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Learning in Repeated Games without Repeating the Game Author info | Abstract | Publisher info | Download info | Related research | Statistics Patrick Leoni
This paper extends the convergence result on Bayesian learning in Kalai and Lehrer (1993a, 1993b) to a class of games where players have a payoff function continuous for the product topology. Provided that 1) every player maximizes her expected payoff against her own beliefs, 2) every player updates her beliefs in a Bayesian manner, and 3) prior beliefs other players’ strategies have a grain of truth, we show that after some finite time the equilibrium outcome of the above game is arbitrarily close to a Nash equilibrium. Those assumptions are shown to be tight.
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Paper provided by Institute for Empirical Research in Economics - IEW in its series IEW - Working Papers with number
iewwp215.
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Keywords: learning ; product topology ; Other versions of this item:
Find related papers by JEL classification: C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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