Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-15T07:52:51.777Z Has data issue: false hasContentIssue false
  • English
  • Français

Individual evolutionary learning with many agents

Published online by Cambridge University Press:  26 April 2012

Jasmina Arifovic  and
John Ledyard
Jasmina Arifovic*
Affiliation:
Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada; e-mail: arifovic@sfu.ca
John Ledyard*
Affiliation:
Division of Humanities and Social Sciences, California Institute of Technology, 1200 East California Boulevard, MC 228-77, Pasadena, CA 91125, USA; e-mail: jledyard@hss.caltech.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Individual Evolutionary Learning (IEL) is a learning model based on the evolution of a population of strategies of an individual agent. In prior work, IEL has been shown to be consistent with the behavior of human subjects in games with a small number of agents. In this paper, we examine the performance of IEL in games with many agents. We find IEL to be robust to this type of scaling. With the appropriate linear adjustment of the mechanism parameter, the convergence behavior of IEL in games induced by Groves–Ledyard mechanisms in quadratic environments is independent of the number of participants.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Special Issue 2: Agent-Based Computational Economics , 26 April 2012 , pp. 239 - 254
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arifovic, J. 1994. Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics and Control 18, 3–28.CrossRefGoogle Scholar
Arifovic, J. 1996. The behavior of the exchange rate in the genetic algorithm and experimental economies. Journal of Political Economy 104, 510–541.CrossRefGoogle Scholar
Arifovic, J., Ledyard, J. 2004. Scaling up learning models in public good games. Journal of Public Economic Theory 6, 205–238.CrossRefGoogle Scholar
Arifovic, J., Ledyard, J. 2009. Individual Evolutioanry Learning and the Voluntary Contributions Mechanisms. manuscript.Google Scholar
Arifovic, J., Ledyard, J. 2011. A behavioral model for mechanism design: Individual Evolutionary Learning. Journal of Economic Behavior and Organization 78, 374–395.CrossRefGoogle Scholar
Camerer, C., Ho, T. 1999. Experience weighted attraction learning in normal form games. Econometrica 67, 827–873.CrossRefGoogle Scholar
Camerer, C. F. 2003. Behavioral Game Theory: Experiments in Strategic Interaction. Princeton University Press.Google Scholar
Camerer, C., Chong, J. 2004. A cognitive hierarchy model of games. The Quarterly Journal of Economics 119, 861–898.CrossRefGoogle Scholar
Chen, Y., Plott, C. 1996. The Groves–Ledyard mechanism: an experimental study of institutional design. Journal of Public Economics 59, 335–364.CrossRefGoogle Scholar
Chen, Y., Tang, F. 1998. Learning and incentive compatible mechanisms for public goods provision: an experimental study. Journal of Political Economy 106, 633–662.CrossRefGoogle Scholar
Erev, I., Roth, A. 1998. Predicting how people play games: reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review 80, 848–881.Google Scholar
Gabay, D., Moulin, H. 1980. On the uniquencess and stability of Nash equilibria in noncooperative games. In Appliced Stochastic Control in Econometrics and Management Science, Bensoussan, A., Kleindorfer, P.S., & Tapiero, C.S. (eds), North-Holland, Amsterdam, pp. 271–293.Google Scholar
Groves, T., Ledyard, J. 1977. Optimal allocation of public goods: a solution to the ‘Free Rider’ problem. Econometrica 45, 783–809.CrossRefGoogle Scholar
Healy, P. 2006. Learning dynamics for mechanism design: an experimental comparison of public goods mechanisms. Journal of Economics Theory 129, 114–149.CrossRefGoogle Scholar
Holland, J.H. 1970. Robust algorithms for adaptation set in a general formal frameowork. Proceedings of the IEEE Symposium on Adaptive Processes, Decision and Control 17, 51–55.Google Scholar
Holland, J. H. 1974. Adaptation in Natural and Artificial Systems. University of Michigan Press.Google Scholar
Hommes, C., Lux, T. 2008. Individual Learning, Heterogeneity and Aggregate Behavior in Cobweb Experiments. Manuscript.Google Scholar
Lux, T., Schornstein, S. 2005. Genetic learning as an explanation of stylized facts of foreign exchange markets. International Journal of Mathematical Economics 41, 169–196.CrossRefGoogle Scholar
Marimon, R., McGrattan, E., Sargent, T.J. 1990. Money as a medium of exchange in an economy with artificially intelligent agents. Journal of Economic Dynamics and Control 14, 329–373.CrossRefGoogle Scholar
Marks, R. E. 1998. Evolved perception and behavior in oligopolies. Journal of Economic Dynamics and Control 22, 1209–1233.CrossRefGoogle Scholar
Milgrom, P., Roberts, J. 1990. Rationalizability, learning, and equilibrium in games with strategic complementarities. Econometrica 58, 1255–1277.CrossRefGoogle Scholar
Miller, J. 1996. The coevolution of automata in the repeated prisonner's dilemma. Journal of Economic Behavior and Organization 29, 87–112.CrossRefGoogle Scholar
Muench, T., Walker, M. 1983. Are Groves–Ledyard equilibria attainable? The Review of Economic Studies 50, 393–396.CrossRefGoogle Scholar
Page, S., Tassier, T. 2004. Equilibrium selection and stability for the Groves–Ledyard mechanism. Journal of Public Economic Theory 6, 311–335.CrossRefGoogle Scholar
Vriend, N. 2000. An illustration of the essential difference between individual and social learning and its consequences for computational analyses. Journal of Economic Dynamics and Control 24, 1–19.CrossRefGoogle Scholar
Watkins, C. 1989. Learning from Delayed Rewards. University of Cambridge.Google Scholar