A central question in multi-agent strategic games deals with learning the underlying utilities driving the agents' behaviour. Motivated by the increasing availability of large data-sets, we develop an unifying data-driven technique to estimate agents' utility functions from their observed behaviour, irrespective of whether the observations correspond to equilibrium configurations or to temporal sequences of action profiles. Under standard assumptions on the parametrization of the utilities, the proposed inference method is computationally efficient and finds all the parameters that rationalize the observed behaviour best. We numerically validate our theoretical findings on the market share estimation problem under advertising competition, using historical data from the Coca-Cola Company and Pepsi Inc. duopoly.

翻译：多智能体战略博弈中的核心问题之一，在于学习驱动智能体行为的潜在效用函数。受大规模数据集日益可获取的启发，我们提出了一种统一的数据驱动技术，能够根据观测到的智能体行为（无论这些观测对应于均衡配置还是行动序列的时间序列）来估计其效用函数。在效用函数参数化的标准假设下，所提出的推理方法计算高效，并能找到所有能最优解释观测行为的参数。我们利用可口可乐公司与百事公司双头垄断的历史数据，在广告竞争背景下的市场份额估计问题中，对理论发现进行了数值验证。