THE EQUIVALENCE OF EVOLUTIONARY GAMES AND DISTRIBUTED MONTE CARLO LEARNING

Yuya Sasaki

No 28338, Economics Research Institute, ERI Series from Utah State University, Economics Department

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: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 39
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/28338/files/er040002.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:usuese:28338

DOI: 10.22004/ag.econ.28338

Access Statistics for this paper

More papers in Economics Research Institute, ERI Series from Utah State University, Economics Department Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().