LEARNING FROM NEIGHBORS

When payoffs from different actions are unknown, agents use their own past experience as well as the experience of their neighbours to guide their decision making. In this paper, we develop a general framework to study the relationship between the structure of these neighbourhood s and the process of social learning. We show that, in a connected soc iety, local learning ensures that all agents obtain the same payoffs i n the long run. Thus, if actions have different payoffs, then all agen ts choose the same action, and social conformism obtains. We develop c onditions on the distribution of prior beliefs, the structure of neigh bourhoods and the informativeness of actions under which this action i s optimal. In particular, we identify a property of neighbourhood stru ctures-local independence-which greatly facilitates social learning. S imulations of the model generate spatial and temporal patterns of adop tion that are consistent with empirical work.