Paper #788

Título:

Global Nash convergence of Foster and Young's regret testing

Autores:

Fabrizio Germano y Gábor Lugosi

Data:

Octubre 2004

Resumen:

We construct an uncoupled randomized strategy of repeated play such that, if every player follows such a strategy, then the joint mixed strategy profiles converge, almost surely, to a Nash equilibrium of the one-shot game. The procedure requires very little in terms of players' information about the game. In fact, players' actions are based only on their own past payoffs and, in a variant of the strategy, players need not even know that their payoffs are determined through other players' actions. The procedure works for general finite games and is based on appropriate modifications of a simple stochastic learning rule introduced by Foster and Young.

Palabras clave:

Regret testing, regret based learning, random search, stochastic dynamics, uncoupled dynamics, global convergence to Nash equilibria

Códigos JEL:

C72, C73, D81, D83

Área de investigación:

Microeconomía

Publicado en:

Games and Economic Behavior (2007) 60: 135-154

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