Approximating the Mathematical Structure of Psychodynamics

The paper proposes a mathematical framework for understanding human psychodynamics that could help bridge psychology, psychiatry, and AI safety research by making psychological concepts more precise and quantifiable across different expert fields.

The authors argue that a formalized mathematical model of human cognition is necessary for developing AI systems that can safely interact with humans, particularly in contexts like autonomous negotiations and human-like AI development, suggesting current AI approaches may lack adequate models of human psychology.

The framework uses "process theory" and diagrammatic representations to translate psychological concepts into mathematical language, potentially enabling researchers from different disciplines (psychology, neurotechnology, AI) to communicate and collaborate more effectively on cognitive and safety challenges.

The work explicitly connects psychodynamic theory to AI alignment and AI agent representation, indicating that understanding human psychological processes mathematically could improve how AI systems model and predict human behavior in safety-critical scenarios.

By formalizing psychodynamics, the paper suggests psychology can move toward the kind of quantitative precision found in other biomedical fields, which could improve both psychological treatment and the development of AI systems that interact responsibly with human cognition.