Learning in Bayesian Games with Binary Actions.

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. E...

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Bibliografske podrobnosti
Glavni avtor: Beggs, A
Format: Journal article
Jezik:English
Izdano: Berkley Electronic Press 2009
Opis
Izvleček: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.