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Conditional Universal Consistency

  • Fudenberg, Drew
  • Levine, David K.

Players choose an action before learning an outcome chosen according to an unknown and history-dependent stochastic rule. Procedures that categorize outcomes, and use a randomized variation on fictitious play within each category are studied. These procedures are “conditionally consistent:†they yield almost as high a time-average payoff as if the player knew the conditional distributions of actions given categories. Moreover, given any alternative procedure, there is a conditionally consistent procedure whose performance is no more than epsilon worse regardless of the discount factor. We also discuss cycles, and argue that the time-average of play should resemble a correlated equilibrium.

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Article provided by Elsevier in its journal Games and Economic Behavior.

Volume (Year): 29 (1999)
Issue (Month): 1-2 (October)
Pages: 104-130

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Handle: RePEc:eee:gamebe:v:29:y:1999:i:1-2:p:104-130
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/622836

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  1. Fudenberg, D. & Levine, D.K., 1991. "Self-Confirming Equilibrium ," Working papers 581, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Drew Fudenberg & David K. Levine, 1996. "An Easier Way to Calibrate," Levine's Working Paper Archive 2059, David K. Levine.
  3. Sergiu Hart & Andreu Mas-Colell, 2000. "A Simple Adaptive Procedure Leading to Correlated Equilibrium," Econometrica, Econometric Society, vol. 68(5), pages 1127-1150, September.
  4. Drew Fudenberg & David K. Levine, 1998. "Learning in Games," Levine's Working Paper Archive 2222, David K. Levine.
  5. Nimrod Megiddo, 1979. "On Repeated Games with Incomplete Information Played by Non-Bayesian Players," Discussion Papers 373, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  6. N. Megiddo, 2010. "On Repeated Games with Incomplete Information Played with Non-Bayesian Players," Levine's Working Paper Archive 480, David K. Levine.
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  8. Fudenberg, D. & Kreps, D.M., 1992. "Learning Mixed Equilibria," Working papers 92-13, Massachusetts Institute of Technology (MIT), Department of Economics.
  9. Y. Freund & R. Schapire, 2010. "A Decision Theoretic Generalization of On-Line Learning and an Application to Boosting," Levine's Working Paper Archive 570, David K. Levine.
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  11. Foster, Dean P. & Vohra, Rakesh, 1999. "Regret in the On-Line Decision Problem," Games and Economic Behavior, Elsevier, vol. 29(1-2), pages 7-35, October.
  12. Sanchirico, Chris William, 1996. "A Probabilistic Model of Learning in Games," Econometrica, Econometric Society, vol. 64(6), pages 1375-93, November.
  13. Ehud Kalai & Ehud Lehrer & Rann Smorodinsky, 2010. "Calibrated Forecasting and Merging," Levine's Working Paper Archive 584, David K. Levine.
  14. T.H. Chung, 2010. "Approximate Methods for Sequential Decision Making Using Expert Advice," Levine's Working Paper Archive 564, David K. Levine.
  15. Aoyagi, Masaki, 1996. "Evolution of Beliefs and the Nash Equilibrium of Normal Form Games," Journal of Economic Theory, Elsevier, vol. 70(2), pages 444-469, August.
  16. Foster, Dean P. & Vohra, Rakesh V., 1997. "Calibrated Learning and Correlated Equilibrium," Games and Economic Behavior, Elsevier, vol. 21(1-2), pages 40-55, October.
  17. P. Auer & N. Cesa-Bianchi & Y. Freund & R. Schapire, 2010. "Gambling in a rigged casino: The adversarial multi-armed bandit problem," Levine's Working Paper Archive 462, David K. Levine.
  18. M. Aoyagi, 2010. "Evolution of Beliefs and the Nash Equilibrium of a Normal Form Game," Levine's Working Paper Archive 562, David K. Levine.
  19. M. Feder & N. Mehrav & M. Gutman, 2010. "Universal Prediction of Individual Sequences," Levine's Working Paper Archive 567, David K. Levine.
  20. Sonsino, Doron, 1997. "Learning to Learn, Pattern Recognition, and Nash Equilibrium," Games and Economic Behavior, Elsevier, vol. 18(2), pages 286-331, February.
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