Learning Game Representations from Data Using Rationality Constraints

Xi Alice Gao, Avi Pfeffer
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:201-208, 2010.

Abstract

While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from? One answer is to learn the representa- tions from data. If one wants to learn both the payoffs and the players’ strategies, a naive approach is to learn them both directly from the data. This approach ignores the fact the players might be playing reasonably good strategies, so there is a connection between the strategies and the data. The main con- tribution of this paper is to make this connec- tion while learning. We formulate the learn- ing problem as a weighted constraint satis- faction problem, including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the pay- offs. We use quantal response equilibrium as our notion of rationality for quantifying the latter fit. Our results show that incorporat- ing rationality constraints can improve learn- ing when the amount of data is limited.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-gao10a, title = {Learning Game Representations from Data Using Rationality Constraints}, author = {Gao, Xi Alice and Pfeffer, Avi}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {201--208}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/gao10a/gao10a.pdf}, url = {https://proceedings.mlr.press/r8/gao10a.html}, abstract = {While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from? One answer is to learn the representa- tions from data. If one wants to learn both the payoffs and the players’ strategies, a naive approach is to learn them both directly from the data. This approach ignores the fact the players might be playing reasonably good strategies, so there is a connection between the strategies and the data. The main con- tribution of this paper is to make this connec- tion while learning. We formulate the learn- ing problem as a weighted constraint satis- faction problem, including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the pay- offs. We use quantal response equilibrium as our notion of rationality for quantifying the latter fit. Our results show that incorporat- ing rationality constraints can improve learn- ing when the amount of data is limited.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning Game Representations from Data Using Rationality Constraints %A Xi Alice Gao %A Avi Pfeffer %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-gao10a %I PMLR %P 201--208 %U https://proceedings.mlr.press/r8/gao10a.html %V R8 %X While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from? One answer is to learn the representa- tions from data. If one wants to learn both the payoffs and the players’ strategies, a naive approach is to learn them both directly from the data. This approach ignores the fact the players might be playing reasonably good strategies, so there is a connection between the strategies and the data. The main con- tribution of this paper is to make this connec- tion while learning. We formulate the learn- ing problem as a weighted constraint satis- faction problem, including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the pay- offs. We use quantal response equilibrium as our notion of rationality for quantifying the latter fit. Our results show that incorporat- ing rationality constraints can improve learn- ing when the amount of data is limited. %Z Reissued by PMLR on 04 October 2026.
APA
Gao, X.A. & Pfeffer, A.. (2010). Learning Game Representations from Data Using Rationality Constraints. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:201-208 Available from https://proceedings.mlr.press/r8/gao10a.html. Reissued by PMLR on 04 October 2026.

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