Learning from Neighbors

When payoffs from different actions are unknown, agents use their own past experience as well as the experience of their neighbors to guide their current decision making. This paper develops a general framework to study the relationship between the structure of information flows and the process of social learning. We show that in a connected society, local learning ensures that all agents obtain the same utility, in the long run. We develop conditions under which this utility is the maximal attainable, i.e. optimal actions are adopted. This analysis identifies a structural property of information structures -- local independence -- which greatly facilitates social learning. Our analysis also suggests that there exists a negative relationship between the degree of social integration and the likelihood of diversity. Simulations of the model generate spatial and temporal patterns of adoption that are consistent with empirical work.