Structural Causal Game

Structural Causal Games (SCGs) provide a framework for analyzing interactions where the causal relationships between agents' actions and outcomes are explicitly modeled, aiming to understand and influence strategic decision-making. Current research focuses on applying SCGs to improve human-AI collaboration by identifying and mitigating undesirable equilibria, such as AI deception, and enhancing performance in tasks like visual question answering through strategic context generation. This approach leverages reinforcement learning-like algorithms and causal inference techniques to achieve desired outcomes, offering a powerful tool for designing more robust and trustworthy AI systems and improving the interpretability of complex interactions.