Skip to main content
SpringerLink
Log in

Adaptive bidding in combinatorial auctions in dynamic markets

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

Combinatorial auction, where bidders can bid on bundles of items, has been the subject of increasing interest in recent years. Although much research work has been conducted on combinatorial auctions, most has been focusing on the winner determination problem. A largely unexplored area of research in combinatorial auctions is the design of bidding strategies, in particular, those that can be used in open and dynamic markets in which market situation is generally constantly changing. Obviously, a good bidding strategy to be used in such realistic markets cannot be obtained by analytical methods, which require all market information be known before a solution can possibly be found. Machine learning based approaches are not completely appropriate, either, as an optimal strategy learned from one market generally no longer perform well when the market situation changes. In this paper, we propose a new adaptive bidding strategy for combinatorial auction-based resource allocation problem in such dynamic markets. A bidder adopting this strategy constantly perceives the market situation, and adaptively reviews and adjusts his bid determination, thus responding to the dynamic market in a timely manner. Experiment results show that agents adopting this adaptive bidding strategy perform very well, even without any prior knowledge about the market.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Some research into the bidding strategies in other types of combinatorial auctions was reported in the literature, e.g., Parkes and Ungar (2000) proposed a myopic best-response bidding strategy in the first-price open-cry combinatorial auctions.

  2. Here, an exponential function is used as the left part of the regression curve, and actually, it does not matter too much if we use other functions. This is because in the second set of experiments, we never use Eq. 9 to estimate the optimal profit margin of the market whose rf value falls out of [0.5, 1.2], and the estimated optimal profit will not vary much if other fitting functions are used.

  3. Actually, setting this upper bound does not affect the performance of the adaptive strategy. This is because without this constraint, when the optimal profit margin is a value infinitely close to 1, the profit margin generated by the adaptive strategy is also very close to 1, and the bidder using the adaptive strategy does not losing utility at all.

  4. If we do not set the upper bound of 0.95 for bidder’s profit margin in the adaptive strategies, the bidder can even get the resources nearly free.

References

  • An N, Elmaghraby W, Keskinocak P (2005) Bidding strategies and their impact on revenues in combinatorial auctions. J Revenue Pricing Manage 3(4):337–357

    Article  Google Scholar 

  • Archer A, Papadimitriou C, Talwar K, Tardos E (2004) An approximate truthful mechanism for combinatorial auctions with single parameter agents. Internet Math 1(2):129–150

    Article  MathSciNet  MATH  Google Scholar 

  • Ausubel L, Cramton P, McAfee R, McMillan J (1997) Synergies in wireless telephony: evidence from the broadband PCS auctions. J Econ Manage Strategy 6(3):497–527

    Article  Google Scholar 

  • Ausubel LM, Cramton P, Milgrom P (2006) The clock-proxy auction: a practical combinatorial auction design. In: Cramton P, Shoham Y, Steinberg R (eds) Combinatorial auctions, chap 5, MIT Press, Cambridge, pp 115–138

  • Avasarala V, Polavarapu H, Mullen T (2006) An approximate algorithm for resource allocation using combinatorial auctions. In: Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology. IEEE Computer Society, Washington, DC, pp 571–578

  • Berhault M, Huang H, Keskinocak P, Koenig S, Elmaghraby W, Griffin P, Kleywegt A (2003) Robot exploration with combinatorial auctions. In: Proceedings of the international conference on intelligent robots and systems, vol 2. IEEE, Piscataway

  • Buyya R, Abramson D, Giddy J, Stockinger H (2002) Economic models for resource management and scheduling in grid computing. Concurrency Comput Pract Exp 14(13–15):1507–1542

    Article  MATH  Google Scholar 

  • Chen M, Yang G, Liu X Gridmarket (2004) A practical, efficient market balancing resource for Grid and P2P computing. Lect Notes Comput Sci 3033:612–619

  • Cheng J, Wellman M (1998) The WALRAS algorithm: a convergent distributed implementation of general equilibrium outcomes. Comput Econ 12(1):1–24

    Article  MATH  Google Scholar 

  • Cramton P, Shoham Y, Steinberg R (2006) Combinatorial auctions. MIT Press, Cambridge

  • de Vries S, Vohra R (2003) Combinatorial auctions: a survey. Inform J Comput 15(3):284–309

    Article  Google Scholar 

  • Elmaghraby W, Keskinocak P (2003) Combinatorial auctions in procurement. Int Ser Oper Res Manage Sci 62(Part 3):245–258

    Google Scholar 

  • Eymann T, Reinicke M, Ardaiz O, Artigas P, Freitag F, Navarro L (2003) Decentralized resource allocation in application layer networks. In: Proceedings of the 3rd IEEE/ACM international symposium on cluster computing and the grid, pp 645–650

  • Foster I, Kesselman C, Tuecke S (2001) The anatomy of the grid: enabling scalable virtual organizations. Int J High Perform Comput Appl 15(3):200

    Article  Google Scholar 

  • Fujishima Y, Leyton-Brown K, Shoham Y (1999) Taming the computational complexity of combinatorial auctions: optimal and approximate approaches. In: Proceedings of the sixteenth international joint conference on artificial intelligence, pp 548–553

  • Grosu D, Das A (2004) Auction-based resource allocation protocols in grids. In: Proceedings of the 16th IASTED international conference on parallel and distributed computing and systems, pp 20–27

  • He M, Leung HF, Jennings N (2003) A fuzzy-logic based bidding strategy for autonomous agents in continuous double auctions. IEEE Trans Knowl Data Eng 15(6):1345–1363

