Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

July 1, 1999, Vol. 11, No. 5, Pages 1199-1209
(doi: 10.1162/089976699300016412)
© 1999 Massachusetts Institute of Technology
Stochastic Learning of Strategic Equilibria for Auctions
Article PDF (56.97 KB)
Abstract

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.