A partial folk theorem for games with private learning

The payoff matrix of a finite stage game is realized randomly, and then the stage game is repeated infinitely. The distribution over states of the world (a state corresponds to a payoff matrix) is commonly known, but players do not observe nature’s choice. Over time, they can learn the state in two ways. After each round, each player observes his own realized payoff (which may be stochastic, conditional on the state), and he observes a noisy public signal of the state (whose informativeness may vary with the actions chosen). Actions are perfectly observable. The result is that for any function that maps each state to a payoff vector that is feasible and individually rational in that state, there is a sequential equilibrium in which patient players learn the realized state with arbitrary precision and achieve a payoff close to the one specified for that state. That result extends to the case where there is no public signal, but instead players receive very closely correlated private signals of the vector of realized payoffs.Repeated games, learning, folk theorem

Belief-free Equilibria in Games with Incomplete Information: Characterization and Existence

We characterize belief-free equilibria in infinitely repeated games with incomplete information with N \ge 2 players and arbitrary information structures. This characterization involves a new type of individual rational constraint linking the lowest equilibrium payoffs across players. The characterization is tight: we define a set of payoffs that contains all the belief-free equilibrium payoffs; conversely, any point in the interior of this set is a belief-free equilibrium payoff vector when players are sufficiently patient. Further, we provide necessary conditions and sufficient conditions on the information structure for this set to be non-empty, both for the case of known-own payoffs, and for arbitrary payoffs.Repeated games with incomplete information, Harsanyi doctrine, Belief-free equilibria

Learning the State of Nature in Repeated Games with Incomplete Information and Signals

The motivation of this paper comes from repeated games with incomplete information and imperfect monitoring. It concerns the existence, for any payoff function, of a particular equilibrium (called completely revealing) allowing each player to learn the state of nature. We consider thus an interaction in which players, facing some incomplete information about the state of nature, exchange messages while imperfectly monitoring them. We then ask the question: can players learn the true state even under unilateral deviations? This problem is indeed closely related to Byzantine agreement problems from computer science. We define two different notions describing what a player can learn if at most one other player is faulty. We first link these notions with existence of completely revealing equilibria, then we characterize them for monitoring structures given by a graph. As a corollary we obtain existence of equilibria for a class of undiscounted repeated games.ou