A general analysis of boundedly rational learning in social networks
Keywords: 
Social networks
Naïve inference
Naïve learning
Bounded rationality
Consensus
Information aggregation
Issue Date: 
19-Jan-2021
Publisher: 
Wiley
Project: 
info:eu-repo/grantAgreement/MINECO/Proyectos de I+D dentro del Subprograma Estatal de Generación del Conocimiento (2015)/ECO2015-63711-P/ES/AVANCES EN ORGANIZACION INDUSTRIAL, FINANZAS Y ECONOMIA EXPERIMENTAL
ISSN: 
1555-7561
Note: 
CC BY-NC 4.0 DEED Attribution-NonCommercial 4.0 International
Citation: 
Mueller-Frank, M. (Manuel); Neri, C. (Claudia). "A general analysis of boundedly rational learning in social networks". Theoretical Economics. 16 (1), 2021-01-19, 317 - 357
Abstract
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.
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