economic Cooperation and Development (OECD) Global Science Forum (2009). Report on applications of complexity science for public policy: New tools for finding unanticipated consequences and unrealized opportunities. https://www.oecd.org/science/sci-tech/43891980.pdf
  • 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.
  • Robinson, D. T., and D. G. Brown (2009). Evaluating the effects of land‐use development policies on ex‐urban forest cover: An integrated agent‐based GIS approach. International Journal of Geographical Information Science 23(9): 1211-1232.
  • Tang, W., and S. Wang (2009). HPABM: A hierarchical parallel simulation framework for spatially-explicit agent-based models. Transactions in GIS 13(3): 315-333.


  • Brown, D. G., D. T. Robinson, J. I. Nassauer, L. An, S.E. Page, B. Low, W. Rand, M. Zellner, R. Riolo, and J. J. Taylor (2008). Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system. GeoForum 39(2): 805-818.
  • Evans, T. P., and H. Kelley (2008). Assessing the transition from deforestation to forest regrowth with an agent-based model of land cover change for south-central Indiana (USA). Geoforum 39: 819-832.
  • Gilbert, G. N. (2007). Agent-based models (No. 153). Sage Publications.
  • Messina, J. P., T. P. Evans, S. M. Manson, A. M. Shortridge, P. J. Deadman, and P. H. Verburg (2008). Complex systems models and the management of error and uncertainty. Journal of Land Use Science 3(1): 11-25.
  • O’Sullivan D. (2008). Geographical information science: Agent-based models. Progress in Human Geography 32(4): 541-550.
  • Parker, D. C., B. Entwisle, R. R. Rindfuss, L. K. VanWey, S. M. Manson, E. Moran, L. An, P. Deadman, T. Evans, M. Linderman, and G. Malanson (2008). Case studies, cross-site comparisons, and the challenge of generalization: Comparing agent-based models of land-use change in frontier regions. Journal of Land Use Science 3(1): 41-72.
  • Polhill, J. G., D. C. Parker, D. G. Brown, and V. Grimm (2008). Using the ODD protocol for comparing three agent-based social simulation models of land use change. Journal of Artificial Societies and Social Simulation 11(23).
  • Valbuena, D., P. H. Verburg, and A. K. Bregt (2008). A method to define a typology for agent-based analysis in regional land-use research. Agriculture, Ecosystems & Environment 128(1–2): 27–36.


  • Castella, J.-C., and P. H. Verburg (2007). Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam. Ecological Modeling 202(3-4): 410-420.
  • Liu, J., T. Dietz, S. R. Carpenter, M. Alberti, C. Folke, E. Moran, A. N. Pell, P. Deadman, T. Kratz, J. Lubchenco, E. Ostrom, Z. Ouyang, W. Provencher, C. L. Redman, S. H. Schneider, and W. W. Taylor (2007). Complexity of coupled human and natural systems. Science 317(5844):1513–1516.
  • Manson, S. M., and T. Evans (2007). Agent-based modeling of deforestation in southern Yucatan, Mexico, and reforestation in the Midwest United States. PNAS 104(52): 20679-20683.
  • Miller, J. H., and S. E. Page (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton University Press.
  • Robinson, D. T., D. G. Brown, D. C. Parker, P. Schreinemachers, M. A. Janssen, M. Huigen, H. Wittmer, N. Gotts, P. Promburom, E. Irwin, T. Berger, F. Gatzweiler, and C. Barnaud (2007). Comparison of empirical methods for building agent-based models in land use science. Journal of Land Use Science 2(1): 31–55.
  • Smith, E. R., and F. R. Conrey (2007). Agent-Based Modeling: A New Approach for Theory Building in Social Psychology. Personality and Social Psychology Review 11: 87-104.


  • An, L., G. He, Z. Liang, and J. Liu (2006). Impacts of demographic and socioeconomic factors on spatio-temporal dynamics of panda habitats. Biodiversity and Conservation 15: 2343-2363.
  • Bennett, D. A., and W. Tang (2006). Modelling adaptive, spatially aware, and mobile agents: Elk migration in Yellowstone. International Journal of Geographical Information Science 20(9): 1039-1066.
  • Berger, T., P. Schreinemachers, and J. Woelcke (2006). Multi-agent simulation for the targeting of development policies in less-favored areas. Agricultural Systems 88: 28-43.
  • Brown, D. G., and D. T. Bobinson (2006). Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and Society 11(1): 46.
  • Grimm, V., U. Berger, F. Bastiansen, S. Eliassen, V. Ginot, J. Giske, J. Goss-Custard, T. Grand, S. K. Heinz, G. Huse, A. Huth, J. U. Jepsen, C. Jorgensen, W. M. Mooij, B. Muller, G. Pe’er, C. Piou, S. F. Railsback, A. M. Robbins, M. M. Robbins, E. Rossmanith, N. Ruger, E. Strand, S. Souissi, R. A. Stillman, R. Vabo, U. Visser, and D. L. DeAngelis (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modeling 198(1-2): 115-126.
  • Happe, K., K. Kellermann, and A. Balmann (2006). Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior. Ecology and Society 11(1): 49.
  • Janssen, M. A., and E. Ostrom (2006). Empirically based, agent-based models. Ecology and Society 11(2): 37.
  • Manson, S. (2006). Land use in the southern Yucatán peninsular region of Mexico: Scenarios of population and institutional change. Computers, Environment and Urban Systems 30(3): 230-253.
  • Manson, S. M. (2006). Bounded rationality in agent-based models: Experiments with evolutionary programs. International Journal of Geographical Information Science 20(9): 991–1012.
  • Parker, D., D. Brown, and J. Polhill. (2006). Chapter: Illustrating a new ’conceptual design pattern’ for agent-based models of land use via five case studies—the MR POTATOHEAD framework. Pages 1–39 in A. L. Paredes and C. H. Iglesias, editors. Agent-based modelling in Natural Resource Management. INSISOC, Spain.
  • Schreinemachers, P., and T. Berger (2006). Land use decisions in developing countries and their representation in multi-agent systems 1(1): 26-44.


  • An, L., M. Linderman, J. Qi, A. Shortridge, and J. Liu (2005). Exploring complexity in a human-environment system: An agent-based spatial model for multidisciplinary and multi-scale integration. Annals of Association of American Geographers 95(1): 54-79.
  • Brown, D. G., S. Page, R. Riolo, M. Zellner, and W. Rand (2005). Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science 19(2): 153-174.
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