Prior-Independent Multi-parameter Mechanism Design

  • Nikhil Devanur
  • Jason Hartline
  • Anna Karlin
  • Thach Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7090)


In a unit-demand multi-unit multi-item auction, an auctioneer is selling a collection of different items to a set of agents each interested in buying at most unit. Each agent has a different private value for each of the items. We consider the problem of designing a truthful auction that maximizes the auctioneer’s profit in this setting. Previously, there has been progress on this problem in the setting in which each value is drawn from a known prior distribution. Specifically, it has been shown how to design auctions tailored to these priors that achieve a constant factor approximation ratio [2, 5]. In this paper, we present a prior-independent auction for this setting. This auction is guaranteed to achieve a constant fraction of the optimal expected profit for a large class of, so called, “regular” distributions, without specific knowledge of the distributions.


Price Auction Optimal Mechanism Favorite Item Supply Constraint Optimal Auction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alaei, S.: Bayesian combinatorial auctions: Expanding single buyer mechanisms to many buyers. In: Proc. 52nd IEEE Symp. on Foundations of Computer Science (2011)Google Scholar
  2. 2.
    Bhattacharya, S., Goel, G., Gollapudi, S., Munagala, K.: Budget constrained auctions with heterogeneous items. In: Proc. 41st ACM Symp. on Theory of Computing (2010)Google Scholar
  3. 3.
    Bulow, J., Klemperer, P.: Auctions versus negotiations. American Economic Review 86, 180–194 (1996)Google Scholar
  4. 4.
    Cai, Y., Daskalakis, C., Matthew Weinberg, S.: On optimal multidimensional mechanism design. SIGecom Exchanges 10(2), 29–33 (2011)CrossRefGoogle Scholar
  5. 5.
    Chawla, S., Hartline, J., Malec, D., Sivan, B.: Sequential posted pricing and multi-parameter mechanism design. In: Proc. 41st ACM Symp. on Theory of Computing (2010)Google Scholar
  6. 6.
    Chawla, S., Malec, D., Sivan, B.: The power of randomness in bayesian optimal mechanism design. In: ACM Conference on Electronic Commerce, pp. 149–158 (2010)Google Scholar
  7. 7.
    Clarke, E.H.: Multipart pricing of public goods. Public Choice 11, 17–33 (1971)CrossRefGoogle Scholar
  8. 8.
    Dhangwatnotai, P., Roughgarden, T.: Qiqi Yan. Revenue maximization with a single sample. In: Proc. 12th ACM Conf. on Electronic Commerce (2010)Google Scholar
  9. 9.
    Groves, T.: Incentives in teams. Econometrica 41, 617–631 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hartline, J., Roughgarden, T.: Simple versus optimal mechanisms. In: Proc. 11th ACM Conf. on Electronic Commerce (2009)Google Scholar
  11. 11.
    Myerson, R.: Optimal auction design. Mathematics of Operations Research 6, 58–73 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Roughgarden, T., Talgam-Cohen, I., Yan, Q.: Prior-independence without sampling (2011) (manuscript)Google Scholar
  13. 13.
    Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. J. of Finance 16, 8–37 (1961)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nikhil Devanur
    • 1
  • Jason Hartline
    • 2
  • Anna Karlin
    • 3
  • Thach Nguyen
    • 3
  1. 1.Microsoft ResearchUSA
  2. 2.Northwestern UniversityUSA
  3. 3.University of WashingtonUSA

Personalised recommendations