Disappearing Private Reputations in Long-Run Relationships

For games of public reputation with uncertainty over types and imperfect public monitoring, Cripps, Mailath, and Samuelson (2004) showed that an informed player facing short-lived uninformed opponents cannot maintain a permanent reputation for playing a strategy that is not part of an equilibrium of the game without uncertainty over types. This paper extends that result to games in which the uninformed player is long-lived and has private beliefs, so that the informed player’s reputation is private. We also show that the rate at which reputations disappear is uniform across equilibria and that reputations disappear in sufficiently long discounted finitely-repeated games.Reputation, Imperfect Monitoring, Repeated Games, Commitment, Private Beliefs

Imperfect Monitoring and Impermanent Reputations

We study the long-run sustainability of reputations in games with imperfect public monitoring. It is impossible to maintain a permanent reputation for playing a strategy that does not play an equilibrium of the game without uncertainty about types. Thus, a player cannot indefinitely sustain a reputation for non-credible behavior in the presence of imperfect monitoring.Reputation, Imperfect Monitoring, Repeated Games

Disappearing Private Reputations in Long-Run Relationships

For games of public reputation with uncertainty over types and imperfect public monitoring, Cripps, Mailath, and Samuelson (2004) showed that an informed player facing short-lived uninformed opponents cannot maintain a permanent reputation for playing a strategy that is not part of an equilibrium of the game without uncertainty over types. This paper extends that result to games in which the uninformed player is long-lived and has private beliefs, so that the informed player’s reputation is private.Reputation, Imperfect Monitoring, Repeated Games, Commitment, Private Beliefs

Common Learning

Consider two agents who learn the value of an unknown parameter by observing a sequence of private signals. The signals are independent and identically distributed across time but not necessarily across agents. We show that that when each agent's signal space is finite, the agents will commonly learn its value, i.e., that the true value of the parameter will become approximate common-knowledge. In contrast, if the agents' observations come from a countably infinite signal space, then this contraction mapping property fails. We show by example that common learning can fail in this case.Common learning, Common belief, Private signals, Private beliefs

Common Learning with Intertemporal Dependence

Consider two agents who learn the value of an unknown parameter by observing a sequence of private signals. Will the agents commonly learn the value of the parameter, i.e., will the true value of the parameter become approximate common-knowledge? If the signals are independent and identically distributed across time (but not necessarily across agents), the answer is yes (Cripps, Ely, Mailath, and Samuelson, 2008). This paper explores the implications of allowing the signals to be dependent over time. We present a counterexample showing that even extremely simple time dependence can preclude common learning, and present sufficient conditions for common learning.Common learning, common belief, private signals, private beliefs