Adaptive Auctions: Learning to Adjust to Bidders

Auction mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, bidders behave unpredictably, making them difficult to model and complicating the design process. To address this challenge, we present an adaptive auction mechanism: one that learns 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. The algorithm is fully implemented and tested. Results indicate that the adaptive mechanism is able to outperform any single fixed mechanism.