Social Learning in One-Arm Bandit Problems
Abstract
We study a two-player one-arm bandit problem in discrete time, in which the risky arm can have two possible types, high and low, the decision to stop experimenting is irreversible, and players observe each other's actions but not each other's payoffs. We prove that all equilibria are in cutoff strategies and provide several qualitative results on the sequence of cutoffs. Copyright The Econometric Society 2007.Download Info
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Bibliographic Info
Article provided by Econometric Society in its journal Econometrica.
Volume (Year): 75 (2007)
Issue (Month): 6 (November)
Pages: 1591-1611
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Related research
Keywords:Other versions of this item:
- Dinah Rosenberg & Eilon Solan & Nicolas Vieille, 2004. "Social Learning in One-Arm Bandit Problems," Discussion Papers 1396, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Francis Bloch & Simona Fabrizi & Steffen Lippert, 2011.
"Learning and Collusion in New Markets with Uncertain Entry Costs,"
Working Papers
1112, University of Otago, Department of Economics, revised Dec 2011.
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