Artikel
A general analysis of boundedly rational learning in social networks
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.
- Sprache
-
Englisch
- Erschienen in
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Journal: Theoretical Economics ; ISSN: 1555-7561 ; Volume: 16 ; Year: 2021 ; Issue: 1 ; Pages: 317-357 ; New Haven, CT: The Econometric Society
- Klassifikation
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Wirtschaft
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Network Formation and Analysis: Theory
- Thema
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Social networks
nä
ive inference
nä
ive learning
bounded rationality
consensus
information aggregation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Mueller-Frank, Manuel
Neri, Claudia
- Ereignis
-
Veröffentlichung
- (wer)
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The Econometric Society
- (wo)
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New Haven, CT
- (wann)
-
2021
- DOI
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doi:10.3982/TE2974
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Mueller-Frank, Manuel
- Neri, Claudia
- The Econometric Society
Entstanden
- 2021
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