Advertisement

Using Priced Options to Solve the Exposure Problem in Sequential Auctions

  • Lonneke Mous
  • Valentin Robu
  • Han La Poutré
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 44)

Abstract

This paper studies the benefits of using priced options for solving the exposure problem that bidders with valuation synergies face when participating in multiple, sequential auctions. We consider a model in which complementary-valued items are auctioned sequentially by different sellers, who have the choice of either selling their good directly or through a priced option, after fixing its exercise price. We analyze this model from a decision-theoretic perspective and we show, for a setting where the competition is formed by local bidders, that using options can increase the expected profit for both buyers and sellers. Furthermore, we derive the equations that provide minimum and maximum bounds between which a synergy buyer’s bids should fall in order for both sides to have an incentive to use the options mechanism. Next, we perform an experimental analysis of a market in which multiple synergy bidders are active simultaneously.

Keywords

Option Price Multiagent System Reserve Price Option Contract Exercise Price 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hull, J.C.: Options, Futures, and Other Derivatives, 5th edn. Prentice-Hall, Englewood Cliffs (2003)zbMATHGoogle Scholar
  2. 2.
    Juda, A.I., Parkes, D.C.: An options-based method to solve the composability problem in sequential auctions. In: Faratin, P., Rodríguez-Aguilar, J.-A. (eds.) AMEC 2004. LNCS (LNAI), vol. 3435, pp. 44–58. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Juda, A.I., Parkes, D.C.: The sequential auction problem on ebay: An empirical analysis and a solution. In: Proc. of the 7th ACM Conf. on Electronic commerce, June 2006, pp. 180–189. ACM Press, New York (2006)CrossRefGoogle Scholar
  4. 4.
    Sandholm, T.: Algorithm for optimal winner determination in combinatorial auctions. Artificial Intelligence 135(1-2), 1–54 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Sandholm, T., Lesser, V.: Leveled-commitment contracting: a backtracking instrument for multiagent systems. AI Magazine 23(3), 89–100 (Fall 2002)Google Scholar
  6. 6.
    ’t Hoen, P.J., Redekar, G., Robu, V., Poutré, J.A.L.: chapter Decommitment in a Competitive Multi-Agent Transportation Setting. In: Whitestein Series in Software Agent Technologies, pp.409–433. Birkhäuser, Basel (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lonneke Mous
    • 1
    • 2
  • Valentin Robu
    • 1
  • Han La Poutré
    • 1
  1. 1.CWIDutch National Center for Mathematics and Computer ScienceSJ AmsterdamThe Netherlands
  2. 2.Econometrics InstituteErasmus University RotterdamDR RotterdamThe Netherlands

Personalised recommendations