Human as a State-Transition System: Structure, Engines & IO Relations

This work presents a structural specification of human behavior as a state-transition system. The model defines human cognition and action as a closed-loop interaction among sensor input, layered state representation, predictive modeling, and evaluation dynamics. The architecture consists of three layers (substrate, state, and time) and two core engines: a surrogate model that generates predictions and an evaluation function that modulates state transitions through error signals. Action is treated as an emergent output of constraint-driven convergence rather than an independent decision process. The system is defined as substrate-independent, requiring only recurrent state updates, self-reference, and continuous interaction with the environment. Identity is preserved through structural continuity rather than physical material, enabling implementation across biological, computational, or hybrid systems. This specification unifies emotion, cognition, learning, and decision-making within a single generative framework. It provides a foundation for consistent analysis and design of human-related systems, including artificial intelligence, behavioral modeling, and social systems.