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.

中文翻译：

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分散的信息、社交网络和聚合行为

本文认为，在存在分散信息的情况下，当代理通过社交网络连接时，个体层面的特质噪声可能会在聚合层面传播。当关于一个共同基础的信息不完整并且跨代理时，考虑其他代理的行为是有益的，因为这些行为传达了额外的信息。我们将在个体决策过程中使用其他代理人的行为称为社会学习。本文表明，旨在减少个人行为相对于基本行为的错误的社会学习可能会根据网络拓扑增加聚合行为的错误。此外，如果网络非常不对称，则聚合动作的误差不会像大数定律所预测的那样衰减。