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Attaining efficiency with imperfect public monitoring and one-sided Markov adverse selection

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  • Barron, Daniel

    (Kellogg School of Management, Northwestern University)

Abstract

I prove an efficiency result for repeated games with imperfect public monitoring in which one player's utility is privately known and evolves according to a Markov process. Under certain assumptions, patient players can attain approximately efficient payoffs in equilibrium. The public signal must satisfy a "pairwise full rank" condition that is somewhat stronger than the monitoring condition required in the Folk Theorem proved by Fudenberg, Levine, and Maskin (1994). Under stronger assumptions, the efficiency result partially extends to settings in which one player has private information that determines every player's payoff. The proof is partially constructive and uses an intuitive technique to mitigate the impact of private information on continuation payoffs.

Suggested Citation

  • Barron, Daniel, 2017. "Attaining efficiency with imperfect public monitoring and one-sided Markov adverse selection," Theoretical Economics, Econometric Society, vol. 12(3), September.
  • Handle: RePEc:the:publsh:1934
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    References listed on IDEAS

    as
    1. Fudenberg, Drew & Yamamoto, Yuichi, 2011. "The folk theorem for irreducible stochastic games with imperfect public monitoring," Journal of Economic Theory, Elsevier, vol. 146(4), pages 1664-1683, July.
    2. Athey, Susan & Bagwell, Kyle, 2001. "Optimal Collusion with Private Information," RAND Journal of Economics, The RAND Corporation, vol. 32(3), pages 428-465, Autumn.
    3. Drew Fudenberg & David K. Levine, 2008. "Efficiency and Observability with Long-Run and Short-Run Players," World Scientific Book Chapters, in: Drew Fudenberg & David K Levine (ed.), A Long-Run Collaboration On Long-Run Games, chapter 13, pages 275-307, World Scientific Publishing Co. Pte. Ltd..
    4. Drew Fudenberg & Eric Maskin, 2008. "The Folk Theorem In Repeated Games With Discounting Or With Incomplete Information," World Scientific Book Chapters, in: Drew Fudenberg & David K Levine (ed.), A Long-Run Collaboration On Long-Run Games, chapter 11, pages 209-230, World Scientific Publishing Co. Pte. Ltd..
    5. Glenn Ellison, 1994. "Cooperation in the Prisoner's Dilemma with Anonymous Random Matching," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(3), pages 567-588.
    6. Johannes Hörner & Takuo Sugaya & Satoru Takahashi & Nicolas Vieille, 2011. "Recursive Methods in Discounted Stochastic Games: An Algorithm for δ→ 1 and a Folk Theorem," Econometrica, Econometric Society, vol. 79(4), pages 1277-1318, July.
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    8. Matthew O Jackson & Hugo F Sonnenschein, 2007. "Overcoming Incentive Constraints by Linking Decisions -super-1," Econometrica, Econometric Society, vol. 75(1), pages 241-257, January.
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    Cited by:

    1. James Best & Daniel Quigley, 2016. "Persuasion for the Long-Run," Economics Papers 2016-W12, Economics Group, Nuffield College, University of Oxford.

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    More about this item

    Keywords

    Repeated Bayesian games; efficiency;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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