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 (Nash) equilibrium configurations or to action profile trajectories. 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:多试剂战略游戏的一个中心问题是学习推动代理人行为的基本公用设施。由于大量数据集越来越多,我们开发了一种统一的数据驱动技术,从观察到的代理人行为中估计代理人的公用功能,而不论观察是否与(纳什)平衡配置或行动剖面轨迹相符。根据关于公用设施平衡的标准假设,拟议的推论方法在计算上效率很高,并找到使所观察到的行为合理化的所有参数。我们利用可口可乐公司和百事可乐公司在广告竞争中的历史数据,从数字上验证了我们关于市场份额估算问题的理论结论。