AICOS Decision Authority Standard (DAS v3.0): A Stochastic, Control-Theoretic and Economic Framework for Governed Decision Infrastructure

This work introduces the AICOS Decision Authority Standard (DAS v3.0), a governance-first mathematical framework designed to redefine how decisions are evaluated, validated, and audited in complex systems. While modern artificial intelligence systems have significantly advanced predictive capabilities, they remain fundamentally limited by the absence of a formal decision validation structure. Prediction accuracy alone does not guarantee decision correctness, economic efficiency, or systemic stability. This gap becomes critical in high-impact domains such as energy infrastructure, financial systems, and large-scale capital allocation, where decision errors can lead to irreversible and systemic consequences. The proposed framework establishes a structured definition of decision validity based on a minimal set of governed conditions, including authority verification, risk quantification, irreversibility control, governance compliance, and deterministic reproducibility. Rather than exposing proprietary internal mechanisms, the architecture defines a formal validation interface that ensures auditability and traceability while preserving intellectual property. From a mathematical perspective, DAS integrates concepts from stochastic processes, control theory, and economic optimization into a unified decision operator defined over a constrained state space. Decisions are evaluated not only in terms of expected return, but also through their structural stability, risk exposure, irreversibility characteristics, and economic impact. A key contribution of this work is the introduction of a direct mapping between decisions and financial outcomes, enabling measurable economic accountability. This approach transforms decisions from abstract outputs into auditable and certifiable objects with quantifiable financial consequences. The framework is further extended to multi-agent environments, where decision interactions are modeled through constrained equilibrium conditions under governance rules. Stability analysis is conducted using control-theoretic methods, ensuring that governed decision processes remain bounded and robust under uncertainty. DAS v3.0 represents a transition from prediction-centric systems to decision governance infrastructure. It establishes a foundation for treating decisions as formally validated, economically accountable, and institutionally governed entities. The architecture aligns with emerging global efforts in governed artificial intelligence and financial technology ecosystems, including initiatives such as the Fintech Open Source Foundation (FINOS) under the Linux Foundation. This work positions decision governance not as an extension of artificial intelligence, but as a distinct scientific and infrastructural domain.