We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we provide a normative foundation for quasi‐Bayesian updating, where a quasi‐Bayesian agent treats others' actions as if they were based only on their private signal. Quasi‐Bayesian updating induces learning (i.e., convergence to the optimal action for every agent in every connected network) only in highly asymmetric environments. In all other environments, learning fails in networks with a diameter larger than 4. Finally, we consider a richer class of updating behavior that allows for nonstationarity and differential treatment of neighbors' actions depending on their position in the network. We show that within this class there exist updating systems that induce learning for most networks.

中文翻译：

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社交网络中有限理性学习的一般分析

我们分析了二元行动环境中社交网络中的有限理性学习。我们确定学习结果如何依赖于环境（即信息结构、效用函数）、强加于更新行为的公理和网络结构。特别是，我们为准贝叶斯更新提供了规范基础，其中准贝叶斯代理将其他人的行为视为仅基于他们的私人信号。准贝叶斯更新仅在高度不对称的环境中引发学习（即，收敛到每个连接网络中每个代理的最佳动作）。在所有其他环境中，在直径大于 4 的网络中学习会失败。最后，我们考虑一种更丰富的更新行为，它允许对邻居的非平稳性和差异化处理 行动取决于他们在网络中的位置。我们表明，在这个类中存在诱导大多数网络学习的更新系统。