Learning to Play Bayesian Games

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  • 1 January 2000
    • preprint
      • Published in RePEc
Abstract
This paper discusses the implications of learning theory for the analysis of games with a move by Nature. One goal is to illuminate the issues that arise when modeling situations where players are learning about the distribution of Nature's 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. (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.)

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