Paper

Stochastic Learning of Strategic Equilibria for Auctions

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.

Authors: Samy Bengio · Yoshua Bengio · Jacques Robert · Gilles Bélanger

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