Deterministic Execution Authority Under Irreversibility Constraints: A Hybrid Governance Model for AI and Capital-Intensive Systems

This preprint introduces a deterministic execution authority framework designed for AI-mediated and capital-intensive systems operating under irreversibility and uncertainty constraints. The study formalizes a separation between prediction and execution, arguing that probabilistic estimation alone is insufficient for legitimate action in high-impact domains. A minimal governance state vector is defined, incorporating probability, impact, irreversibility, uncertainty, authority adequacy, and time exposure. A log-domain hybrid risk aggregation model is proposed, together with a hybrid authority construct integrating organizational tier validation, cryptographic signature integrity, and a quantitative authority adequacy score. Execution decisions are compiled through a deterministic, fail-closed gate that enforces uncertainty bounds, authority thresholds, and irreversibility-scaled risk constraints. Formal safety invariants are presented, including determinism under canonical input, irreversibility monotonicity, and authority score non-increasing behavior with respect to risk amplification. The framework is compatible with formal specification and model-checking methodologies and structurally aligned with contemporary risk-based AI governance principles. This work does not propose a new predictive model. Instead, it defines an execution governance architecture intended to increase auditability, systemic stability, and regulatory defensibility in AI-driven decision environments.