This paper introduces the Chief Decision & Capital Officer (CDCO) framework and the AICOS (Artificial Intelligence Control Operating System) as a governance-first decision authority infrastructure for AI-driven systems. While modern AI systems excel at prediction and optimization, they lack a deterministic mechanism to validate decisions under uncertainty, irreversibility, and systemic risk. This work addresses this structural gap by formalizing decision-making as a constrained, multi-domain transformation process integrating mathematics, physics, economics, and information theory. The proposed framework introduces a composite risk function based on probability, impact, irreversibility, time-decay, and uncertainty, combined with entropy-based decision constraints and human-final authority enforcement. A layered system architecture is presented, where AI models generate signals, and the CDCO enforces validation through governance constraints, ensuring that decisions are auditable, reproducible, and economically valid. A real-world financial case study demonstrates the application of the framework in credit risk decision-making, highlighting its ability to prevent premature approvals and enforce decision discipline under uncertainty. This work positions Decision Authority Infrastructure as a missing layer in modern AI systems and proposes a new executive paradigm (CDCO) for governing high-impact decisions across finance, energy, and critical infrastructure domains.
YASIN KALAFATOGLU (2026) studied this question.