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Maximizing Revenue in Sequential Auctions

  • Edith Elkind
  • Shaheen Fatima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4858)

Abstract

We study sequential auctions for private value objects and unit-demand bidders using second-price sealed-bid rules. We analyze this scenario from the seller’s perspective and consider several approaches to increasing the total revenue. We derive the equilibrium bidding strategies for each individual auction. We then study the problem of selecting an optimal agenda, i.e., a revenue-maximizing ordering of the auctions. We describe an efficient algorithm that finds an optimal agenda in the important special case when the revenue of each auction is guaranteed to be strictly positive. We also show that the seller can increase his revenue by canceling one or more auctions, even if the number of bidders exceeds the number of objects for sale, and analyze the bidders’ behavior and the seller’s profit for different cancellation rules.

Keywords

Optimal Agenda Total Revenue Bidding Strategy Combinatorial Auction Sequential 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.

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References

  1. 1.
    Bernhardt, D., Scoones, D.: A note on sequential auctions. American Economic Review 84(3), 653–657 (1994)Google Scholar
  2. 2.
    Dasgupta, P., Maskin, E.: Efficient auctions. Quarterly Journal of Economics 115, 341–388 (2000)zbMATHCrossRefGoogle Scholar
  3. 3.
    Edmonds, J.: Optimum branchings. Journal of Research of the National Bureau of Standards  (1967)Google Scholar
  4. 4.
    Elkind, E., Fatima, S.: Maximizing Revenue in Sequential Auctions (full version). Available from, http://www.csc.liv.ac.uk/~elkind/agenda.pdf
  5. 5.
    Elmaghraby, W.: The importance of ordering in sequential auctions. Management Science 49(5), 673–682 (2003)CrossRefGoogle Scholar
  6. 6.
    Galambos, J.: The asymptotic theory of extreme order statistics. John Wiley and Sons, West Sussex, England (1978)zbMATHGoogle Scholar
  7. 7.
    Sandholm, T., Suri, S.: BOB: Improved winner determination in combinatorial auctions and generalizations. Artificial Intelligence 145, 33–58 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Vickrey, W.: Counterspeculation, auctions and competitive sealed tenders. Journal of Finance 16, 8–37 (1961)CrossRefGoogle Scholar
  9. 9.
    Wellman, M.P., Walsh, W.E., Wurman, P.R., McKie-Mason, J.K.: Auction protocols for decentralised scheduling. Games and Economic Behavior 35, 271–303 (2001)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Edith Elkind
    • 1
  • Shaheen Fatima
    • 2
  1. 1.School of Electronics and Computer Science, University of Southampton, SO17 1BJUnited Kingdom
  2. 2.Department of Computer Science, Loughborough University, Loughborough, LE11 3TUUnited Kingdom

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