Dispersed information, social networks, and aggregate behavior

This article argues that, in the presence of dispersed information, individual‐level idiosyncratic noise may propagate at aggregate level when agents are connected through a social network. When information about common fundamental is incomplete and heterogeneous across agents, it beneficial to consider actions other because additional conveyed by these actions. We refer act using agents' individual decision process as learning. shows that learning aimed reducing error with respect increase action depending on network topology. Moreover, if very asymmetric, does not decay predicted law large numbers.