Adaptive Adjustment of Starting Price for Agents in Continuous Double Auctions

  • Huiye Ma
  • Harry Timmermans
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5925)


Software agents can act flexibly in a variety of electronic marketplaces. Continuous Double Auction (CDA) is an efficient and common form of these marketplaces. There are several bidding strategies proposed in the literature for agents to adopt to compute their asks or bids in CDAs. For all of these bidding strategies, starting price has not been taken into account. However, in online auction marketplaces, the starting price is an important parameter for sellers to set and has been discussed many a time in the literature. Given the importance of starting price, the main objective of our work is to explore the effect of starting price on agents using various bidding strategies and how to adjust it adaptively within a dynamic CDA market. Experimental results confirm that when agents set their starting prices at varying values in different market situations, their profit changes significantly no matter which strategy they adopt. In order to guide agents to adjust their starting prices in dynamic and unknown markets, an adaptive mechanism is proposed. Experimental results show that agents adopting the adaptive mechanism generally outperform the corresponding agents without. Furthermore, another set of experiments are carried out to let all the agents use the adaptive mechanism and compete together in one market. Not surprisingly, the profit of agents is observed to drop down a lot in this situation.


Adaptive Mechanism Reservation Price Belief Function Bidding Strategy Transaction 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.


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  1. 1.
    He, M., Jennings, N.R., Leung, H.: On agent-mediated electronic commerce. IEEE Transactions on Knowledge and Data Engineering 15(4), 985–1003 (2003)CrossRefGoogle Scholar
  2. 2.
    Klemperer, P.: Auctions: Theory and Practice. Princeton University Press, Princeton (2004)Google Scholar
  3. 3.
    Xiong, G., Hashiyama, T., Okuma, S.: An electricity supplier bidding strategy through q-learning. Power Engineering Society Summer Meeting 3, 1516–1521 (2002)CrossRefGoogle Scholar
  4. 4.
    Li, L., Smith, S.F.: Speculation agents for dynamic multi-period continuous double auctions in b2b exchanges. In: Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences, pp. 1–9. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  5. 5.
    Guttman, R.H., Moukas, A.G., Maes, P.: Agent-mediated electronic commerce: A survey. The Knowledge Engeering Review 13(2), 147–159 (1998)CrossRefGoogle Scholar
  6. 6.
    Jennings, N.R.: An agent-based approach for building complex software systems. Communications of The ACM 44(4), 35–41 (2001)CrossRefGoogle Scholar
  7. 7.
    Bapna, R., Jank, W., Shmueli, G.: Price formation and its dynamics in online auctions. Decis. Support Syst. 44(3), 641–656 (2008)CrossRefGoogle Scholar
  8. 8.
    Ariely, D., Simonson, I.: Buying, bidding, playing, or competing? value assessment and decision dynamics in online auctions. Journal of Consumer Psychology 13, 113–123 (2003)Google Scholar
  9. 9.
    Lucking-Reiley, D., Bryan, D., Prasad, N., Reeves, D.: Pennies from ebay: The determinants of price in online auctions. Journal of Industrial Economics 55(2), 223–233 (2007)CrossRefGoogle Scholar
  10. 10.
    Gode, D.K., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy 101(1), 119–137 (1993)CrossRefGoogle Scholar
  11. 11.
    Smith, V.: An experimental study of competitive market behavior. Journal of Political Economy 70(2), 111–137 (1962)CrossRefGoogle Scholar
  12. 12.
    Cliff, D., Bruten, J.: Minimal-intelligence agents for bargaining behaviors in market-based environments. Technical Report HP–97–91, Bristol, UK (August 1997)Google Scholar
  13. 13.
    Preist, C., van Tol, M.: Adaptive agents in a persistent shout double auction. In: Proceedings of the 1st International Conference on Information and Computation Economies, pp. 11–18. ACM, New York (1998)CrossRefGoogle Scholar
  14. 14.
    Gjerstad, S., Dickhaut, J.: Price formation in double auctions. Games and Economic Behavior 22, 1–29 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Tesauro, G., Bredin, J.L.: Strategic sequential bidding in auctions using dynamic programming. In: Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 591–598. ACM, New York (2002)CrossRefGoogle Scholar
  16. 16.
    He, M., Leung, H., Jennings, N.R.: A fuzzy-logic based bidding strategy for autonomous agents in continuous double auctions. IEEE Transactions on Knowledge and Data Engineering 15(6), 1345–1363 (2003)CrossRefGoogle Scholar
  17. 17.
    Ma, H., Leung, H.: An adaptive attitude bidding strategy for agents in continuous double auctions. Electronic Commerce Research and Applications 6(4), 383–398 (2007)CrossRefGoogle Scholar
  18. 18.
    Vytelingum, P., Cliff, D., Jennings, N.R.: Strategic bidding in continuous double auctions. Artif. Intell. 172(14), 1700–1729 (2008)zbMATHCrossRefGoogle Scholar
  19. 19.
    Ma, H., Leung, H.: Adaptive soft bid determination in bidding strategies for continuous double auctions. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, Hong Kong, China (November 2005)Google Scholar
  20. 20.
    Ma, H., Leung, H.: Effect of time strategies on the profit of agents using adaptive bid softness determination in continuous double auctions with a fixed deadline. In: Proceedings of 2006 IEEE Joint Conference on E-Commerce Technology and Enterprise Computing, E-Commerce and E-Services, Washington, DC, USA, pp. 16–23. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  21. 21.
    Ma, H., Leung, H.: Bidding Strategies in Agent-Based Continuous Double Auctions. Springer, Heidelberg (2008)Google Scholar
  22. 22.
    Ren, F., Zhang, M., Sim, K.M.: Adaptive conceding strategies for automated trading agents in dynamic, open markets. Decis. Support Syst. 46(3), 704–716 (2009)CrossRefGoogle Scholar
  23. 23.
    Sim, K.: A market-driven model for designing negotiation agents. Computational Intelligence 18(4), 618–637 (2002)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Dumas, M., Aldred, L., Governatori, G., Hofstede, A.: Probabilistic automated bidding in multiple auctions. Electronic Commerce Research 5, 25–49 (2005)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Huiye Ma
    • 1
  • Harry Timmermans
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands

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