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A Common Protocol for Agent-Based Social Simulation

  • Roberto Leombruni
  • Matteo Richiardi
  • Nicole J. Saam
  • Michele Sonnessa

Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic simulations.

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Paper provided by LABORatorio R. Revelli, Centre for Employment Studies in its series LABORatorio R. Revelli Working Papers Series with number 47.

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Length: 26 pages
Date of creation: 2005
Date of revision:
Handle: RePEc:cca:wplabo:47
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  8. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-41, June.
  9. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-37, February.
  10. Werker, C. & Brenner, T., 2004. "Empirical calibration of simulation models," Working Papers 04.13, Eindhoven Center for Innovation Studies.
  11. Nelson Minar & Rogert Burkhart & Chris Langton & Manor Askenazi, 1996. "The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations," Working Papers 96-06-042, Santa Fe Institute.
  12. Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August.
  13. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
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