Open AccessA general analysis of boundedly rational learning in social networks
Author(s) -
MuellerFrank Manuel,
Neri Claudia
Publication year - 2021
Publication title -
theoretical economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.404
H-Index - 32
eISSN - 1555-7561
pISSN - 1933-6837
DOI - 10.3982/te2974
Subject(s) - computer science , axiom , artificial intelligence , social learning , class (philosophy) , convergence (economics) , action (physics) , bayesian network , function (biology) , reinforcement learning , machine learning , mathematical economics , mathematics , economics , knowledge management , physics , geometry , quantum mechanics , evolutionary biology , biology , economic growth
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.