The Equivalence Of Evolutionary Games And Distributed Monte Carlo Learning
AbstractThis 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.
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Bibliographic InfoPaper provided by Utah State University, Economics Department in its series Economics Research Institute, ERI Series with number 28338.
Date of creation: 2004
Date of revision:
Research Methods/ Statistical Methods;
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