The Equivalence of Evolutionary Games and Distributed Monte Carlo Learning

Yuya Sasaki

No 2004-02, Working Papers from Utah State University, Department of Economics

Abstract: This paper presents a tight relationship between evolutionary game theory and distributed intelligence models. After reviewing some existing theories of replicator dynamics and distributed Monte Carlo learning, we make formulations and proofs of the equivalence between these two models. The relationship will be revealed not only from a theoretical viewpoint, but also by experimental simulations of the models by taking a simple symmetric zero-sum game as an example. As a consequence, it will be verified that seemingly chaotic macro dynamics generated by distributed micro-decisions can be explained with theoretical models.

Keywords: evolutionary game; replicator dynamics; agent based models; Monte Carlo learning; recency-weighted learning (search for similar items in EconPapers)
JEL-codes: C63 C73 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2004-01
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https://repec.bus.usu.edu/RePEc/usu/pdf/ERI2004-02.pdf First version, 2004 (application/pdf)

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