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An adjusted payoff-based procedure for normal form games
Mario Bravo 1
(27/06/2011)

We study a simple adaptive model in the framework of an $N$-player normal form game. The model consists of a repeated game where the players only know their own strategy space and their own payoff scored at each stage. The information about the other agents (actions and payoffs) is unknown. In particular, we consider a variation of the procedure studied by Cominetti et al. (2010) where, in our case, each player is allowed to use the number of times she has played each action to update her strategy. The resultant stochastic process is analyzed via the so-called ODE method from stochastic approximation theory. We are interested in the convergence of the process to rest points of a related continuous dynamics. Results concerning almost sure convergence and convergence with positive probability are obtained and applied to a traffic game. Also, we provide some examples where convergence occurs with probability zero. Finally, we verify that part of the analysis holds when players are facing a random environment.
1 :  Institut de Mathématiques de Jussieu (IMJ)
CNRS : UMR7586 – Université Pierre et Marie Curie [UPMC] - Paris VI – Université Paris VII - Paris Diderot
Informatique/Informatique et théorie des jeux

Mathématiques/Probabilités
Normal form games – Learning – Adaptive dynamics – Stochastic approximation
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