We analyze a game of strategic experimentation with two-armed bandits whose risky arm might yield payoffs after exponentially distributed random times. Free-riding causes an inefficiently low level of experimentation in any equilibrium where the players use stationary Markovian strategies with beliefs as the state variable. We construct the unique symmetric Markovian equilibrium of the game, followed by various asymmetric ones. There is no equilibrium where all players use simple cut-off strategies. Equilibria where players switch finitely often between experimenting and free-riding all yield a similar pattern of information acquisition, greater efficiency being achieved when the players share the burden of experimentation more equitably. When players switch roles infinitely often, they can acquire an approximately efficient amount of information, but still at an inefficient rate. In terms of aggregate payoffs, all these asymmetric equilibria dominate the symmetric one wherever the latter prescribes simultaneous use of both arms. Copyright The Econometric Society 2005.
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Article provided by Econometric Society in its journal Econometrica.
Volume (Year): 73 (2005) Issue (Month): 1 (01) Pages: 39-68 Download reference. The following formats are available: HTML,
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Nicolas Klein & Sven Rady, 2008.
"Negatively Correlated Bandits,"
Discussion Papers
243, SFB/TR 15 Governance and the Efficiency of Economic Systems, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
[Downloadable!]
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.
[Downloadable!]
Other versions:
Daniel Krähmer, 2005.
"Equilibrium Learning in Simple Contests,"
Discussion Papers
73, SFB/TR 15 Governance and the Efficiency of Economic Systems, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
[Downloadable!]