Document Type
Article
Journal/Book Title/Conference
Economics Research Institute Study Paper
Volume
2
Publisher
Utah State University Department of Economics
Publication Date
2004
Rights
Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact the Institutional Repository Librarian at digitalcommons@usu.edu.
First Page
1
Last Page
45
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 fonnulations 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.
Recommended Citation
Sasaki, Yuya, "The Equivalence of Evolutionary Games and Distributed Monte Carlo Learning" (2004). Economic Research Institute Study Papers. Paper 276.
https://digitalcommons.usu.edu/eri/276