Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/150263
Authors: 
Lobel, Ilan
Sadler, Evan
Year of Publication: 
2015
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 10 [Year:] 2015 [Issue:] 3 [Pages:] 807-851
Abstract: 
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.
Subjects: 
Social networks
Bayesian learning
information aggregation
herding
JEL: 
C72
D83
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Article

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