Adaptive Mechanism Design: A Metalearning Approach (2006)
David Pardoe, Peter Stone, Maytal Saar-Tsechansky, and Kerem Tomak
Auction mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, bidders often exhibit unknown and variable behavior, making them difficult to model and complicating the design process. To address this challenge, we explore the use of an adaptive auction mechanism: one that emphlearns to adjust its parameters in response to past empirical bidder behavior so as to maximize an objective function such as auctioneer revenue. In this paper, we give an overview of our general approach and then present an instantiation in a specific auction scenario. In addition, we show how predictions of possible bidder behavior can be incorporated into the adaptive mechanism through a emphmetalearning process. The approach is fully implemented and tested. Results indicate that the adaptive mechanism is able to outperform any single fixed mechanism, and that the addition of metalearning improves performance substantially.
In The Eighth International Conference on Electronic Commerce, pp. 92-102, August 2006.

David Pardoe Ph.D. Alumni dpardoe [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu