A general analysis of boundedly rational learning in social networks

• 成果类型: Article
• 署名作者: Mueller-Frank, Manuel; Neri, Claudia
• 署名单位: University of Navarra; IESE Business School
• 刊物名称: THEORETICAL ECONOMICS
• ISSN/ISSBN: 1933-6837
• DOI: 10.3982/TE2974
• 发表日期: 2021-01-01
• 页码: 317-357
• 关键词: Social networks; naive inference; naive learning; bounded rationality; consensus; information aggregation

摘要

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.