Before revision: We consider  a group of Bayesian agents who each possess an independent private signal about an unknown state of the world . We study the question of efficient learning: in which games is private information efficiently disseminated among the agents? In particular, we explore the notion of asymptotic learning, which is said to occur when agents learn the state of the world with probability that approaches one as the number of agents tends to infinity. We show that under general conditions asymptotic learning follows from agreement on posterior actions or posterior beliefs, regardless of the information exchange dynamics .
After revision: We consider a group of Bayesian agents who each possess an independent private signal about an unknown state of the world , observe private signals, share the same utility function, and act in a general dynamic setting. We introduce Social Learning Equilibria, a static equilibrium concept that abstracts away from the details of the given dynamics, but nevertheless captures the corresponding asymptotic equilibrium behavior. We establish strong equilibrium properties on agreement, herding, and information aggregation .