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Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge

Department of Computer Sciences, University of Texas at Austin, Austin, Texas 78712
Department of Computer Sciences, University of Texas at Austin, Austin, Texas 78712
McCombs School of Business, University of Texas at Austin, Austin, Texas 78712
McCombs School of Business, University of Texas at Austin, Austin, Texas 78712
Yahoo.com, Santa Clara, California 95954
dpardoe{at}cs.utexas.edu
pstone{at}cs.utexas.edu
maytal{at}mail.utexas.edu
tayfun.keskin{at}mccombs.utexas.edu
kerem{at}yahoo.com

Electronic auction markets are economic information systems that facilitate transactions between buyers and sellers. Whereas auction design has traditionally been an analytic process that relies on theory-driven assumptions such as bidders' rationality, bidders often exhibit unknown and variable behaviors. In this paper we present a data-driven adaptive auction mechanism that capitalizes on key properties of electronic auction markets, such as the large transaction volume, access to information, and the ability to dynamically alter the mechanism's design to acquire information about the benefits from different designs and adapt the auction mechanism online in response to actual bidders' behaviors. Our auction mechanism does not require an explicit representation of bidder behavior to infer about design profitability—a key limitation of prior approaches when they address complex auction settings. Our adaptive mechanism can also incorporate prior general knowledge of bidder behavior to enhance the search for effective designs. The data-driven adaptation and the capacity to use prior knowledge render our mechanisms particularly useful when there is uncertainty regarding bidders' behaviors or when bidders' behaviors change over time. Extensive empirical evaluations demonstrate that the adaptive mechanism outperforms any single fixed mechanism design under a variety of settings, including when bidders' strategies evolve in response to the seller's adaptation; our mechanism's performance is also more robust than that of alternatives when prior general information about bidders' behaviors differs from the encountered behaviors.

Key words: auction design; online learning; data-driven modeling; information acquisition
History: received March 2008; revised May 2009; accepted June 2009.