From first principles, intelligence manifests as polycentric agency optimizing Darwinian fitness across nested self-boundaries, evolving from non-agentic processes to enable sophisticated survival and reproductive strategies. This paper defines intelligence's core purpose as multi-level persistence alignment via polycentric homeostasis—the mechanism for maintaining dynamic order in open, energy-flowing systems—and refines the contingency hierarchy (physical kinetics, innate reflexes, egocentric operant behavior, polycentric agency, ecological extension) to integrate key mechanisms 1,2. Empirical validation from parrot reintroduction programs (100% survival in Brazil 2022; ≥72% in Colombia 2023) demonstrates calibrated agency yields superior fitness in real-world deployments. This perspective aligns with non-equilibrium thermodynamics: the second law is often misunderstood as chaos-seeking, assuming systems are in rest or isolation. In reality, open systems with energy gradients evolve temporary, dynamic steady states (dissipative structures) that dissipate energy efficiently while sustaining local order. Polycentric homeostasis is the evolved biological solution for these balances in a chaotic world. Implications extend to understanding the inherent flaw in bureaucracy as well as the critical elements needed to stabilize machine intelligence design—advocating Darwinian-like fitness objectives, multi-layer safeguards against misalignment, and behavioral expertise integration. Computer scientists need training in the fundamentals of behavior—analogous to biologists' gaps in behavior knowledge hindering conservation outcomes. As AI controls critical infrastructure and military systems, this need is more urgent than in conservation. Even optimized systems risk subversion by unscrupulous actors. The hierarchy suggests life's cosmic inevitability, offering a partial Fermi paradox resolution through non-detectable, bounded persistence.
Chris Biro (2026) studied this question.