Stochastic Learning of Strategic Equilibria for Auctions
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.
Cite
Text
Bengio et al. "Stochastic Learning of Strategic Equilibria for Auctions." Neural Computation, 1999. doi:10.1162/089976699300016412Markdown
[Bengio et al. "Stochastic Learning of Strategic Equilibria for Auctions." Neural Computation, 1999.](https://mlanthology.org/neco/1999/bengio1999neco-stochastic/) doi:10.1162/089976699300016412BibTeX
@article{bengio1999neco-stochastic,
title = {{Stochastic Learning of Strategic Equilibria for Auctions}},
author = {Bengio, Samy and Bengio, Yoshua and Robert, Jacques and Bélanger, Gilles},
journal = {Neural Computation},
year = {1999},
pages = {1199-1209},
doi = {10.1162/089976699300016412},
volume = {11},
url = {https://mlanthology.org/neco/1999/bengio1999neco-stochastic/}
}