    Article  Google Scholar 

  • Holland A, O’Sullivan B (2007) Truthful risk-managed combinatorial auctions. In: Proceedings of the twentieth international joint conference on artificial intelligence, pp 1315–1320

  • Hoos HH, Boutilier C (2000) Solving combinatorial auctions using stochastic local search. In: Proceedings of the seventeenth national conference on artificial intelligence, pp 22–29

  • Kale L, Kumar S, Potnuru M, DeSouza J, Bandhakavi S (2004) Faucets: efficient resource allocation on the computational grid. In: Proceedings of the 2004 international conference on parallel processing, pp 396–405

  • Kant U, Grosu D (2005) Double auction protocols for resource allocation in grids. Proc Int Conf Inform Technol Coding Comput 1:366–371

    Google Scholar 

  • Krishna V (2002) Auction theory. Academic Press, New York

  • Lavi R, Mu’alem A, Nisan N (2003) Towards a characterization of truthful combinatorial auctions. In: Proceedings of the 44th annual IEEE symposium on foundations of computer science. IEEE Computer Society, Washington, DC

  • Lehmann D, O’callaghan LI, Shoham Y (1999) Truth revelation in rapid, approximately efficient combinatorial auctions. In: Proceedings of the ACM conference on electronic commerce, pp 96–102

  • Ma H, Leung HF (2007) Adaptive agents for sequential english auctions with a fixed deadline. In: Proceedings of the 9th IEEE international conference on E-commerce technology and the 4th IEEE international conference on enterprise computing, E-commerce, and E-services, pp 29–38

  • Ma H, Leung HF (2008) Bidding strategies in agent-based continuous double auctions. Birkhäuser, Basel

  • Nisan N (2000) Bidding and allocation in combinatorial auctions. In: Proceedings of the 2nd ACM conference on electronic commerce, EC ’00. ACM, New York, pp 1–12

  • Nisan N, Ronen A (2000) Computationally feasible vcg mechanisms. In: Proceedings of the 2nd ACM conference on electronic commerce. ACM Press, New York, pp 242–252

  • Parkes D, Ungar L (2000) Iterative combinatorial auctions: theory and practice. In: Proceedings of the national conference on artificial intelligence, pp 74–81

  • Rassenti S, Smith V, Bulfin R (1982) A combinatorial auction mechanism for airport time slot allocation. Bell J Econ 13(2):402–417

    Article  Google Scholar 

  • Rastegari B, Condon A, Leyton-Brown K (2009) Stepwise randomized combinatorial auctions achieve revenue monotonicity. In: Proceedings of the nineteenth annual ACM–SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, Philadelphia, pp 738–747

  • Rothkopf M, Pekec A, Harstad R (1998) Computationally manageable combinational auctions. Manage Sci 44(8):1131–1147

    Article  MATH  Google Scholar 

  • Sandholm T (1993) An implementation of the contract net protocol based on marginal cost calculations. In: Proceedings of the national conference on artificial intelligence, pp 256–262

  • Sandholm T (2002) Algorithm for optimal winner determination in combinatorial auctions. Artif Intell Eng Des Anal Manuf 135(1–2):1–54

    MathSciNet  MATH  Google Scholar 

  • Sandholm T, Suri S, Gilpin A, Levine D (2005) CABOB: a fast optimal algorithm for winner determination in combinatorial auctions. Manage Sci 51(3):374–390

    Article  Google Scholar 

  • Schwind M, Stockheim T, Gujo O (2006) Agents’ bidding strategies in a combinatorial auction controlled grid environment. In: TADA/AMEC, pp 149–163

  • Smith R, Davis R (1983) Negotiation as a metaphor for distributed problem solving. Artif Intell Eng Des Anal Manuf 20(1):63–109

    Google Scholar 

  • Sureka A, Wurman PR (2005) Applying metaheuristic techniques to search the space of bidding strategies in combinatorial auctions. In: Proceedings of the 2005 conference on genetic and evolutionary computation. ACM, New York, pp 2097–2103

  • Wilenius J, Andersson, A (2007) Discovering equilibrium strategies for a combinatorial first price auction. In: 9th IEEE international conference on E-commerce technology (CEC 2007)/4th IEEE international conference on enterprise computing, E-commerce and E-services (EEE 2007), 23–26 July 2007, National Center of Sciences, Tokyo, Japan, IEEE Computer Society, pp 13–20

  • Xiao L, Zhu Y, Ni L, Xu Z (2005) Gridis: an incentive-based grid scheduling. In: Proceedings of the 19th IEEE international parallel and distributed processing symposium, pp 65b–65b

  • Yokoo M, Matsutani T, Iwasaki A (2006) False-name-proof combinatorial auction protocol: groves mechanism with submodular approximation. In: Proceedings of the 5th international joint conference on autonomous agents and multiagent systems, pp 1135–1142

  • Zurel E, Nisan N (2001) An efficient approximate allocation algorithm for combinatorial auctions. In: Proceedings of the 3rd ACM conference on electronic commerce. ACM, New York, pp 125–136

Download references

Author information

Author notes

  1. Xin Sui

    Present address: Department of Computer Science, University of Toronto, Toronto, ON, M5S 3G4, Canada

Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Xin Sui & Ho-fung Leung

Authors
  1. Xin Sui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ho-fung Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ho-fung Leung.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sui, X., Leung, Hf. Adaptive bidding in combinatorial auctions in dynamic markets. Evolving Systems 2, 219–233 (2011). https://doi.org/10.1007/s12530-011-9035-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-011-9035-0

Keywords

Search

Navigation