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Abstract: 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 confirm the correctness and usefulness of the approach. (Update)
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BibTeX entry: (Update)
@article{ bengio99stochastic,
author = "Samy Bengio and Yoshua Bengio and Jacques Robert and Gilles Belanger",
title = "Stochastic Learning of Strategic Equilibria for Auctions",
journal = "Neural Computation",
volume = "11",
number = "5",
pages = "1199-1209",
year = "1999",
url = "citeseer.ist.psu.edu/bengio99stochastic.html" }
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