Predicting bidders' willingness to pay in online multiunit ascending auctions: Analytical and empirical insights

Ravi Bapna, Paulo Goes, Alok Gupta, Gilbert Karuga

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


we develop a real-time estimation approach to predict bidders' maximum willingness to pay in a multiunit ascending uniform-price and discriminatory-price (Yankee) online auction. Our two-stage approach begins with a bidder classification step, which is followed by an analytical prediction model. The classification model identifies bidders as either adopting a myopic best-response (MBR) bidding strategy or a non-MBR strategy. We then use a generalized bid-inversion function to estimate the willingness to pay for MBR bidders. We empirically validate our two-stage approach using data from two popular online auction sites. Our joint classification-and- prediction approach outperforms two other naive prediction strategies that draw random valuations between a bidder's current bid and the known market upper bound. Our prediction results indicate that, on average, our estimates are within 2% of bidders' revealed willingness to pay for Yankee and uniform-price multiunit auctions. We discuss how our results can facilitate mechanism-design changes such as dynamic-bid increments and dynamic buy-it-now prices.

Original languageEnglish (US)
Pages (from-to)345-355
Number of pages11
JournalINFORMS Journal on Computing
Issue number3
StatePublished - 2008


  • Dynamic-mechanism design
  • Online auctions
  • Predicting willingness to pay

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research


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