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

Author

Listed:
  • Alessandro Pavan
  • Ilya Segal
  • Juuso Toikka

Abstract

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.

Suggested Citation

  • 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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    Citations

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    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. Mettral, Thomas, 2018. "Deterministic versus Stochastic Contracts in a Dynamic Principal-Agent Model," Rationality and Competition Discussion Paper Series 93, CRC TRR 190 Rationality and Competition.
    7. 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.
    8. repec:spr:joptap:v::y::i::d:10.1007_s10957-018-1230-8 is not listed on IDEAS
    9. 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.
    10. Deb, Rahul, 2008. "Optimal Contracting Of New Experience Goods," MPRA Paper 9880, University Library of Munich, Germany.
    11. 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

    Keywords

    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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