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Competitive Bidding with Dependent Value Estimates

Author

Listed:
  • Robert L. Winkler

    (Indiana University, Bloomington, Indiana)

  • Daniel G. Brooks

    (Arizona State University, Tempe, Arizona)

Abstract

A bidding situation in which there is uncertainty about the value of the item of interest is modeled. The uncertainty is modeled in probabilistic terms, and the model allows the errors of estimation (the differences between expected values and the actual value) of the bidders to be dependent. The effect of this dependence on the “winner's curse” (the tendency for the highest bidder to be one who has overvalued the item) is studied, and optimal bidding strategies are determined.

Suggested Citation

  • Robert L. Winkler & Daniel G. Brooks, 1980. "Competitive Bidding with Dependent Value Estimates," Operations Research, INFORMS, vol. 28(3-part-i), pages 603-613, June.
    • Handle: RePEc:inm:oropre:v:28:y:1980:i:3-part-i:p:603-613
    DOI: 10.1287/opre.28.3.603
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    Cited by:

    1. Saurabh Bansal & James S. Dyer, 2017. "Technical Note—Multivariate Partial-Expectation Results for Exact Solutions of Two-Stage Problems," Operations Research, INFORMS, vol. 65(6), pages 1526-1534, December.
    2. Javier Castro & Rosa Espínola & Inmaculada Gutiérrez & Daniel Gómez, 2023. "Auctions: A New Method for Selling Objects with Bimodal Density Functions," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1707-1743, April.
    3. Anil Gaba & Ilia Tsetlin & Robert L. Winkler, 2004. "Modifying Variability and Correlations in Winner-Take-All Contests," Operations Research, INFORMS, vol. 52(3), pages 384-395, June.
    4. Max H. Bazerman & William F. Samuelson, 1983. "I Won the Auction But Don't Want the Prize," Journal of Conflict Resolution, Peace Science Society (International), vol. 27(4), pages 618-634, December.
    5. Ashish Arora & Amy Greenwald & Karthik Kannan & Ramayya Krishnan, 2007. "Effects of Information-Revelation Policies Under Market-Structure Uncertainty," Management Science, INFORMS, vol. 53(8), pages 1234-1248, August.
    6. Lorentziadis, Panos L., 2012. "Optimal bidding in auctions of mixed populations of bidders," European Journal of Operational Research, Elsevier, vol. 217(3), pages 653-663.
    7. Lorentziadis, Panos L., 2016. "Optimal bidding in auctions from a game theory perspective," European Journal of Operational Research, Elsevier, vol. 248(2), pages 347-371.
    8. Harstad, Ronald M. & Pekec, Aleksandar Sasa & Tsetlin, Ilia, 2008. "Information aggregation in auctions with an unknown number of bidders," Games and Economic Behavior, Elsevier, vol. 62(2), pages 476-508, March.
    9. Aleksandar Saša Pekev{c} & Ilia Tsetlin, 2008. "Revenue Ranking of Discriminatory and Uniform Auctions with an Unknown Number of Bidders," Management Science, INFORMS, vol. 54(9), pages 1610-1623, September.
    10. Mordechai E. Schwarz, 2021. "Auctions with endogenous opting‐out fees and recursive winning procedures from the Talmud," International Journal of Economic Theory, The International Society for Economic Theory, vol. 17(4), pages 345-374, December.
    11. James E. Smith & Robert L. Winkler, 2006. "The Optimizer's Curse: Skepticism and Postdecision Surprise in Decision Analysis," Management Science, INFORMS, vol. 52(3), pages 311-322, March.
    12. 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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