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(Neural Computation. 1999;11:1199-1209.)
© 1999 The MIT Press

Letter

Stochastic Learning of Strategic Equilibria for Auctions

Samy Bengio

CIRANO, Montréal, Québec, Canada, H3A 2A5

Yoshua Bengio

CIRANO and Département Informatique et Recherche Operationnelle, Université de Montréal, Montréal, Québec, Canada, H3C 3J7

Jacques Robert

CIRANO and Département Sciences Economiques, Université de Montréal, Montréal, Québec, Canada, H3C 3J7

Gilles Bélanger

Département Sciences Economiques, Université de Montréal, Montréal, Québec, Canada, H3C 3J7

This article 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 confirm the correctness and usefulness of the approach.






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