Graphical Models for Decision-Making: Integrating Causality and Game Theory
This work addresses the gap between theoretical advancements and practical applications in decision-making for practitioners, though it is incremental in nature.
This paper clarifies key concepts at the intersection of causality and game theory within probabilistic graphical models to improve decision-making, providing practitioners with insights for implementation across different scenarios.
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.