Stochastic Learning of Strategic Equilibria for Auctions

This paper presents a new application of stochastic adaptive learning algorithms to the computation of strategic equilibria in auctions The proposed approach addresses the problems of tracking a moving target and balancing exploration of action space versus exploitation of better modeled regions of action space Neural networks are used to represent a stochastic decision model for each bidder Experiments con rm the correctness and usefulness of the approach