Learning from Neighbours
构建了一个通用框架，研究邻居结构如何影响社会学习过程。在连通社会中，局部学习导致所有代理人长期获得相同收益，从而产生社会从众行为。文章分析了先验信念分布、邻居结构和行动信息性对最优行动的影响，并识别出促进社会学习的“局部独立性”属性。模拟结果与实证研究中的时空采纳模式一致。
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 neighbourhoods and the process of social learning. We 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. We develop conditions on the distribution of prior beliefs, the structure of neighbourhoods and the informativeness of actions under which this action is optimal. In particular, we identify a property of neighbourhood 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.