Information diffusion in networks through social learning
研究网络中的代理人通过观察邻居选择进行社会学习时，信息能否扩散。发现邻居关系独立时信息总能扩散，但相关时可能失败，并引入新指标衡量扩散。
We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new metric of information diffusion in social learning, which is weaker than the traditional aggregation metric. We show that if a minimal connectivity condition holds and neighborhoods are independent, information always diffuses. Diffusion can fail in a well-connected network if neighborhoods are correlated. We show that information diffuses if neighborhood realizations convey little information about the network, as measured by network distortion, or if information asymmetries are captured through beliefs over the state of a finite Markov chain.