An adjusted payoff-based procedure for normal form games

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Abstract

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 action space and their own payoff scored at each stage, not those of the other agents. Each player, in order to update her mixed action, computes the average vector payoff she has obtained by using the number of times she has played each pure action. The resulting stochastic process is analyzed via the ODE method from stochastic approximation theory. We are interested in the convergence of the process to rest points of the related continuous dynamics. Results concerning almost sure convergence and convergence with positive probability are obtained and applied to a traffic game. We also provide some examples where convergence occurs with probability zero.

Original languageEnglish
Pages (from-to)1469-1483
Number of pages15
JournalMathematics of Operations Research
Volume41
Issue number4
DOIs
StatePublished - Nov 2016
Externally publishedYes

Keywords

  • Adaptive Dynamics
  • Learning
  • Normal Form Games
  • Stochastic Approximation

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