Efficient Nash Computation in Large Population Games with Bounded Influence
Abstract
We introduce a general representation of large-population games in which each player's influence on the others is centralized and limited, but may otherwise be arbitrary. This representation significantly generalizes the class known as congestion games in a natural way. Our main results are provably correct and efficient algorithms for computing and learning approximate Nash equilibria in this general framework.
Cite
Text
Kearns and Mansour. "Efficient Nash Computation in Large Population Games with Bounded Influence." Conference on Uncertainty in Artificial Intelligence, 2002.Markdown
[Kearns and Mansour. "Efficient Nash Computation in Large Population Games with Bounded Influence." Conference on Uncertainty in Artificial Intelligence, 2002.](https://mlanthology.org/uai/2002/kearns2002uai-efficient/)BibTeX
@inproceedings{kearns2002uai-efficient,
title = {{Efficient Nash Computation in Large Population Games with Bounded Influence}},
author = {Kearns, Michael J. and Mansour, Yishay},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2002},
pages = {259-266},
url = {https://mlanthology.org/uai/2002/kearns2002uai-efficient/}
}