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：在多主体策略博弈的核心问题中，学习驱动主体行为的潜在效用函数是关键。受大规模数据集日益丰富的启发，我们提出了一种统一的数据驱动技术，用于从观测到的行为中估测主体的效用函数——无论这些观测是对应于均衡配置还是行动序列的时间轨迹。在效用函数参数化的标准假设下，该推理方法具有计算高效性，并能找出最合理表征观测行为的所有参数。我们利用可口可乐公司与百事公司双头垄断历史数据，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。