Scholarly consensus that social ties resolve social dilemmas is largely predicated on common knowledge of networks. But what happens when people do not know all relevant social ties? Does network uncertainty translate into worse outcomes? I address these concerns by advancing the notion of a Network Estimation Bayesian Equilibrium to examine cooperative behavior under different epistemic conditions. When networks are common knowledge, I find that all possible outcomes of an original cooperation game can be realized in equilibrium, albeit with a higher likelihood of defection for more connected players. Variable knowledge of the network also has a distributional impact. With incomplete network knowledge, it’s possible to observe reversed equilibrium behavior when more connected players actually cooperate more often than less connected ones. In fact, aggregate network uncertainty in some social contexts incentivizes more mutual cooperation than would be the case with common knowledge of all social ties.

中文翻译：

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明显和未知网络的社会困境

社会关系解决社会困境的学术共识很大程度上取决于网络的常识。但是当人们不了解所有相关的社会关系时会发生什么？网络不确定性是否会转化为更糟糕的结果？我通过提出网络估计贝叶斯均衡的概念来检查不同认知条件下的合作行为来解决这些问题。当网络是常识时，我发现原始合作博弈的所有可能结果都可以在均衡中实现，尽管更多连接的玩家叛逃的可能性更高。网络的可变知识也具有分布影响。由于网络知识不完整，当更多连接的参与者实际上比连接更少的参与者更频繁地合作时，有可能观察到反向均衡行为。实际上，