Dispersed information, social networks, and aggregate behavior
研究当信息分散且个体间存在噪声时，社交网络如何导致个体噪声传播到总体层面，并发现旨在减少个体误差的社会学习可能反而增大总体误差，且网络不对称时总体误差不按大数定律衰减。
Abstract This article argues that, in the presence of dispersed information, individual‐level idiosyncratic noise may propagate at the aggregate level when agents are connected through a social network. When information about a common fundamental is incomplete and heterogeneous across agents, it is beneficial to consider the actions of other agents because of the additional information conveyed by these actions. We refer to the act of using other agents' actions in the individual decision process as social learning. This article shows that social learning aimed at reducing the error of individual actions with respect to the fundamental may increase the error of the aggregate action depending on the network topology. Moreover, if the network is very asymmetric, the error of the aggregate action does not decay as predicted by the law of large numbers.