Theoretical Economics, Volume 16, Number 1 (January 2021)

Theoretical Economics 16 (2021), 317–357

A general analysis of boundedly rational learning in social networks

Manuel Mueller-Frank, Claudia Neri


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 four. Finally, we consider a richer class of updating behavior that allows for non-stationarity and differential treatment of neighbors' actions depending on their position in the network. We show that within this class there exist updating systems which induce learning for most networks.

Keywords: Social networks, naive inference, information aggregation, bounded rationality, agreement

JEL classification: D83, D85

Full Text:  PRINT  VIEW