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Two Competing Models of How People Learn in Games (first version)

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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.

Suggested Citation

  • Ed Hopkins, 1999. "Two Competing Models of How People Learn in Games (first version)," Edinburgh School of Economics Discussion Paper Series 42, Edinburgh School of Economics, University of Edinburgh.
    • Handle: RePEc:edn:esedps:42
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    File URL: http://www.econ.ed.ac.uk/papers/id42_esedps.pdf
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    Cited by:

    1. Dana Heller, 2000. "Parametric Adaptive Learning," Econometric Society World Congress 2000 Contributed Papers 1496, Econometric Society.

    More about this item

    Keywords

    games; reinforcement learning; fictitious play;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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