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Can genetic algorithms explain experimental anomalies? An application to common property resources

It is common to find in experimental data persistent oscillations in the aggregate outcomes and high levels of heterogeneity in individual behavior. Furthermore, it is not unusual to find significant deviations from aggregate Nash equilibrium predictions. In this paper, we employ an evolutionary model with boundedly rational agents to explain these findings. We use data from common property resource experiments (Casari and Plott, 2003). Instead of positing individual-specific utility functions, we model decision makers as selfish and identical. Agent interaction is simulated using an individual learning genetic algorithm, where agents have constraints in their working memory, a limited ability to maximize, and experiment with new strategies. We show that the model replicates most of the patterns that can be found in common property resource experiments.

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Paper provided by Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC) in its series UFAE and IAE Working Papers with number 542.02.

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Length: 27
Date of creation: 10 2002
Date of revision:
Handle: RePEc:aub:autbar:542.02
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  1. Miller, John H. & Andreoni, James, 1991. "Can evolutionary dynamics explain free riding in experiments?," Economics Letters, Elsevier, vol. 36(1), pages 9-15, May.
  2. Elena Rocco & Massimo Warglien, 1996. "Computer Mediated Communication and the Emergence of "Electronic Opportunism"," CEEL Working Papers 9601, Cognitive and Experimental Economics Laboratory, Department of Economics, University of Trento, Italia.
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  8. M.A. Nowak & K. Sigmund, 1998. "Evolution of Indirect Reciprocity by Image Scoring/ The Dynamics of Indirect Reciprocity," Working Papers ir98040, International Institute for Applied Systems Analysis.
  9. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  10. Ho, Teck Hua & Weigelt, Keith & Camerer, Colin, 1996. "Iterated Dominance and Iterated Best-Response in Experimental P-Beauty Contests," Working Papers 974, California Institute of Technology, Division of the Humanities and Social Sciences.
  11. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-71, May.
  12. Brandts, Jordi & Schram, Arthur, 2001. "Cooperation and noise in public goods experiments: applying the contribution function approach," Journal of Public Economics, Elsevier, vol. 79(2), pages 399-427, February.
  13. Andreoni James & Miller John H., 1995. "Auctions with Artificial Adaptive Agents," Games and Economic Behavior, Elsevier, vol. 10(1), pages 39-64, July.
  14. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
  15. Kollman, Ken & Miller, John H & Page, Scott E, 1997. "Political Institutions and Sorting in a Tiebout Model," American Economic Review, American Economic Association, vol. 87(5), pages 977-92, December.
  16. Jasmina Arifovic & John Ledyard, 2002. "Computer Testbeds and Mechanism Design," Computing in Economics and Finance 2002 262, Society for Computational Economics.
  17. Dawid, Herbert, 1999. "On the convergence of genetic learning in a double auction market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1545-1567, September.
  18. Casari, Marco & Plott, Charles R., 2003. "Decentralized management of common property resources: experiments with a centuries-old institution," Journal of Economic Behavior & Organization, Elsevier, vol. 51(2), pages 217-247, June.
  19. Bullard, James & Duffy, John, 1998. "A model of learning and emulation with artificial adaptive agents," Journal of Economic Dynamics and Control, Elsevier, vol. 22(2), pages 179-207, February.
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