Lower Bounds on the Rate of Learning in Social Networks

Lower Bounds on the Rate of Learning in Social Networks

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e study the rate of convergence of Bayesian learning in social networks. Each individual receives a signal about the underlying state of the world, observes a subset of past actions and chooses one of two possible actions. Our previous work established that when signals generate unbounded likelihood...

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Bibliographic Details
Main Authors: Lobel, Inna, Ozdaglar, Asuman E, Acemoglu, K. Daron, Dahleh, Munther A
Other Authors: Massachusetts Institute of Technology. Department of Economics
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers 2010
Online Access:http://hdl.handle.net/1721.1/59971
https://orcid.org/0000-0002-1827-1285
https://orcid.org/0000-0002-1470-2148
https://orcid.org/0000-0003-0908-7491

Internet

http://hdl.handle.net/1721.1/59971
https://orcid.org/0000-0002-1827-1285
https://orcid.org/0000-0002-1470-2148
https://orcid.org/0000-0003-0908-7491

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