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 human 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, exponential fictitious play and suitably perturbed reinforcement model have the same expected motion and therefore will have the same asymptotic behaviour. It is also shown that more general models of stochastic fictitious play and perturbed reinforcement between the 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: 1999-10, Revised 2000-12
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Citations: View citations in EconPapers (1)

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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)
Working Paper: Two Competing Models of How People Learn in Games (2001)
Working Paper: Two Competing Models of How People Learn in Games (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:edn:esedps:42

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