A general analysis of boundedly rational learning in social networks
研究了二元行动环境中社交网络上的有限理性学习，分析了环境、更新行为公理和网络结构如何影响学习结果，发现准贝叶斯更新仅在高度不对称环境中有效，而更丰富的更新行为可在多数网络中实现学习。
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.