Background Information
Agent based modeling techniques have become increasingly important tools for understanding how micro level processes interact and aggregate up to produce macro level ecological and economic patterns. The initial incarnations of these models were designed and simulated in abstract or purely hypothetical environments, not necessarily tied to a particular ecological landscape or economic market. Although this abstract research provided important insights into the behavior of these models, this research had a limited ability to be validated with respect to field phenomena since there was little or no field data with which to compare model outcomes. More recently a variety of agent based models (ABMs) have been applied to settings with the more specific goal of explaining observed phenomena. A benefit of this more applied research has been the ability to validate the internal cognitive, institutional model features, as well as the external model predictions regarding ecological or market outcomes.
To date the empirical techniques that have been used to measure the goodness of fit of ABM models has been quite varied. Therefore it would be of interest to compare some of the techniques that have been applied in various application areas of ABM. This workshop will bring together a group of representative scholars who use ABM techniques to empirically study various social-ecological and economic systems. A goal is to derive a state of the art synthesis of the various approaches used to test and compare ABMs. Another particular empirical interest of the workshop is in the use of experimental and survey techniques for validating the assumptions made about the internal learning and decision-making behavior of individual agents. A workshop bringing together a diverse group of agent based researchers can allow a synergistic collaboration in this empirical ABM research area, potentially providing new insights beyond those that might result from a narrower research focus.
A potential list of topics could include comparisons of:
A potential list of topics could include comparisons of:
- Participants' application areas for ABMs;
- Empirical techniques the have been used to validate model internal structure and external predictions. (External: land-use, prices; Internal: experiments, surveys); summary of results to date;
- Model selection techniques.
- Synthesis of modeling and validation approaches to allow across area comparisons.
Some relevant references on empirical applications of ABM. If you have additional references, send them to maajanss@indiana.edu
Axtell, RL. (1999) The Emergence of Firms in a Population of Agents: Local Increasing Returns, Unstable Nash Equilibria, and Power Law Size Distributions, CSED working paper 3, Blookings Institute http://www.brookings.org/es/dynamics/papers/firms/firms.htm
Balmann,A. K. Happe, K. Kellermann and A. Kleingarn (2002) Adjustment costs of agri-environmental policy switchings: an agent-based analysis of the German region Hohenlohe, in M.A. Janssen, editor. Complexity and ecosystem management: The theory and practice of multi-agent systems. Edward Elgar Publishers. Pp. 127–157
Barreteau, O., Le Page, C. and D'Aquino, P. 2003. Role-Playing Games, Models and Negotiation Processes. Journal of Artificial Societies and Social Simulation 6(2).
Berger, T. (2001) Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics 25 (2/3), 245–260.
Bouchaud, J.P., (2001), "Power-Laws in Economy and Finance: Some Ideas from Physics," Quantitative Finance, 1, 105–112.
Bousquet, F., Barreteau, O., Aquino (d'), P., Etienne, M., Boissau, S., Aubert, S., Le Page, C., Babin, D. and Castella, J.-C. 2002. Multi-agent systems and role games : Collective learning processes for ecosystem management. in M.A. Janssen, editor. Complexity and ecosystem management: The theory and practice of multi-agent systems. Edward Elgar Publishers. 248–285
Brock, W. (1999), "Scaling in Economics: A Reader's Guide," Industrial and Corporate Change, 8, 3, 409–446.
Brock, W. and Durlauf, S., (2001) "Discrete Choice with Social Interactions," Review of Economic Studies, 68, 235–260.
Brown, D.G., North, M., Robinson, D., Riolo, R., Rand, W. (In press) "Spatial Process and Data Models: Toward Integration of Agent-Based Models and GIS", Journal of Geographical Systems, http://www.pscs.umich.edu/research/projects/sluce/publications/jgs_abmgis.pdf
Brown, D.G., Page, S.E., Riolo, R., Zellner, M., and Rand. W. (in press) "Path dependence and the validation of agent-based spatial models of land use.", International Journal of Geographical Information Science, http://www.pscs.umich.edu/research/projects/sluce/publications/ijgis-sluce-final.pdf
Busemeyer, J. R., & Wang, Y.-M. (2000). Model comparisons and model selections based on generalization criterion methodology. Journal of Mathematical Psychology, 44, 171–189.
Deffuant G., S. Huet, J.P. Bousset, J. Henriot, G. Amon, G. Weisbuch (2002) Agent-based simulation of organic farming conversion in Allier département in M.A. Janssen, editor. Complexity and ecosystem management: The theory and practice of multi-agent systems. Edward Elgar Publishers. Pp. 158–187
Duffy, J. (draft) Agent-based modeling and Human Subject experiments, http://www.pitt.edu/~jduffy/docs/duffy_chapter_0504.pdf
Durlauf S.N. (2003) Complexity and Empirical Economics, Santa Fe Working paper 03-02-014 http://www.santafe.edu/sfi/publications/wpabstract/200302014
Evans, T. P., and Kelley, H. 2004. Multi-scale analysis of a household level agent-based model of landcover change. Journal of Environmental Management. 72 (1-2), 57–72.
Etienne, M. 2003. SYLVOPAST: a multiple target role-playing game to assess negotiation processes in sylvopastoral management planning. Journal of Artificial Societies and Social Simulation 6(2).
Fernandez, L.E., Brown, D.G., Marans, R.W., and Nassauer, J.I. (in press) "Characterizing location preferences in an exurban population: Implications for agent based modeling.", Environment and Planning B, http://www.pscs.umich.edu/research/projects/sluce/publications/Fernandez_et_al.pdf
Gabaix, X., (1999), "Zipf's Law for Cities: An Explanation," Quarterly Journal of Economics, 739–767.
Grünwald, P. (2000). Model selection based on minimum description length. Journal of Mathematical Psychology, 44, 133–152.
Hoffmann M., H. Kelley and T. Evans (2002) Simulating land-cover change in South-Central Indiana: an agent-based model of deforestation and afforestation, in M.A. Janssen, editor. Complexity and ecosystem management: The theory and practice of multi-agent systems. Edward Elgar Publishers.218–247
Jager, W. and M.A. Janssen (2002), Using artificial agents to understand laboratory experiments of common-pool resources with real agents, in M.A. Janssen, editor. Complexity and ecosystem management: The theory and practice of multi-agent systems. Edward Elgar Publishers: pp. 75–102
Janssen, M.A. and T.K. Ahn (draft) Adaptation and Anticipation in Public Good experiments,
http://mailer.fsu.edu/~tahn/JanssenAhn.pdf
Koesrindartoto, D. and L. Tesfatsion (2004) Testing the Reliability of FERC's Wholesale Power Market Platform: An Agent-Based Computational Economics Approach", Energy, Environment, and Economics in a New Era, Proceedings of the 24th USAEE/IAEE North American Conference, Washington, D.C., July 8-10, 2004, http://www.econ.iastate.edu/tesfatsi/usaeetalk.pdf
LeBaron, B., (2001), "Stochastic Volatility as a Simple Generator of Power Laws and Long Memory," Quantitative Finance,1, 621–631.
Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109 (3), 472–491.
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