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Individual evolutionary learning with many agents

Arifovic, Jasmina and Ledyard, John (2012) Individual evolutionary learning with many agents. Knowledge Engineering Review, 27 (2). pp. 239-254. ISSN 0269-8889. doi:10.1017/S026988891200015X.

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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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Arifovic, Jasmina0000-0002-7092-6541
Additional Information:© 2012 Cambridge University Press. We thank Olena Kostyshyna for her very able research assistance. We also thank an anonymous referee for very helpful comments.
Issue or Number:2
Record Number:CaltechAUTHORS:20120607-094141948
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Official Citation:Jasmina Arifovic and John Ledyard (2012). Individual evolutionary learning with many agents. The Knowledge Engineering Review, 27 , pp 239-254 doi:10.1017/S026988891200015X
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:31835
Deposited On:07 Jun 2012 20:03
Last Modified:09 Nov 2021 20:01

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