Learning in Repeated Interactions on Networks
研究了理性代理人在社交网络中如何通过观察邻居过去行为来学习，发现任何均衡下的学习速度上限仅取决于私人信号分布，与网络结构、效用函数和耐心程度无关。
We study how long‐lived, rational agents learn in a social network. In every period, after observing the past actions of his neighbors, each agent receives a private signal, and chooses an action whose payoff depends only on the state. Since equilibrium actions depend on higher‐order beliefs, it is difficult to characterize behavior. Nevertheless, we show that regardless of the size and shape of the network, the utility function, and the patience of the agents, the speed of learning in any equilibrium is bounded from above by a constant that only depends on the private signal distribution.