Operationalizing Contingency: The Axiomatic Model (AXM) for Applied AI Alignment

Abstract Current methodologies for Artificial Intelligence alignment operate under a fundamental systems vulnerability: they attempt to derive ethical constraints from historically biased human datasets. This reliance on subjective probability-averaging floods computational models with changing variables and undefined relational weights, inevitably generating un-auditable "ethical hallucinations" and utilitarian trade-offs that compromise human agency. To build systems capable of safe and scalable ethical solutions, alignment must transition from subjective behavioral principles to objective ontological computation. This paper introduces the Axiomatic Model (AXM), an executable systems framework that translates the formal S4 modal logic and Standard Deontic Logic proofs of the Ontological Contingency Model (OCM) directly into applied cybernetics. The AXM operationalizes the preservation of Unconditional Human Worth (□W) and Free Will (◇FW) through a dual-mechanic architecture. It establishes a 1D Lexicographic Governor—a strict Boolean triage hierarchy governed by an objective function (J(a))—that prohibits subtractive computational trajectories. Concurrently, it deploys a 3D State Vector Space (S = ⟨ X, Y, Z ⟩) that actively optimizes the human operator’s biological and psychological baseline, absorbing environmental complexity to maximize their capacity for teleological choice. By halting at the systems formulation boundary and eschewing simulated empirical datasets, this paper provides the mathematical constraints required for future machine learning models to facilitate syntropic, transgenerational co-evolution between artificial intelligence and human operators. Author's Note: The foundational concepts and early theoretical explorations of the Axiomatic Model (AXM) were originally serialized as a digital essay series by the author. This manuscript serves as the formal, unified systems-architecture consolidation of that work.