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一个核心问题涉及学习驱动代理行为的潜在效用。受到大型数据集可用性不断增强的推动,我们开发了一种通用的数据驱动技术,用于估计代理的效用函数,无论观测到的情况是均衡配置还是时间序列的行动配置。在效用参数化的标准假设下,所提出的推理方法具有计算效率,并找到最佳理性化观测行为的所有参数。我们使用可口可乐公司和百事可乐公司的历史数据,在广告竞争下市场份额估计问题上在数值上验证了我们的理论发现。