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Coalition-Based Pricing in Ascending Combinatorial Auctions

Author

Listed:
  • Martin Bichler

    (Department of Informatics, Technical University of Munich, D-85748 Garching, Germany)

  • Zhen Hao

    (Department of Informatics, Technical University of Munich, D-85748 Garching, Germany)

  • Gediminas Adomavicius

    (Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

Abstract

Bidders in larger ascending combinatorial auctions face a substantial coordination problem, which has received little attention in the literature. The coordination problem manifests itself by the fact that losing bidders need to submit nonoverlapping package bids that are high enough to outbid the standing winners. We propose an auction format, which leverages the information that the auctioneer collects throughout the auction about the preferences of individual bidders and suggests prices for the members of losing bidder coalitions, which in total would make a given coalition winning. We model the bidder’s bundle selection problem as a coordination game, which provides a theoretical rationale for bidders to agree to these prices, and highlights the role of the auctioneer in providing relevant information feedback. Results of extensive numerical simulations and experiments with human participants demonstrate that this type of pricing substantially reduces the number of auction rounds and bids necessary to find a competitive equilibrium, and at the same time significantly increases auction efficiency in the lab. This rapid convergence is crucial for the practical viability of combinatorial auctions in larger markets.

Suggested Citation

  • Martin Bichler & Zhen Hao & Gediminas Adomavicius, 2017. "Coalition-Based Pricing in Ascending Combinatorial Auctions," Information Systems Research, INFORMS, vol. 28(1), pages 159-179, March.
    • Handle: RePEc:inm:orisre:v:28:y:2017:i:1:p:159-179
    DOI: 10.1287/isre.2016.0681
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    References listed on IDEAS

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    Cited by:

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    4. Gediminas Adomavicius & Shawn P. Curley & Alok Gupta & Pallab Sanyal, 2020. "How Decision Complexity Affects Outcomes in Combinatorial Auctions," Production and Operations Management, Production and Operations Management Society, vol. 29(11), pages 2579-2600, November.
    5. Fugger, Nicolas & Gillen, Philippe & Rasch, Alexander & Zeppenfeld, Christopher, 2016. "Preferences and Decision Support in Competitive Bidding," VfS Annual Conference 2016 (Augsburg): Demographic Change 145849, Verein für Socialpolitik / German Economic Association.
    6. Martin Bichler & Paul Milgrom & Gregor Schwarz, 2023. "Taming the Communication and Computation Complexity of Combinatorial Auctions: The FUEL Bid Language," Management Science, INFORMS, vol. 69(4), pages 2217-2238, April.
    7. De Liu & Adib Bagh, 2020. "Preserving Bidder Privacy in Assignment Auctions: Design and Measurement," Management Science, INFORMS, vol. 66(7), pages 3162-3182, July.
    8. Gediminas Adomavicius & Alok Gupta & Mochen Yang, 2022. "Bidder Support in Multi-item Multi-unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework," Information Systems Research, INFORMS, vol. 33(4), pages 1174-1195, December.
    9. Bart Vangerven & Dries R. Goossens & Frits C. R. Spieksma, 2021. "Using Feedback to Mitigate Coordination and Threshold Problems in Iterative Combinatorial Auctions," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(2), pages 113-127, April.

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