Learning from Neighbours
When payoffs from different actions are unknown, agents use their own past experience as well as the experience of their neighbors to guide their decision making. In this paper, the authors develop a general framework to study the relationship between the structure of these neighborhoods and the process of social learning. They show that, in a connected society, local learning ensures that all agents obtain the same payoffs in the long run. Thus, if actions have different payoffs, then all agents choose the same action, and social conformism obtains. The authors develop conditions on the distribution of prior beliefs, the structure of neighborhoods and the informativeness of actions under which this action is optimal. In particular, they identify a property of neighborhood structures--local independence--which greatly facilitates social learning. Simulations of the model generate spatial and temporal patterns of adoption that are consistent with empirical work. Copyright 1998 by The Review of Economic Studies Limited.
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Volume (Year): 65 (1998)
Issue (Month): 3 (July)
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