Multi-agent game theory for precision psychiatry

Psychiatry has learned to measure and map the brain in exquisite detail, yet it still lacks a theory of what happens between minds. Most current frameworks treat cognition as an individual process, studied through static and non-interactive tasks, creating a fundamental mismatch with real-world social behavior, which is dynamic and reciprocal. In restricted psychiatric conditions such as autism spectrum disorder (ASD) and schizophrenia with social phenotypes, core symptoms emerge most clearly during interaction. Here I argue that these differences are better understood as failures of coordination between agents rather than solely deficits within individuals, and further make forward-looking computational predictions based on a game-theoretical perspective. Multi-agent game theory (MAGT) provides a conceptual yet principled framework to model such interactions, capturing how beliefs, strategies, and learning evolve through exchange. This perspective reframes psychiatric disorders involving interactional phenotypes as emergent properties of interacting systems and opens new avenues for mechanistic understanding and intervention. I also discuss the limitations and challenges of this new computational framework as applied to precision psychiatry.