Published March 17, 2026 | Version v1.0
Preprint Open

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

Authors/Creators

Description

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.

Other (English)

This work should be understood not merely as an academic contribution, but as a foundational step toward defining a new class of technological and institutional systems: Decision Authority Infrastructure.

The AICOS Decision Authority Standard (DAS v3.0) is positioned as a minimal yet extensible framework that can operate across multiple domains where decisions carry significant economic, structural, or systemic impact. Rather than functioning as a predictive model or a decision automation tool, the framework establishes the conditions under which decisions become valid, auditable, and economically accountable.

A key design principle of this work is controlled disclosure. The internal architecture, optimization mechanisms, and parameter structures are intentionally not fully exposed. This approach ensures the preservation of intellectual property while providing sufficient formal structure for validation, auditability, and institutional integration.

The framework introduces the concept of decisions as certified objects, where each decision can be associated with authority validation, risk exposure characterization, irreversibility assessment, and reproducibility guarantees. This perspective enables decision systems to be evaluated not only by performance metrics, but by governance integrity and financial impact.

In high-capital environments, such as energy infrastructure and financial systems, decision errors are not isolated events but can propagate across networks, leading to cascading failures. DAS addresses this by embedding governance constraints directly into the decision structure, ensuring that invalid decisions are rejected prior to execution.

The approach is compatible with emerging regulatory and governance-oriented AI frameworks, and is designed to integrate with institutional processes, audit systems, and compliance environments.

From a broader perspective, this work contributes to the transition from tool-based artificial intelligence toward infrastructure-level decision governance systems. It suggests that future large-scale systems will not be evaluated solely on predictive accuracy, but on their ability to produce decisions that are verifiable, constrained, and economically justified.

This publication represents an initial formalization of the AICOS Decision Authority paradigm. Further developments are expected to extend the framework through empirical validation, scenario-based datasets, and domain-specific implementations.

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AICOS_DAS_v3_Governed_Decision_Infrastructure.tex.pdf

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Repository URL
https://orcid.org/0009-0009-5063-999X

References

  • Kalafatoğlu, Y. (2026). AICOS Decision Authority Standard (DAS v3.0): A Stochastic, Control-Theoretic and Economic Framework for Governed Decision Infrastructure. Zenodo. https://doi.org/XXXXX