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：实证博弈理论分析（EGTA）是一个通过智能体模拟推理复杂博弈的通用框架。该方法利用对选定策略分布的仿真数据，构建一个与潜在博弈近似且易于处理的精简博弈模型。迄今为止，EGTA方法论主要聚焦于标准式博弈模型——尽管仿真过程涉及随时间展开的序贯观测与决策，但现有模型抽象化了这一时序结构。相比之下，更具表达力的\textit{扩展式博弈}（EFG）模型能够通过树形结构捕捉行动与信息的时序模式。本文提出\textit{树结构利用型EGTA}（TE-EGTA），这是一种将EFG模型融入EGTA框架的方法。TE-EGTA所构建的博弈模型虽比底层智能体仿真模型更粗粒度，却能有效表达活动中的观测与时间组织模式。其核心思想是在保持可计算性的前提下利用关键结构特征。我们通过理论分析与实验证明：相较于标准式模型，即使仅利用少量时序结构，也能显著降低策略组合收益的估计误差。进一步地，我们探索了EFG模型对迭代式EGTA方法（通过逐步扩展策略空间进行优化）的影响。在多个博弈实例上的实验表明，随着策略空间扩展，TE-EGTA在迭代设置下也能提升均衡近似的质量。