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Designing Intelligent Software Agents for Auctions with Limited Information Feedback

Author

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  • Gediminas Adomavicius

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

  • Alok Gupta

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

  • Dmitry Zhdanov

    (School of Business, University of Connecticut, Storrs, Connecticut 06269)

Abstract

This paper presents analytical, computational, and empirical analyses of strategies for intelligent bid formulations in online auctions. We present results related to a weighted-average ascending price auction mechanism that is designed to provide opaque feedback information to bidders and presents a challenge in formulating appropriate bids. Using limited information provided by the mechanism, we design strategies for software agents to make bids intelligently. In particular, we derive analytical results for the important characteristics of the auction, which allow estimation of the key parameters; we then use these theoretical results to design several bidding strategies. We demonstrate the validity of designed strategies using a discrete event simulation model that resembles the mechanisms used in treasury bills auctions, business-to-consumer (B2C) auctions, and auctions for environmental emission allowances. In addition, using the data generated by the simulation model, we show that intelligent strategies can provide a high probability of winning an auction without significant loss in surplus.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orisre:v:20:y:2009:i:4:p:507-526
    DOI: 10.1287/isre.1080.0172
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    References listed on IDEAS

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    Cited by:

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    2. Wolfgang Ketter & Karsten Schroer & Konstantina Valogianni, 2023. "Information Systems Research for Smart Sustainable Mobility: A Framework and Call for Action," Information Systems Research, INFORMS, vol. 34(3), pages 1045-1065, September.
    3. Jing Wang & Panagiotis G. Ipeirotis & Foster Provost, 2017. "Cost-Effective Quality Assurance in Crowd Labeling," Information Systems Research, INFORMS, vol. 28(1), pages 137-158, March.
    4. Ashraf Labib & Salem Chakhar & Lorraine Hope & John Shimell & Mark Malinowski, 2022. "Analysis of noise and bias errors in intelligence information systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1755-1775, December.
    5. Micha Kahlen & Karsten Schroer & Wolfgang Ketter & Alok Gupta, 2024. "Smart Markets for Real-Time Allocation of Multiproduct Resources: The Case of Shared Electric Vehicles," Information Systems Research, INFORMS, vol. 35(2), pages 871-889, June.
    6. Martin Bichler & Alok Gupta & Wolfgang Ketter, 2010. "Research Commentary ---Designing Smart Markets," Information Systems Research, INFORMS, vol. 21(4), pages 688-699, December.
    7. Daniel Montanera & Abhay Nath Mishra & T. S. Raghu, 2022. "Mitigating Risk Selection in Healthcare Entitlement Programs: A Beneficiary-Level Competitive Bidding Approach," Information Systems Research, INFORMS, vol. 33(4), pages 1221-1247, December.
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    9. Karthik N. Kannan, 2012. "Effects of Information Revelation Policies Under Cost Uncertainty," Information Systems Research, INFORMS, vol. 23(1), pages 75-92, March.

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