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Two Competing Models of How People Learn in Games

Ed Hopkins

Edinburgh School of Economics Discussion Paper Series from Edinburgh School of Economics, University of Edinburgh

Abstract: Reinforcement learning and stochastic fictitious play are apparent rivals as models of humans learning. They embody quite different assumptions about the processing of information and optimisation. This paper compares their properties and finds that they are far more similar than were thought. In particular, the expected motion of stochastic fictitious play and reinforcement learning with experimentation can both be written as a perturbed form of the evolutionary replicator dynamics. Therefore they will in many cases have the same asymptotic behaviour. In particular, they have identical local stability properties at mixed equilibria. The main identifiable difference between two models is speed: stochastic fictitious play gives rise to faster learning.

Keywords: games; reinforcement learning; fictitious play (search for similar items in EconPapers)
JEL-codes: C72 D83 (search for similar items in EconPapers)
Pages: 38
Date: 2000-12, Revised 2000-12
New Economics Papers: this item is included in nep-exp and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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http://www.econ.ed.ac.uk/papers/id51_esedps.pdf Revised version, December 2000 (application/pdf)

Related works:
Journal Article: Two Competing Models of How People Learn in Games (2002)
Working Paper: Two Competing Models of How People Learn in Games (2001) Downloads
Working Paper: Two Competing Models of How People Learn in Games (2001) Downloads
Working Paper: Two Competing Models of How People Learn in Games (2000) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:edn:esedps:51

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