Predicting Bidders' Willingness to Pay in Online Multiunit Ascending Auctions: Analytical and Empirical Insights

  • Authors:

  • Ravi Bapna;Paulo Goes;Alok Gupta;Gilbert Karuga

  • Affiliations:

  • Centre for Information Technology and the Networked Economy, Indian School of Business, Hyderabad - 500 032, India;Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269;Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455;Accounting and Information Systems, School of Business, University of Kansas, Lawrence, Kansas 66045

  • Venue:
  • INFORMS Journal on Computing
  • Year:
  • 2008

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Abstract

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 naïve 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.