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Dynamic Reserve Prices for Repeated Auctions: Learning from Bids

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  • Yash Kanoria
  • Hamid Nazerzadeh

Abstract

A large fraction of online advertisement is sold via repeated second price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: Can changing the reserve prices based on the previous bids improve the revenue of the auction, taking into account the long-term incentives and strategic behavior of the bidders? We show that if the distribution of the valuations is known and satisfies the standard regularity assumptions, then the optimal mechanism has a constant reserve. However, when there is uncertainty in the distribution of the valuations, previous bids can be used to learn the distribution of the valuations and to update the reserve price. We present a simple, approximately incentive-compatible, and asymptotically optimal dynamic reserve mechanism that can significantly improve the revenue over the best static reserve. The paper is from July 2014 (our submission to WINE 2014), posted later here on the arxiv to complement the 1-page abstract in the WINE 2014 proceedings.

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  • Yash Kanoria & Hamid Nazerzadeh, 2020. "Dynamic Reserve Prices for Repeated Auctions: Learning from Bids," Papers 2002.07331, arXiv.org.
    • Handle: RePEc:arx:papers:2002.07331
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    Cited by:

    1. Dirk Bergemann & Alessandro Bonatti & Nicholas Wu, 2023. "Managed Campaigns and Data-Augmented Auctions for Digital Advertising," Cowles Foundation Discussion Papers 2359, Cowles Foundation for Research in Economics, Yale University.
    2. Ilan Lobel, 2021. "Revenue Management and the Rise of the Algorithmic Economy," Management Science, INFORMS, vol. 67(9), pages 5389-5398, September.
    3. Dirk Bergemann & Alessandro Bonatti & Nicholas Wu, 2023. "How Do Digital Advertising Auctions Impact Product Prices?," Papers 2304.08432, arXiv.org, revised Apr 2024.
    4. Dragos Florin Ciocan & Krishnamurthy Iyer, 2021. "Tractable Equilibria in Sponsored Search with Endogenous Budgets," Operations Research, INFORMS, vol. 69(1), pages 227-244, January.

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