论文标题
参数游戏中数据驱动的行为估计
Data-Driven Behaviour Estimation in Parametric Games
论文作者
论文摘要
多代理战略游戏中的一个核心问题涉及学习推动代理商行为的基础公用事业。由于大型数据集的可用性的增加，我们开发了一种统一的数据驱动技术，以从其观察到的行为中估算药物的效用函数，而与观测值相关，无论观测值是否对应于平衡构型还是对动作概况的时间序列。根据公用事业参数化的标准假设，所提出的推论方法在计算上是有效的，并且找到了所有合理化观察到的行为的参数。我们使用可口可乐公司和百事可乐公司的历史数据来验证广告竞赛中市场份额估计问题的理论发现。
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.
