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Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions

Author

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  • Gediminas Adomavicius

    (Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, 321 19th Avenue South, Minneapolis, Minnesota 55455)

  • Alok Gupta

    (Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, 321 19th Avenue South, Minneapolis, Minnesota 55455)

Abstract

Many auctions involve selling several distinct items simultaneously, where bidders can bid on the whole or any part of the lot. Such auctions are referred to as combinatorial auctions. Examples of such auctions include truck delivery routes, industrial procurement, and FCC spectrum. Determining winners in such auctions is an NP-hard problem, and significant research is being conducted in this area. However, multiple-round (iterative) combinatorial auctions present significant challenges in bid formulations as well. Because the combinatorial dynamics in iterative auctions can make a given bid part of a winning and nonwinning set of bids without any changes in the bid, bidders are usually not able to evaluate whether they should revise their bid at a given point in time or not. Therefore, in this paper we address various computational problems that are relevant from the bidder's perspective. In particular, we introduce two bid evaluation metrics that can be used by bidders to determine whether any given bid can be a part of the winning allocation and explore their theoretical properties. Based on these metrics, we also develop efficient data structures and algorithms that provide comprehensive information about the current state of the auction at any time, which can help bidders in evaluating their bids and bidding strategies. Our approach uses exponential memory storage but provides fast incremental update for new bids, thereby facilitating bidder support for real-time iterative combinatorial auctions.

Suggested Citation

  • Gediminas Adomavicius & Alok Gupta, 2005. "Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions," Information Systems Research, INFORMS, vol. 16(2), pages 169-185, June.
    • Handle: RePEc:inm:orisre:v:16:y:2005:i:2:p:169-185
    DOI: 10.1287/isre.1050.0052
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    References listed on IDEAS

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    Cited by:

    1. Ioannis Petrakis & Georg Ziegler & Martin Bichler, 2013. "Ascending Combinatorial Auctions with Allocation Constraints: On Game Theoretical and Computational Properties of Generic Pricing Rules," Information Systems Research, INFORMS, vol. 24(3), pages 768-786, September.
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    4. Pallab Sanyal, 2016. "Characteristics and Economic Consequences of Jump Bids in Combinatorial Auctions," Information Systems Research, INFORMS, vol. 27(2), pages 347-364, June.
    5. Martin Bichler & Pasha Shabalin & Alexander Pikovsky, 2009. "A Computational Analysis of Linear Price Iterative Combinatorial Auction Formats," Information Systems Research, INFORMS, vol. 20(1), pages 33-59, March.
    6. Martin Bichler & Pasha Shabalin & Georg Ziegler, 2013. "Efficiency with Linear Prices? A Game-Theoretical and Computational Analysis of the Combinatorial Clock Auction," Information Systems Research, INFORMS, vol. 24(2), pages 394-417, June.
    7. Martin Bichler & Alexander Hammerl & Thayer Morrill & Stefan Waldherr, 2021. "How to Assign Scarce Resources Without Money: Designing Information Systems that are Efficient, Truthful, and (Pretty) Fair," Information Systems Research, INFORMS, vol. 32(2), pages 335-355, June.
    8. Zhiling Guo & Gary J. Koehler & Andrew B. Whinston, 2012. "A Computational Analysis of Bundle Trading Markets Design for Distributed Resource Allocation," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 823-843, September.
    9. Onur Şeref & Ravindra K. Ahuja & James B. Orlin, 2009. "Incremental Network Optimization: Theory and Algorithms," Operations Research, INFORMS, vol. 57(3), pages 586-594, June.
    10. 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.
    11. Abhishek Ray & Mario Ventresca & Karthik Kannan, 2021. "A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions," Information Systems Research, INFORMS, vol. 32(4), pages 1099-1114, December.
    12. 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.
    13. Vangerven, Bart & Goossens, Dries R. & Spieksma, Frits C.R., 2017. "Winner determination in geometrical combinatorial auctions," European Journal of Operational Research, Elsevier, vol. 258(1), pages 254-263.
    14. Soumyakanti Chakraborty & Anup K. Sen & Amitava Bagchi, 2015. "Addressing the valuation problem in multi-round combinatorial auctions," Information Systems Frontiers, Springer, vol. 17(5), pages 1145-1160, October.
    15. De Liu & Adib Bagh, 2020. "Preserving Bidder Privacy in Assignment Auctions: Design and Measurement," Management Science, INFORMS, vol. 66(7), pages 3162-3182, July.
    16. Martin Bichler & Alok Gupta & Wolfgang Ketter, 2010. "Research Commentary ---Designing Smart Markets," Information Systems Research, INFORMS, vol. 21(4), pages 688-699, December.
    17. Martin Bichler & Vladimir Fux & Jacob Goeree, 2018. "A Matter of Equality: Linear Pricing in Combinatorial Exchanges," Information Systems Research, INFORMS, vol. 29(4), pages 1024-1043, December.
    18. Gediminas Adomavicius & Shawn P. Curley & Alok Gupta & Pallab Sanyal, 2012. "Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions," Management Science, INFORMS, vol. 58(4), pages 811-830, April.
    19. Soumyakanti Chakraborty & Anup K. Sen & Amitava Bagchi, 2015. "Combinatorial Auctions for Player Selection in the Indian Premier League (IPL)," Journal of Sports Economics, , vol. 16(1), pages 86-107, January.
    20. Tobias Scheffel & Alexander Pikovsky & Martin Bichler & Kemal Guler, 2011. "An Experimental Comparison of Linear and Nonlinear Price Combinatorial Auctions," Information Systems Research, INFORMS, vol. 22(2), pages 346-368, June.
    21. 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.
    22. 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.
    23. Gediminas Adomavicius & Alok Gupta & Dmitry Zhdanov, 2009. "Designing Intelligent Software Agents for Auctions with Limited Information Feedback," Information Systems Research, INFORMS, vol. 20(4), pages 507-526, December.

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