This paper analyzes repeated games in which it is possible for players to observe the other players' past actions without noise but it is costly. One's observation decision itself is not observable to the other players, and this private nature of monitoring activity makes it difficult to give...

The set of payoffs associated to pure uniform equilibria of a repeated game with public observation is characterized in terms of the one shot-game. The key of the result is first, a study of the effect of undetectable deviations and second, the definition of new types of punishments using...

Correlation of players' actions may evolve in the common course of the play of a repeated game with perfect monitoring (“online correlation”). In this paper we study the concealment of such correlation from a boundedly rational player. We show that “strong” players, i.e., players whose...

This paper characterizes an equilibrium payoff subset for dynamic Bayesian games as discounting vanishes. Monitoring is imperfect, transitions may depend on actions, types may be correlated and values may be interdependent. The focus is on equilibria in which players report truthfully. The...

We prove a folk theorem for multiplayer games in continuous time when players observe a public signal distorted by Brownian noise. The proof is based on a rigorous foundation for such continuous-time multiplayer games. We study in detail the relation between behaviour and mixed strategies, and...

Much of the repeated game literature is concerned with proving Folk Theorems. The logic of the exercise is to specify a particular game, and to explore for that game specification whether any given feasible (and individually rational) value vector can be an equilibrium outcome for some...

This paper characterizes an equilibrium payoff subset for Markovian games with private information as discounting vanishes. Monitoring is imperfect, transitions may depend on actions, types be correlated and values interdependent. The focus is on equilibria in which players report truthfully....