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Effects of Information Revelation Policies Under Cost Uncertainty

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  • Karthik N. Kannan

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47907)

Abstract

The paper presents insights regarding the key learning-related factors a buyer should consider when deciding the extent to which information about bids is revealed in a procurement auction context. It offers the insights by analyzing the following two first-price sealed-bid policies in a private-value sequential auction with no winner dropouts: (i) iis , where only the winner's bid is revealed, and (ii) cis , where all bids are revealed. Our analysis identifies two important learning effects---the extraction and the deception effects---as having significant welfare implications. Both these effects arise because of a bidder's desire to gain an informational advantage relative to his competitors, but their manifestations are different. The extraction effect occurs because of a bidder's incentive to learn about his competitors, and the deception effect is a consequence of the incentive to prevent an opponent from gaining the information. Both effects lead to higher bid prices, and either may be dominant from a procurer surplus standpoint. With the deception effect, social welfare can decrease even when the number of suppliers increases, a result that is counterintuitive. The paper also discusses how insights regarding the learning effects might apply to other policies.

Suggested Citation

  • Karthik N. Kannan, 2012. "Effects of Information Revelation Policies Under Cost Uncertainty," Information Systems Research, INFORMS, vol. 23(1), pages 75-92, March.
    • Handle: RePEc:inm:orisre:v:23:y:2012:i:1:p:75-92
    DOI: 10.1287/isre.1100.0292
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    References listed on IDEAS

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    Cited by:

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    4. Chen Liang & Yili Hong & Pei-Yu Chen & Benjamin B. M. Shao, 2022. "The Screening Role of Design Parameters for Service Procurement Auctions in Online Service Outsourcing Platforms," Information Systems Research, INFORMS, vol. 33(4), pages 1324-1343, December.
    5. Vidyanand Choudhary & Shivendu Shivendu, 2017. "Targeted Couponing in Online Auctions," Information Systems Research, INFORMS, vol. 28(3), pages 490-510, September.
    6. Ninoslav Malekovic & Lazaros Goutas & Juliana Sutanto & Dennis Galletta, 2020. "Regret under different auction designs: the case of English and Dutch auctions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(1), pages 151-161, March.
    7. Zhijuan Hong & Ruhai Wu & Yan Sun & Kunxiang Dong, 2020. "Buyer preferences for auction pricing rules in online outsourcing markets: fixed price vs. open price," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(1), pages 163-179, March.
    8. Shuang Xu & Yong Zhao & Yeming Gong, 2021. "Equivalence and revenue comparison among identical-item auctions," Journal of Economics, Springer, vol. 134(3), pages 261-292, December.
    9. Eric Overby & Karthik Kannan, 2015. "How Reduced Search Costs and the Distribution of Bidder Participation Affect Auction Prices," Management Science, INFORMS, vol. 61(6), pages 1398-1420, June.

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