Large Bandit Games
Résumé
We study a multi-player one-arm bandit game: for infinitely many stages, players choose between playing a risky action or dropping out irreversibly to a safe action. Each player observe his own payoffs and other players' actions. We study equilibria of the game when the number of players gets large. We argue that either limit equilibrium exhibits aggregate randomness, or players behaviours leads to a swift determination of the value of the risky action. The conditions under which one or the other case arises are provided.
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