Estimation of Unknown Payoff Parameters in Large Network Games
Abstract
We consider network games where a large number of agents interact according to a network sampled from a random network model, represented by a graphon. By exploiting previous results on convergence of such large network games to graphon games, we examine a procedure for estimating unknown payoff parameters, from observations of equilibrium actions, without the need for exact network information. We prove smoothness and local convexity of the optimization problem involved in computing the proposed estimator. Additionally, under a notion of graphon parameter identifiability, we show that the optimal estimator is globally unique. We present several examples of identifiable homogeneous and heterogeneous parameters in different classes of linear quadratic network games with numerical simulations to validate the proposed estimator.
Cite
@article{arxiv.2303.10262,
title = {Estimation of Unknown Payoff Parameters in Large Network Games},
author = {Feras Al Taha and Francesca Parise},
journal= {arXiv preprint arXiv:2303.10262},
year = {2023}
}
Comments
The extended arXiv version of the original paper accepted for publication in the 2023 American Control Conference, 9 pages, 1 figure