Performance of Auctions and Sealed Bids

  • Erol Gelenbe
  • László Györfi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5652)


We develop models of automated E-commerce techniques, which predict the economic outcomes of these decision mechanisms, including the price attained by a good and the resulting income per unit time, as a function of the rate at which bidders provide the bids and of the time taken by the seller to decide whether to accept a bid. This paper extends previous work in two main directions. Since automated E-commerce mechanisms are typically implemented in software residing on the Internet, this paper shows how network quality of service will impact the economic outcome of automated auctions. We also analyse sealed bids which can also be automated, but which differ significantly from auctions in the manner in which information is shared between the bidders and the the party that decides the outcome. The approach that we propose opens novel avenues of research that bring together traditional computer and system performance analysis and the economic analysis of Internet based trading methods.


Auctions Sealed Bids Networked Economics Economic Performance Quality of Service 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chow, Y.S., Moriguti, S., Robbins, H., Samuels, S.M.: Optimal selection based on relative rank (the Secretary problem). Israel J. Math. 2, 81–90 (1964)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Gelenbe, E., Mitrani, I.: Analysis and Synthesis of Computer Systems. Academic Press, New York (1980); Imperial College Press & World Scientific, Singapore and London (2nd edn.) (in press, 2009) Google Scholar
  3. 3.
    Milgrom, P.R., Weber, R.: A theory of auctions and competitive bidding. Econometrica 50, 1089–1122 (1982)CrossRefMATHGoogle Scholar
  4. 4.
    McAfee, R.P., McMillan, J.: Auctions and bidding. J. Economic Literature 25, 699–738 (1987)MATHGoogle Scholar
  5. 5.
    Karr, A.F.: Point Processes and their Statistical Inference. Marcel Dekker, Inc., New York (1991)MATHGoogle Scholar
  6. 6.
    Medhi, J.: Stochastic Processes, 2nd edn. Wiley Eastern Ltd., New Delhi (1994)MATHGoogle Scholar
  7. 7.
    Gelenbe, E., Pujolle, G.: Introduction to Networks of Queues, 2nd edn. J. Wiley & Sons, Chichester (1998)MATHGoogle Scholar
  8. 8.
    Shehory, O.: Optimality and risk in purchase from multiple auctions. In: Klusch, M., Zambonelli, F. (eds.) CIA 2001. LNCS (LNAI), vol. 2182, pp. 142–153. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Guo, X.: An optimal strategy for sellers in an online auction. ACM Trans. Internet Tech. 2(1), 1–13 (2002)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Shehory, O.: Optimal bidding in multiple concurrent auctions. International Journal of Cooperative Information Systems 11(3–4), 315–327 (2002)CrossRefGoogle Scholar
  11. 11.
    Finch, S.R.: Optimal stopping constants. Mathematical Constants, pp. 361–363. Cambridge Univ. Press, Cambridge (2003)Google Scholar
  12. 12.
    Gelenbe, E.: Sensible decisions based on QoS. Computational Management Science 1(1), 1–14 (2003)CrossRefMATHGoogle Scholar
  13. 13.
    Dash, N.R., Jennings, N.R., Parkes, D.C.: Computational mechanism design: a call to arms. In: IEEE Intelligent Systems, November-December 2003, pp. 40–47 (2003)Google Scholar
  14. 14.
    Hajiaghayi, M.T., Kleinberg, R., Parkes, D.C.: Adaptive limited-supply online auctions. In: Proc. 5th ACM Conference on Electronic Commerce, May 17–20, pp. 71–90. ACM Press, New York (2004)CrossRefGoogle Scholar
  15. 15.
    David, E., Rogers, A., Schiff, J., Kraus, S., Jennings, N.R.: Optimal design of English auctions with discrete bid levels. In: Proc. of 6th ACM Conference on Electronic Commerce (EC 2005), Vancouver, Canada, pp. 98–107 (2005)Google Scholar
  16. 16.
    Fatima, S., Wooldridge, M., Jennings, N.R.: Sequential auctions for objects with common and private values. In: Proc. 4th Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems, Utrecht, Netherlands, pp. 635–642 (2005)Google Scholar
  17. 17.
    Gelenbe, E.: Analysis of automated auctions. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Dramitinos, M., Stamoulis, G., Courcoubetis, C.: An auction mechanism for allocating the bandwidth of networks to their users. Computer Networks 51(18), 4979–4996 (2007)CrossRefMATHGoogle Scholar
  19. 19.
    Kovacs, L., Vidacs, A., Heder, B.: Spectrum auction and pricing in dynamic spectrum allocation networks. The Mediterranean Journal of Computers and Networks 4(3), 125–138 (2008)Google Scholar
  20. 20.
    Gelenbe, E.: Analyis of single and networked auctions. ACM Trans. Internet Technology 9(2), Article 8 (May 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Erol Gelenbe
    • 1
  • László Györfi
    • 2
  1. 1.Intelligent Systems and Networks Group Department of Electrical and Electronic EngineeringImperial CollegeLondon
  2. 2.Dept. Computer Science & Information TheoryTechnical University of BudapestBudapestHungary

Personalised recommendations