A Game-Theoretic Framework for Network Formation in Large Populations
Abstract
In this paper, we study a model of network formation in large populations. Each agent can choose the strength of interaction (i.e. connection) with other agents to find a Nash equilibrium. Different from the recently-developed theory of graphon games, here each agent's control depends not only on her own index but also on the index of other agents. After defining the general model of the game, we focus on a special case with piecewise constant graphs and we provide optimality conditions through a system of forward-backward stochastic differential equations. Furthermore, we show the uniqueness and existence results. Finally, we provide numerical experiments to discuss the effects of different model settings.
Cite
@article{arxiv.2508.03847,
title = {A Game-Theoretic Framework for Network Formation in Large Populations},
author = {Gokce Dayanikli and Mathieu Lauriere},
journal= {arXiv preprint arXiv:2508.03847},
year = {2025}
}
Comments
Accepted at 2025 IEEE Conference on Control and Decision (CDC)