融合因果与博弈论的图模型，提升复杂决策可靠性
Graphical Models for Decision-Making: Integrating Causality and Game Theory
- 用概率图模型统一建模因果关系与多方博弈
- 明确模型输入要求，指导实际应用选择
- 适合政策制定与多主体决策场景研究者
因果推断与博弈论是决策科学中两个重要领域，分别用于刻画复杂政策问题中的因果关系和利益相关方的战略互动。将二者结合已带来理论突破，但其实际应用仍不充分。本文系统梳理了二者交叉的核心概念，聚焦于概率图模型框架下的整合路径。通过严谨分析与直观案例，阐明了模型实施所需的关键输入，为实践者提供在不同场景下选择与应用模型的指导，并引用已有研究支持实现可行性。期望推动该类模型在真实世界决策中的广泛应用。
原文摘要 · Abstract (English)
Causality and game theory are two influential fields that contribute significantly to decision-making in various domains. Causality defines and models causal relationships in complex policy problems, while game theory provides insights into strategic interactions among stakeholders with competing interests. Integrating these frameworks has led to significant theoretical advancements with the potential to improve decision-making processes. However, practical applications of these developments remain underexplored. To support efforts toward implementation, this paper clarifies key concepts in game theory and causality that are essential to their intersection, particularly within the context of probabilistic graphical models. By rigorously examining these concepts and illustrating them with intuitive, consistent examples, we clarify the required inputs for implementing these models, provide practitioners with insights into their application and selection across different scenarios, and reference existing research that supports their implementation. We hope this work encourages broader adoption of these models in real-world scenarios.
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