Learning in Repeated Games without Repeating the Game

Patrick Leoni ()

No 215, IEW - Working Papers from Institute for Empirical Research in Economics - University of Zurich

Abstract: 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.

Keywords: learning; product topology (search for similar items in EconPapers)
JEL-codes: C73 D83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-evo and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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