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Dynamic Mechanism Design: Incentive Compatibility, Profit Maximization and Information Disclosure


  • Alessandro Pavan
  • Ilya Segal
  • Juuso Toikka


We examine the design of incentive-compatible screening mechanisms for dynamic environments in which the agents types follow a (possibly non-Markov) stochastic process, decisions may be made over time and may affect the type process, and payoffs need not be time-separable. We derive a formula for the derivative of an agent’s equilibrium payoff with respect to his current type in an incentive-compatible mechanism, which summarizes all first-order conditions for incentive compatibility and generalizes Mirrlees’s envelope formula of static mechanism design. We provide conditions on the environment under which this formula must hold in any incentive-compatible mechanism. When specialized to quasi-linear environments, this formula yields a dynamic revenue-equivalence result and an expression for dynamic virtual surplus, which is instrumental for the design of optimal mechanisms. We also provide some sufficient conditions for incentive compatibility, and for its robustness to an agent’s observation of the other agents’ past and future types. We apply these results to a number of novel settings, including the design of profit-maximizing auctions and durable-good selling mechanisms for buyers whose values follow an AR(k) process.

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  • Alessandro Pavan & Ilya Segal & Juuso Toikka, 2009. "Dynamic Mechanism Design: Incentive Compatibility, Profit Maximization and Information Disclosure," Discussion Papers 1501, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  • Handle: RePEc:nwu:cmsems:1501

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    References listed on IDEAS

