Stochastic learning of strategic equilibria for auctions. Neural Computation

Departement d'Informatique et Recherche Operationnelle, Universite deMontreal 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.