• Media type: E-Article
  • Title: Incentive-Compatible Learning of Reserve Prices for Repeated Auctions
  • Contributor: Kanoria, Yash; Nazerzadeh, Hamid
  • Published: Institute for Operations Research and the Management Sciences (INFORMS), 2021
  • Published in: Operations Research, 69 (2021) 2, Seite 509-524
  • Language: English
  • DOI: 10.1287/opre.2020.2007
  • ISSN: 0030-364X; 1526-5463
  • Keywords: Management Science and Operations Research ; Computer Science Applications
  • Origination:
  • Footnote:
  • Description: <jats:p> How can an auctioneer optimize revenue by learning the reserve prices from the bids in the previous auctions? How should the long-term incentives and strategic behavior of the bidders be taken into account? Motivated in part by applications in online advertising, in “Incentive-Compatible Learning of Reserve Prices for Repeated Auctions,” Kanoria and Nazerzadeh investigate these questions. They show that if a seller attempts to dynamically update a common reserve price using the bidding history, buyers will shade their bids, which can hurt the revenue. However, when there is more than one buyer, using personalized reserve prices, the auctioneer can achieve a near-optimal revenue. In their proposed mechanism, the personal reserve price for each buyer is determined using the historical bids of other buyers. </jats:p>