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

Bapna, R and Goes, P and Gupta, A and Karuga, G (2008) Predicting bidders' willingness to pay in online multiunit ascending auctions: Analytical and empirical insights. INFORMS Journal on Computing, 20 (3). pp. 345-355.

[img]
Preview
(Publised version - as permitted publishers)
| Preview

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 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.

ISB Creators:
ISB CreatorsORCiD
Bapna, RUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Dynamic-mechanism design; Online auctions; Predicting willingness to pay
Subjects: Business and Management
Depositing User: Veeramani R
Date Deposited: 01 Nov 2014 14:48
Last Modified: 05 Nov 2014 18:32
URI: http://eprints.exchange.isb.edu/id/eprint/115
Publisher URL: http://dx.doi.org/10.1287/ijoc.1070.0247
Publisher OA policy: http://www.sherpa.ac.uk/romeo/issn/1091-9856/
Related URLs:

Actions (login required)

View Item View Item
Statistics for DESI ePrint 115 Statistics for this ePrint Item