Surrogate Bayesian Networks for Approximating Evolutionary Games

Vincent Hsiao, Dana S Nau, Bobak Pezeshki, Rina Dechter
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:2566-2574, 2024.

Abstract

Spatial evolutionary games are used to model large systems of interacting agents. In earlier work, a method was developed using Bayesian Networks to approximate the population dynamics in these games. One of the advantages of the Bayesian Network modeling approach is that it is possible to smoothly adjust the size of the network to get more accurate approximations. However, scaling the method up can be intractable if the number of strategies in the evolutionary game increases. In this paper, we propose a new method for computing more accurate approximations by using surrogate Bayesian Networks. Instead of computing inference on larger networks directly, we perform inference on a much smaller surrogate network extended with parameters that exploit the symmetry inherent to the domain. We learn the parameters on the surrogate network using KL-divergence as the loss function. We illustrate the value of this method empirically through a comparison on several evolutionary games.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-hsiao24a, title = { Surrogate {B}ayesian Networks for Approximating Evolutionary Games }, author = {Hsiao, Vincent and S Nau, Dana and Pezeshki, Bobak and Dechter, Rina}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {2566--2574}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/hsiao24a/hsiao24a.pdf}, url = {https://proceedings.mlr.press/v238/hsiao24a.html}, abstract = { Spatial evolutionary games are used to model large systems of interacting agents. In earlier work, a method was developed using Bayesian Networks to approximate the population dynamics in these games. One of the advantages of the Bayesian Network modeling approach is that it is possible to smoothly adjust the size of the network to get more accurate approximations. However, scaling the method up can be intractable if the number of strategies in the evolutionary game increases. In this paper, we propose a new method for computing more accurate approximations by using surrogate Bayesian Networks. Instead of computing inference on larger networks directly, we perform inference on a much smaller surrogate network extended with parameters that exploit the symmetry inherent to the domain. We learn the parameters on the surrogate network using KL-divergence as the loss function. We illustrate the value of this method empirically through a comparison on several evolutionary games. } }
Endnote
%0 Conference Paper %T Surrogate Bayesian Networks for Approximating Evolutionary Games %A Vincent Hsiao %A Dana S Nau %A Bobak Pezeshki %A Rina Dechter %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-hsiao24a %I PMLR %P 2566--2574 %U https://proceedings.mlr.press/v238/hsiao24a.html %V 238 %X Spatial evolutionary games are used to model large systems of interacting agents. In earlier work, a method was developed using Bayesian Networks to approximate the population dynamics in these games. One of the advantages of the Bayesian Network modeling approach is that it is possible to smoothly adjust the size of the network to get more accurate approximations. However, scaling the method up can be intractable if the number of strategies in the evolutionary game increases. In this paper, we propose a new method for computing more accurate approximations by using surrogate Bayesian Networks. Instead of computing inference on larger networks directly, we perform inference on a much smaller surrogate network extended with parameters that exploit the symmetry inherent to the domain. We learn the parameters on the surrogate network using KL-divergence as the loss function. We illustrate the value of this method empirically through a comparison on several evolutionary games.
APA
Hsiao, V., S Nau, D., Pezeshki, B. & Dechter, R.. (2024). Surrogate Bayesian Networks for Approximating Evolutionary Games . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:2566-2574 Available from https://proceedings.mlr.press/v238/hsiao24a.html.

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