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Prediction, Optimization, and Learning in Repeated Games

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Author Info
John H. Nachbar
Abstract

This paper shows that, in many infinitely repeated games, if players optimize with respect to beliefs that satisfy a diversity condition termed neutrality, then each player will choose a strategy that his opponent was certain would not be played. This is an obstacle to formulation of a learning theory in which Nash equilibrium behavior is a necessary long-run consequence of optimization by cautious players.

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Publisher Info
Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 65 (1997)
Issue (Month): 2 (March)
Pages: 275-310
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Handle: RePEc:ecm:emetrp:v:65:y:1997:i:2:p:275-310

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  1. David K Levine & Balázs Szentes, 2006. "Can A Turing Player Identify Itself?," Levine's Working Paper Archive 618897000000001015, David K. Levine. [Downloadable!]
  2. Francesco Squintani, 2004. "Backward Induction and Model Deterioration," The B.E. Journal of Theoretical Economics, Berkeley Electronic Press, vol. 0(1). [Downloadable!]
  3. John H. Nachbar, 2003. "Beliefs in Repeated Games," ISER Discussion Paper 0597, Institute of Social and Economic Research, Osaka University. [Downloadable!]
  4. David Levine & Balázs Szentes, 2006. "Can A Turing Player Identify Itself?," Economics Bulletin, Economics Bulletin, vol. 1(1), pages 1-6. [Downloadable!]
  5. Scott E. Page, 1998. "Uncertainty, Difficulty, and Complexity," Research in Economics 98-08-076e, Santa Fe Institute. [Downloadable!]
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