    1. Ricardo J. Caballero & Emmanuel Farhi & Mohamad L. Hammour, 2006. "Speculative Growth: Hints from the U.S. Economy," American Economic Review, American Economic Association, vol. 96(4), pages 1159-1192, September.
    2. Dow, James & Gorton, Gary, 1997. " Stock Market Efficiency and Economic Efficiency: Is There a Connection?," Journal of Finance, American Finance Association, vol. 52(3), pages 1087-1129, July.
    3. Manuel Amador & Pierre-Olivier Weill, 2010. "Learning from Prices: Public Communication and Welfare," Journal of Political Economy, University of Chicago Press, vol. 118(5), pages 866-907.
    4. George-Marios Angeletos & Jennifer La'O, 2010. "Noisy Business Cycles," NBER Chapters,in: NBER Macroeconomics Annual 2009, Volume 24, pages 319-378 National Bureau of Economic Research, Inc.
    5. George-Marios Angeletos & Alessandro Pavan, 2009. "Policy with Dispersed Information," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 11-60, March.
    6. Goldstein, Itay & Ozdenoren, Emre & Yuan, Kathy, 2013. "Trading frenzies and their impact on real investment," Journal of Financial Economics, Elsevier, vol. 109(2), pages 566-582.
    7. Bartosz Mackowiak & Mirko Wiederholt, 2009. "Optimal Sticky Prices under Rational Inattention," American Economic Review, American Economic Association, vol. 99(3), pages 769-803, June.
    8. Franck Portier & Aude Pommeret & Olivier Loisel, 2008. "Monetary policy and herd behavior in new-tech investment," 2008 Meeting Papers 444, Society for Economic Dynamics.
    9. Christian Hellwig & Laura Veldkamp, 2009. "Knowing What Others Know: Coordination Motives in Information Acquisition," Review of Economic Studies, Oxford University Press, vol. 76(1), pages 223-251.
    10. Gilchrist, Simon & Himmelberg, Charles P. & Huberman, Gur, 2005. "Do stock price bubbles influence corporate investment?," Journal of Monetary Economics, Elsevier, vol. 52(4), pages 805-827, May.
    11. Giovanni Cespa & Xavier Vives, 2012. "Dynamic Trading and Asset Prices: Keynes vs. Hayek," Review of Economic Studies, Oxford University Press, vol. 79(2), pages 539-580.
    12. Laura L. Veldkamp, 2006. "Media Frenzies in Markets for Financial Information," American Economic Review, American Economic Association, vol. 96(3), pages 577-601, June.
    13. George-Marios Angeletos & Alessandro Pavan, 2007. "Efficient Use of Information and Social Value of Information," Econometrica, Econometric Society, vol. 75(4), pages 1103-1142, July.
    14. Amador, Manuel & Weill, Pierre-Olivier, 2006. "Learning from Private and Public Observation of Other's Actions," MPRA Paper 109, University Library of Munich, Germany.
    15. Xavier Vives, 2007. "Information and Learning in Markets," Levine's Bibliography 122247000000001520, UCLA Department of Economics.
    16. Itay Goldstein & Alexander Guembel, 2008. "Manipulation and the Allocational Role of Prices," Review of Economic Studies, Oxford University Press, vol. 75(1), pages 133-164.
    17. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
    18. Christian Hellwig, "undated". "Monetary Business Cycle Models: Imperfect Information (Review Article, March 2006)," UCLA Economics Online Papers 377, UCLA Department of Economics.
    19. David P. Myatt & Chris Wallace, 2012. "Endogenous Information Acquisition in Coordination Games," Review of Economic Studies, Oxford University Press, vol. 79(1), pages 340-374.
    20. Jennifer La'O, 2010. "Collateral Constraints and Noisy Fluctuations," 2010 Meeting Papers 780, Society for Economic Dynamics.
    21. Dupor, Bill, 2005. "Stabilizing non-fundamental asset price movements under discretion and limited information," Journal of Monetary Economics, Elsevier, vol. 52(4), pages 727-747, May.
    22. Vives, X., 1993. "Learning from Others," UFAE and IAE Working Papers 206.93, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    23. George-Marios Angeletos & Guido Lorenzoni & Alessandro Pavan, 2007. "Wall Street and Silicon Valley: A Delicate Interaction," NBER Working Papers 13475, National Bureau of Economic Research, Inc.
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    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. LiCalzi, Marco & Pavan, Alessandro, 2005. "Tilting the supply schedule to enhance competition in uniform-price auctions," European Economic Review, Elsevier, vol. 49(1), pages 227-250, January.
    2. Said, Maher, 2012. "Auctions with dynamic populations: Efficiency and revenue maximization," Journal of Economic Theory, Elsevier, vol. 147(6), pages 2419-2438.
    3. Daniel Garrett & Alessandro Pavan, 2009. "Dynamic Managerial Compensation: A Mechanism Design Approach," 2009 Meeting Papers 375, Society for Economic Dynamics.
    4. Zhang, Jun, 2013. "Revenue maximizing with return policy when buyers have uncertain valuations," International Journal of Industrial Organization, Elsevier, vol. 31(5), pages 452-461.
    5. Stéphane Auray & Thomas Mariotti & Fabien Moizeau, 2011. "Dynamic regulation of quality," RAND Journal of Economics, RAND Corporation, vol. 42(2), pages 246-265, June.
    6. George-Marios Angeletos & Alessandro Pavan, 2007. "Socially Optimal Coordination: Characterization and Policy Implications," Journal of the European Economic Association, MIT Press, vol. 5(2-3), pages 585-593, 04-05.
    7. Daniel F. Garrett & Alessandro Pavan, 2012. "Managerial Turnover in a Changing World," Journal of Political Economy, University of Chicago Press, vol. 120(5), pages 879-925.
    8. Deb, Rahul, 2008. "Optimal Contracting Of New Experience Goods," MPRA Paper 9880, University Library of Munich, Germany.
    9. Xiaojun Zhao, 2015. "Optimal Income Taxations with Information Asymmetry: The Lagrange Multiplier Approach," Annals of Economics and Finance, Society for AEF, vol. 16(1), pages 199-229, May.

    More about this item


    asymmetric information; stochastic processes; incentives JEL Classification Numbers: D82; C73; L1;

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance


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