Can classical game-theoretic frameworks be extended to capture the bounded rationality and causal reasoning of AI agents? We investigate this question by extending Causal Normal Form Games (CNFGs) to sequential settings, introducing Sequential Causal Multi-Agent Systems (S-CMAS) that incorporate Pearl's Causal Hierarchy across leader-follower interactions. While theoretically elegant -- we prove PSPACE-completeness, develop equilibrium refinements, and establish connections to signaling theory -- our comprehensive empirical investigation reveals a critical limitation: S-CNE provides zero welfare improvement over classical Stackelberg equilibrium across all tested scenarios. Through 50+ Monte Carlo simulations and hand-crafted synthetic examples, we demonstrate that backward induction with rational best-response eliminates any strategic advantage from causal layer distinctions. We construct a theoretical example illustrating conditions where benefits could emerge ($ε$-rational satisficing followers), though implementation confirms that even relaxed rationality assumptions prove insufficient when good instincts align with optimal play. This negative result provides valuable insight: classical game-theoretic extensions grounded in rational choice are fundamentally incompatible with causal reasoning advantages, motivating new theoretical frameworks beyond standard Nash equilibrium for agentic AI.

翻译：经典博弈论框架能否扩展以捕捉智能体有限理性与因果推理？我们通过将因果正规型博弈扩展至序贯场景来研究这一问题，引入了序贯因果多智能体系统，该系统在领导者-跟随者交互中融入了珀尔的因果层级结构。尽管在理论上具有优雅性——我们证明了PSPACE完备性，发展了均衡精炼，并建立了与信号传递理论的联系——但我们的综合实证研究揭示了一个关键局限：在所有测试场景中，S-CNE相较于经典斯塔克尔伯格均衡未能带来任何福利改进。通过50多次蒙特卡洛模拟和手工构建的合成案例，我们证明采用理性最佳响应的逆向归纳法会消除因果层级区分带来的任何策略优势。我们构建了一个理论案例来说明可能产生收益的条件（ε理性满意型跟随者），但实际实施证实，即使放宽理性假设，当良好直觉与最优策略一致时仍显不足。这一否定性结果提供了重要启示：基于理性选择的经典博弈论扩展与因果推理优势存在根本性不兼容，这推动着为智能体人工智能建立超越标准纳什均衡的新理论框架。