Optimizing Coordination among Bounded Rational Agents

Zhewei Wang, Marcos M. Vasconcelos
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:953-964, 2026.

Abstract

Coordination is a desirable feature in many multi-agent systems, such as robotic and socioeconomic networks. Traditionally, these agents are assumed to be perfectly rational, i.e., they are designed and trained to maximize their expected utilities. However, in many situations, perfectly rational behavior is not possible. We consider a binary networked coordination game over a weighted undirected regular graph with a sparsity constraint. Each agent exhibits bounded rationality and employs a distributed stochastic learning algorithm known as {\it Log Linear Learning} to update its action conditioned on the actions currently played by its neighbors. We optimize the probability that the multi-agent system will converge to a pure Nash equilibria of the game with respect to the graph weights. We provide analytical and numerical results for specific sparsity patterns considered in a classical behavioral economics experiment from Leavitt and Bavellas (1951).

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-wang26b, title = {Optimizing Coordination among Bounded Rational Agents}, author = {Wang, Zhewei and Vasconcelos, Marcos M.}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {953--964}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/wang26b/wang26b.pdf}, url = {https://proceedings.mlr.press/v331/wang26b.html}, abstract = {Coordination is a desirable feature in many multi-agent systems, such as robotic and socioeconomic networks. Traditionally, these agents are assumed to be perfectly rational, i.e., they are designed and trained to maximize their expected utilities. However, in many situations, perfectly rational behavior is not possible. We consider a binary networked coordination game over a weighted undirected regular graph with a sparsity constraint. Each agent exhibits bounded rationality and employs a distributed stochastic learning algorithm known as {\it Log Linear Learning} to update its action conditioned on the actions currently played by its neighbors. We optimize the probability that the multi-agent system will converge to a pure Nash equilibria of the game with respect to the graph weights. We provide analytical and numerical results for specific sparsity patterns considered in a classical behavioral economics experiment from Leavitt and Bavellas (1951).} }
Endnote
%0 Conference Paper %T Optimizing Coordination among Bounded Rational Agents %A Zhewei Wang %A Marcos M. Vasconcelos %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-wang26b %I PMLR %P 953--964 %U https://proceedings.mlr.press/v331/wang26b.html %V 331 %X Coordination is a desirable feature in many multi-agent systems, such as robotic and socioeconomic networks. Traditionally, these agents are assumed to be perfectly rational, i.e., they are designed and trained to maximize their expected utilities. However, in many situations, perfectly rational behavior is not possible. We consider a binary networked coordination game over a weighted undirected regular graph with a sparsity constraint. Each agent exhibits bounded rationality and employs a distributed stochastic learning algorithm known as {\it Log Linear Learning} to update its action conditioned on the actions currently played by its neighbors. We optimize the probability that the multi-agent system will converge to a pure Nash equilibria of the game with respect to the graph weights. We provide analytical and numerical results for specific sparsity patterns considered in a classical behavioral economics experiment from Leavitt and Bavellas (1951).
APA
Wang, Z. & Vasconcelos, M.M.. (2026). Optimizing Coordination among Bounded Rational Agents. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:953-964 Available from https://proceedings.mlr.press/v331/wang26b.html.

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