Navigating uncertainty in human interaction: A management science approach using Bayesian games in therapy

Abstract

Therapy sessions are widely recognized as an effective form of treatment, with outcomes sometimes more strongly influenced by the quality of therapist–patient interaction and decision-making than by the specific methods employed, prompting extensive empirical investigation into these dynamics. Existing studies overlook individual differences and long-term effects, relying on generalized findings that miss the complexity of human interactions and therapeutic decision-making. To address these limitations, this study adopts a structured analytical approach that captures the nuanced, evolving nature of therapist–patient interactions and enables long-term insight into how individual behaviors and strategic decisions shape therapeutic trajectories.

Game theory, widely used to optimize and analyze multi-agent decision-making across various domains, provides a powerful framework for this study. By incorporating Nash equilibrium, Bayesian games, and repeated games, the proposed model captures the uncertainty and complexity inherent in real-world interactions. The model highlights the strategic merit of selecting non-cooperative policies under certain conditions and, through simulation analysis, demonstrates that patient behavior has a significantly greater impact on session outcomes compared to that of the therapist. Furthermore, the influence of cooperation becomes more pronounced as the planning horizon extends into the long term. Therapists who adapt their strategies to patient type and behavior can enhance outcomes, while rigidity may hinder progress. The model offers practical value in guiding effective, personalized strategy selection.