TY - JOUR
T1 - Predicting bidders' willingness to pay in online multiunit ascending auctions
T2 - Analytical and empirical insights
AU - Bapna, Ravi
AU - Goes, Paulo
AU - Gupta, Alok
AU - Karuga, Gilbert
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Dynamic-mechanism design
KW - Online auctions
KW - Predicting willingness to pay
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U2 - 10.1287/ijoc.1070.0247
DO - 10.1287/ijoc.1070.0247
M3 - Article
AN - SCOPUS:61349199461
SN - 1091-9856
VL - 20
SP - 345
EP - 355
JO - INFORMS Journal on Computing
JF - INFORMS Journal on Computing
IS - 3
ER -