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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Main Authors: Lobel, Inna (Contributor), Ozdaglar, Asuman E (Author), Acemoglu, K. Daron (Author), Dahleh, Munther A (Author)
Other Authors: Massachusetts Institute of Technology. Department of Economics (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Operations Research Center (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor), Ozdaglar, Asuman E. (Contributor), Acemoglu, Daron (Contributor), Dahleh, Munther A. (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers, 2010-11-12T16:10:45Z.
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Rate of Convergence of Learning in Social Networks
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