Computational Irreducibility as the Foundation of Agency: A Formal Model Connecting Undecidability to Autonomous Behavior in Complex Systems
This foundational work addresses the problem of understanding agency and autonomy for researchers in AI, biology, and philosophy, offering a formal model that connects undecidability to autonomous behavior.
The paper tackles the problem of defining genuine autonomy in systems by proving that truly autonomous systems have fundamentally undecidable future behavior, establishing a formal distinction from non-autonomous systems based on computational unpredictability.
This article presents a formal model demonstrating that genuine autonomy, the ability of a system to self-regulate and pursue objectives, fundamentally implies computational unpredictability from an external perspective. we establish precise mathematical connections, proving that for any truly autonomous system, questions about its future behavior are fundamentally undecidable. this formal undecidability, rather than mere complexity, grounds a principled distinction between autonomous and non-autonomous systems. our framework integrates insights from computational theory and biology, particularly regarding emergent agency and computational irreducibility, to explain how novel information and purpose can arise within a physical universe. the findings have significant implications for artificial intelligence, biological modeling, and philosophical concepts like free will.