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Learning in Bayesian Games with Binary Actions

Listed author(s):
  • Beggs Alan

    ()

    (University of Oxford)

This paper considers a simple adaptive learning rule in Bayesian games with binary actions where players employ threshold strategies. Global convergence results are given for supermodular games and potential games. If there is a unique equilibrium, players' strategies converge almost surely to it. Even if there is not, in potential games and in the two-player case in supermodular games, any limit point of the learning process must be an equilibrium. In particular, if equilibria are isolated, the learning process converges to one of them almost surely.

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Article provided by De Gruyter in its journal The B.E. Journal of Theoretical Economics.

Volume (Year): 9 (2009)
Issue (Month): 1 (September)
Pages: 1-30

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Handle: RePEc:bpj:bejtec:v:9:y:2009:i:1:n:33
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