When Analysis Fails: Heuristic Mechanism Design via Self-correcting Procedures

  • David C. Parkes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5404)

Abstract

Computational mechanism design (CMD) seeks to understand how to design game forms that induce desirable outcomes in multi-agent systems despite private information, self-interest and limited computational resources. CMD finds application in many settings, in the public sector for wireless spectrum and airport landing rights, to Internet advertising, to expressive sourcing in the supply chain, to allocating computational resources. In meeting the demands for CMD in these rich domains, we often need to bridge from the theory of economic mechanism design to the practice of deployable, computational mechanisms. A compelling example of this need arises in dynamic combinatorial environments, where classic analytic approaches fail and heuristic, computational approaches are required. In this talk I outline the direction of self-correcting mechanisms, which dynamically modify decisions via “output ironing” to ensure truthfulness and provide a fully computational approach to mechanism design. For an application, I suggest heuristic mechanisms for dynamic auctions in which bids arrive over time and supply may also be uncertain.

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References

  1. 1.
    Jackson, M.O.: Mechanism theory. In: Derigs, U. (ed.) The Encyclopedia of Life Support Systems, EOLSS Publishers (2003)Google Scholar
  2. 2.
    Nisan, N.: Introduction to mechanism design (for computer scientists). In: Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V. (eds.) Algorithmic Game Theory, Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  3. 3.
    Parkes, D.C.: Iterative Combinatorial Auctions: Achieving Economic and Computational Efficiency. PhD thesis, Department of Computer and Information Science, University of Pennsylvania (May 2001)Google Scholar
  4. 4.
    Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. Journal of Finance 16, 8–37 (1961)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Varian, H., MacKie-Mason, J.K.: Generalized Vickrey auctions. Technical report, University of Michigan (1995)Google Scholar
  6. 6.
    Clarke, E.H.: Multipart pricing of public goods. Public Choice 11, 17–33 (1971)CrossRefGoogle Scholar
  7. 7.
    Groves, T.: Incentives in teams. Econometrica 41, 617–631 (1973)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Cramton, P., Shoham, Y., Steinberg, R. (eds.): Combinatorial Auctions. MIT Press, Cambridge (2006)MATHGoogle Scholar
  9. 9.
    Goldberg, A., Hartline, J., Karlin, A., Saks, M., Wright, A.: Competitive auctions. Games and Economic Behavior 55, 242–269 (2006)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Nisan, N., Ronen, A.: Algorithmic mechanism design. Games and Economic Behavior 35, 166–196 (2001)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Lehmann, D., O’Callaghan, L.I., Shoham, Y.: Truth revelation in approximately efficient combinatorial auctions. Journal of the ACM 49(5), 577–602 (2002)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Conitzer, V., Sandholm, T.: Applications of automated mechanism design. In: Proc. UAI Bayesian Modeling Applications Workshop, Acapulco, Mexico (2003)Google Scholar
  13. 13.
    Likhodedov, A., Sandholm, T.: Approximating revenue-maximizing combinatorial auctions. In: Proc. of the National Conference on Artificial Intelligence (AAAI) (2005)Google Scholar
  14. 14.
    Parkes, D.C., Duong, Q.: An ironing-based approach to adaptive online mechanism design in single-valued domains. In: Proc. 22nd National Conference on Artificial Intelligence (AAAI 2007) (2007)Google Scholar
  15. 15.
    Constantin, F., Parkes, D.C.: Self-correcting sampling-based dynamic multi-unit auctions. Technical report, Harvard University (2008)Google Scholar
  16. 16.
    Hajiaghayi, M.T., Kleinberg, R., Mahdian, M., Parkes, D.C.: Online auctions with re-usable goods. In: Proc. ACM Conf. on Electronic Commerce, pp. 165–174 (2005)Google Scholar
  17. 17.
    Hajiaghayi, M.T., Kleinberg, R., Parkes, D.C.: Adaptive limited-supply online auctions. In: Proc. ACM Conf. on Electronic Commerce, pp. 71–80 (2004)Google Scholar
  18. 18.
    Pai, M., Vohra, R.: Optimal dynamic auctions. Technical report, Kellogg School of Management (2008)Google Scholar
  19. 19.
    Lavi, R., Nisan, N.: Competitive analysis of incentive compatible on-line auctions. In: Proc. 2nd ACM Conf. on Electronic Commerce (EC 2000), pp. 233–241 (2000)Google Scholar
  20. 20.
    Gallien, J.: Dynamic mechanism design for online commerce. Operations Research (2006)Google Scholar
  21. 21.
    Gershkov, A., Moldovanu, B.: Dynamic revenue maximization with heterogeneous objects: A mechanism design approach. Technical report, University of Bonn (2008)Google Scholar
  22. 22.
    Pavan, A., Segal, I., Toikka, J.: Dynamic mechanism design: Revenue equivalence, profit maximization, and information disclosure. Technical report, Stanford University (2008)Google Scholar
  23. 23.
    Hentenryck, P.V., Bent, R.: Online Stochastic Combinatorial Optimization. MIT Press, Cambridge (2006)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David C. Parkes
    • 1
  1. 1.School of Engineering and Applied SciencesHarvard UniversityUSA

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