Number 1926 Learning To Play Bayesian Games
نویسنده
چکیده
This paper discusses the implications of learning theory for the analysis of Bayesian games. One goal is to illuminate the issues that arise when modeling situations where players are learning about the distribution of Natures move as well as learning about the opponents strategies. A second goal is to argue that quite restrictive assumptions are necessary to justify the concept of Nash equilibrium without a common prior as a steady state of a learning process. 1 This work is supported by the National Science Foundation under Grants 99-86170, 97-30181, and 9730493. 2 Departments of Economics: Northwestern and Tel Aviv Universities, Harvard University and UCLA.
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