Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games
Abstract
We propose a mean-field approximation that dramatically reduces the computational complexity of solving stochastic dynamic games. We pro- vide conditions that guarantee our method approximates an equilibrium as the number of agents grow. We then derive a performance bound to assess how well the approximation performs for any given number of agents. We apply our method to an important class of problems in ap- plied microeconomics. We show with numerical experiments that we are able to greatly expand the set of economic problems that can be analyzed computationally.
Cite
Text
Weintraub et al. "Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games." Neural Information Processing Systems, 2005.Markdown
[Weintraub et al. "Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/weintraub2005neurips-oblivious/)BibTeX
@inproceedings{weintraub2005neurips-oblivious,
title = {{Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games}},
author = {Weintraub, Gabriel Y. and Benkard, Lanier and Van Roy, Benjamin},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {1489-1496},
url = {https://mlanthology.org/neurips/2005/weintraub2005neurips-oblivious/}
}