Designing Risk-Averse Bidding Strategies in Sequential Auctions for Transportation Orders

  • Valentin Robu
  • Han La Poutré
Part of the Studies in Computational Intelligence book series (SCI, volume 233)

Summary

Designing efficient bidding strategies for agents participating in multiple, sequential auctions remains an important challenge for researchers in agent-mediated electronic markets. The problem is particularly hard if the bidding agents have complementary (i.e. super-additive) utilities for the items being auctioned, such as is often the case in distributed transportation logistics. This paper studies the effect that a bidding agent’s attitude towards taking risks plays in her optimal, decision-theoretic bidding strategy. We model the sequential bidding decision process as an MDP and we analyze, for a category of expectations of future price distributions, the effect that a bidder’s risk aversion profile has on her decision-theoretic optimal bidding policy. Next, we simulate the above strategies, and we study the effect that an agent’s risk aversion has on the chances of winning the desired items, as well as on the market efficiency and expected seller revenue. The paper extends the results presented in our previous work (reported in [1]), not only by providing additional details regarding the analytical part, but also by considering a more complex and realistic market setting for the simulations. This simulation setting is based on a real transportation logistics scenario [2]), in which bidders have to choose between several combinations (bundles) of orders that can be contracted for transportation.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Robu, V., La Poutré, H.: Designing bidding strategies in sequential auctions for risk averse agents. In: Proc. of the 9th Int. Workshop on Agent-Mediated Electronic Commerce (AMEC 2007), Honolulu, Hawai’i. LNCS (LNBI). Springer, Heidelberg (2007) (to appear)Google Scholar
  2. 2.
    Robu, V., Noot, H., La Poutré, H., van Schijndel, W.-J.: An agent platform for auction-based allocation of loads in transportation logistics. In: Proc. of AAMAS 2008 (Industry Track), Estoril, Portugal, pp. 3–10. IFAAMAS Press (2008)Google Scholar
  3. 3.
    Reeves, D.M., Wellman, M.P., MacKie-Mason, J.K., Osepayshvili, A.: Exploring bidding strategies for market based scheduling. Decision Support Syst. 39, 67–85 (2005)CrossRefGoogle Scholar
  4. 4.
    Boutilier, C., Goldszmidt, M., Sabata, B.: Sequential auctions for the allocation of resources with complementarities. In: Proc. of the 16th Int. Joint Conf. on AI (IJCAI 1999), Stockholm, pp. 527–534 (1999)Google Scholar
  5. 5.
    Greenwald, A., Boyan, J.: Bidding under uncertainty: Theory and experiments. In: Proceedings of the 20th Conf. on Uncertainty in AI (UAI 2004), pp. 209–216 (2004)Google Scholar
  6. 6.
    Hoen, P.J., La Poutré, J.A.: Repeated auctions with complementarities. In: La Poutré, H., Sadeh, N.M., Janson, S. (eds.) AMEC 2005 and TADA 2005. LNCS, vol. 3937, pp. 16–29. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Osepayshvili, A., Wellman, M.P., Reeves, D.M., MacKie-Mason, J.K.: Self-confirming price prediction for bidding in simultaneous ascending auctions. In: Proc. of the 21st Conf. on Uncertainty in AI, UAI 2005 (2005)Google Scholar
  8. 8.
    Gerding, E.H., Dash, R.K., Yuen, D.C.K., Jennings, N.R.: Bidding optimally in concurrent second-price auctions of perfectly substitutable goods. In: Proc. of AAMAS 2006, Honolulu, Hawaii, pp. 267–274 (2007)Google Scholar
  9. 9.
    Arrow, K.J.: Aspects of the Theory of Risk-Bearing. Y. Hahnsson Foundation, Helsinki (1965)Google Scholar
  10. 10.
    Green, W.H.: Econometric Analysis. Prentice Hall, Englewood Cliffs (1993)Google Scholar
  11. 11.
    Paarsch, H.J., Hong, H.: An Introduction to the Structural Econometrics of Auction Data. MIT Press, Cambridge (2006)Google Scholar
  12. 12.
    Mas-Collel, A., Whinston, M.D., Green, J.R.: Microeconomic Theory. Oxford University Press, Oxford (1995)Google Scholar
  13. 13.
    Milgrom, P.: Putting Auction Theory to Work. Cambridge University Press, Cambridge (2004)Google Scholar
  14. 14.
    Babanov, A., Collins, J., Gini, M.: Harnessing the search for rational bid schedules with stochastic search. In: Proc. of AAMAS 2004, New York, USA, pp. 355–368 (2004) Google Scholar
  15. 15.
    Liu, Y., Goodwin, R., Koenig, S.: Risk-averse auction agents. In: Proc. of AAMAS 2003, Melbourne, Australia, pp. 353–360 (2003)Google Scholar
  16. 16.
    Dash, R.K., Jennings, N.R., Parkes, D.C.: Computational mechanism design: A call to arms. IEEE Intelligent Systems, 40–47 (2003)Google Scholar
  17. 17.
    Vetsikas, I.A., Jennings, N.R.: Towards agents participating in realistic multiunit sealed-bid auctions. In: Proc. 7th Int. Conf. on Autonomous Agents and Multi-agent Systems (AAMAS 2008), Estoril, Portugal, pp. 1621–1624 (2008)Google Scholar
  18. 18.
    Jiang, A.X., Leyton-Brown, K.: Bidding agents for online auctions with hidden bids. Machine Learning (2006) (to appear)Google Scholar
  19. 19.
    Milgrom, P., Weber, R.J.: A theory of auctions and competitive bidding - part 2. Economic Theory of Auctions (2000)Google Scholar
  20. 20.
    van der Putten, S., Robu, V., La Poutré, J.A., Jorritsma, A., Gal, M.: Automating supply chain negotiations using autonomous agents: a case study in transportation logistics. In: Proc. 5th Int. Joint Conf. on Autonomous Agents and Multi Agent Systems (AAMAS 2006), Industry Track, pp. 1506–1513. ACM Press, New York (2006)CrossRefGoogle Scholar
  21. 21.
    Juda, A.I., Parkes, D.C.: An options-based method to solve the composability problem in sequential auction. In: Agent-Mediated Electronic Commerce VI, pp. 44–58. Springer, Heidelberg (2006)Google Scholar
  22. 22.
    Mous, L., Robu, V., La Poutré, H.: Using priced options to solve the exposure problem in sequential auctions. In: Proc. of the 10th Int. Workshop on Agent-Mediated Electronic Commerce (AMEC 2008), Estoril, Portugal. LNCS (LNAI). Springer, Heidelberg (2008) (to appear)Google Scholar
  23. 23.
    Mous, L., Robu, V., La Poutré, H.: Can priced options solve the exposure problem in sequential auctions. ACM SIGEcom Exchanges 7(2) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Valentin Robu
    • 1
  • Han La Poutré
    • 1
  1. 1.Dutch National Research Institute for Mathematics and Computer ScienceCWIAmsterdamThe Netherlands

Personalised recommendations