Agents in a network want to learn the true state of the world from their own signals and their neighbors' reports. Agents know only their local networks, consisting of their neighbors and the links among them. Every agent is Bayesian with the (possibly misspecified) prior belief that her local...

We study behavior in repeated interactions when agents have no information about the structure of the underlying game and they cannot observe other agents' actions or payoffs. Theory shows that even when players have no such information, simple payoff-based learning rules eventually lead to...

The analysis in this paper searches for individual and group determinants of learning behavior in Monty Hall's Three Door problem examined in Friedman (1998). The results show that the size of monetary incentives, individuals' initial abilities, and social interactions with others are all...

This paper explores the extent to which people learn in repeated games without feedback, and the extent to which this learning transfers to new games. Current theories of learning model learning as adjustment in behavior in response to feedback about outcomes and payoffs and largely ignore the...