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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
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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.

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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

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