Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253450 
Year of Publication: 
2021
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 16 [Issue:] 1 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 317-357
Publisher: 
The Econometric Society, New Haven, CT
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 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.
Subjects: 
Social networks
na&#x0308
ive inference
na&#x0308
ive learning
bounded rationality
consensus
information aggregation
JEL: 
D83
D85
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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