Published February 2014
| Submitted
Journal Article
Open
Asymptotic learning on Bayesian social networks
- Creators
-
Mossel, Elchanan
- Sly, Allan
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Tamuz, Omer
Chicago
An error occurred while generating the citation.
Abstract
We study a standard model of economic agents on the nodes of a social network graph who learn a binary "state of the world" S, from initial signals, by repeatedly observing each other's best guesses. Asymptotic learning is said to occur on a family of graphs G_n=(V_n,E_n) with |V_n|→∞ if with probability tending to 1 as n→∞ all agents in G_n eventually estimate S correctly. We identify sufficient conditions for asymptotic learning and contruct examples where learning does not occur when the conditions do not hold.
Additional Information
© 2013 Springer-Verlag Berlin Heidelberg. Received: 29 May 2012. Revised: 1 January 2013. Published online: 13 February 2013. The authors would like to thank Shachar Kariv for an enthusiastic introduction to his work with Douglas Gale, and for suggesting the significance of asymptotic learning in this model. Elchanan Mossel is supported by NSF award DMS 1106999, by ONR award N000141110140 and by ISF Grant 1300/08. Allan Sly is supported in part by an Alfred Sloan Fellowship in Mathematics. Omer Tamuz is supported by ISF Grant 1300/08, and is a recipient of the Google Europe Fellowship in Social Computing. This research is supported in part by this Google Fellowship.Attached Files
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Additional details
- Eprint ID
- 71971
- DOI
- 10.1007/s00440-013-0479-y
- Resolver ID
- CaltechAUTHORS:20161114-072802716
- NSF
- DMS-1106999
- Office of Naval Research (ONR)
- N000141110140
- Israel Science Foundation
- 1300/08
- Alfred P. Sloan Foundation
- Created
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2016-11-16Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field