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.

翻译：我们考虑网络博弈，其中大量代理根据随机网络模型进行交互，该模型由图状函数表示。通过利用以前的大型网络博弈收敛于图状博弈的结果，我们研究了一种从均衡行动的观察中估计未知收益参数的过程，而无需精确的网络信息。我们证明了涉及计算所提议的估计器的优化问题的平滑性和局部凸性。此外，在图状参数可识别性的概念下，我们证明了最优估计器的全局唯一性。我们提供了几个可识别的同质和异质参数的示例，这些参数属于不同类的线性二次网络博弈，并提供了数值模拟来验证所提议的估计器。