Empirical game-theoretic analysis (EGTA) is a general framework for reasoning about complex games using agent-based simulation. Data from simulating select strategy profiles is employed to estimate a cogent and tractable game model approximating the underlying game. To date, EGTA methodology has focused on game models in normal form; though the simulations play out in sequential observations and decisions over time, the game model abstracts away this temporal structure. Richer models of \textit{extensive-form games} (EFGs) provide a means to capture temporal patterns in action and information, using tree representations. We propose \textit{tree-exploiting EGTA} (TE-EGTA), an approach to incorporate EFG models into EGTA\@. TE-EGTA constructs game models that express observations and temporal organization of activity, albeit at a coarser grain than the underlying agent-based simulation model. The idea is to exploit key structure while maintaining tractability. We establish theoretically and experimentally that exploiting even a little temporal structure can vastly reduce estimation error in strategy-profile payoffs compared to the normal-form model. Further, we explore the implications of EFG models for iterative approaches to EGTA, where strategy spaces are extended incrementally. Our experiments on several game instances demonstrate that TE-EGTA can also improve performance in the iterative setting, as measured by the quality of equilibrium approximation as the strategy spaces are expanded.
翻译:模拟游戏理论分析(EGTA)是利用代理模拟对复杂游戏进行推理的一般框架。来自模拟选定战略剖面的数据被用来估计一个具有说服力和可移动的游戏模型,以模拟基本游戏。迄今为止,EGTA的方法一直以正常的形式侧重于游戏模型;虽然模拟在连续的观察和决定中发挥作用,但游戏模型摘要却消除了这一时间结构。较丰富的 kextit{extensive-form Game}(EFGs)模型提供了一种手段,利用树形演示来捕捉行动和信息的时间模式和信息模式。我们建议采用\textit{tree-developing EGTA}(TE-EGTATA})(TE-EGTA)数据,这是将EFG模型纳入 EGTA模型的一种方法。TGTA(TE-EGTA)构建了显示观察和时间活动组织模式的游戏模型,尽管比基于基本代理模拟模型的粒子粒子粒子。我们从理论上和实验中确定即使很少利用时间结构空间,也可以大大减少战略的估测算值的估算错误错误。 与SEGEGEGEGA 变压战略相比,我们也可以实验的变压模型的模型的模型的模型可以展示。我们用一系列变压模型的模型的实验。