Understanding how agents coordinate or compete from limited behavioral data is central to modeling strategic interactions in traffic, robotics, and other multi-agent systems. In this work, we investigate the following complementary formulations of inverse game-theoretic learning: (i) a Closed-form Correlated Equilibrium Maximum-Likelihood estimator (CE-ML) specialized for $2\times2$ games; and (ii) a Logit Best Response Maximum-Likelihood estimator (LBR-ML) that captures long-run adaptation dynamics via stochastic response processes. Together, these approaches span the spectrum between static equilibrium consistency and dynamic behavioral realism. We evaluate them on synthetic "chicken-dare" games and traffic-interaction scenarios simulated in SUMO, comparing parameter recovery and distributional fit. Results reveal clear trade-offs between interpretability, computational tractability, and behavioral expressiveness across models.

翻译：如何从有限的行为数据中理解智能体之间的协作或竞争，是交通、机器人学及其他多智能体系统中战略交互建模的核心问题。本研究探讨以下两种互补的逆博弈学习框架：（i）专为$2\times2$博弈设计的闭式相关均衡最大似然估计器（CE-ML）；（ii）通过随机响应过程捕捉长期适应动态的Logit最优响应最大似然估计器（LBR-ML）。这两种方法共同覆盖了静态均衡一致性与动态行为真实性之间的研究谱系。我们在合成“胆小鬼博弈”及SUMO仿真的交通交互场景中评估了这些方法，比较了参数还原能力与分布拟合效果。结果表明，不同模型在可解释性、计算易处理性和行为表达能力之间存在明确的权衡关系。