Artikel
Information diffusion in networks through social learning
We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new metric of information diffusion in social learning, which is weaker than the traditional aggregation metric. We show that if a minimal connectivity condition holds and neighborhoods are independent, information always diffuses. Diffusion can fail in a well-connected network if neighborhoods are correlated. We show that information diffuses if neighborhood realizations convey little information about the network, as measured by network distortion, or if information asymmetries are captured through beliefs over the state of a finite Markov chain.
- Language
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Englisch
- Bibliographic citation
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Journal: Theoretical Economics ; ISSN: 1555-7561 ; Volume: 10 ; Year: 2015 ; Issue: 3 ; Pages: 807-851 ; New Haven, CT: The Econometric Society
- Classification
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Wirtschaft
Noncooperative Games
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- Subject
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Social networks
Bayesian learning
information aggregation
herding
- Event
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Geistige Schöpfung
- (who)
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Lobel, Ilan
Sadler, Evan
- Event
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Veröffentlichung
- (who)
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The Econometric Society
- (where)
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New Haven, CT
- (when)
-
2015
- DOI
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doi:10.3982/TE1549
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Lobel, Ilan
- Sadler, Evan
- The Econometric Society
Time of origin
- 2015
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