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Two Competing Models of How People Learn in Games

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  • Ed Hopkins

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 optimization. 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 behavior. In particular, local stability of mixed equilibria under stochastic fictitious play implies local stability under perturbed reinforcement learning. The main identifiable difference between the two models is speed: stochastic fictitious play gives rise to faster learning. Copyright The Econometric Society 2002.

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Paper provided by www.najecon.org in its series NajEcon Working Paper Reviews with number 625018000000000226.

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Date of creation: 21 Sep 2001
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Handle: RePEc:cla:najeco:625018000000000226

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  16. Binmore, Ken & Samuelson, Larry, 1999. "Evolutionary Drift and Equilibrium Selection," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 66(2), pages 363-93, April.
  17. John Duffy & Ed Hopkins, 2004. "Learning, Information and Sorting in Market Entry Games: Theory and Evidence," ESE Discussion Papers, Edinburgh School of Economics, University of Edinburgh 78, Edinburgh School of Economics, University of Edinburgh.
  18. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, Elsevier, vol. 10(1), pages 6-38, July.
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