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

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INFORMS Journal on Computing, ISSN: 1091-9856, Vol: 20, Issue: 3, Page: 345-355

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Citations 17
Citation Indexes 17
Ravi Bapna; Paulo Goes; Alok Gupta; Gilbert Karuga
Computer Science; Decision Sciences
article description
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. © 2008 INFORMS.