Decision Engineering Science™ A Foundational Framework for Layered Decision Architectures Architectural Primacy in Decision Systems Distinguishing Normative Design, Predictive Simulation, and Optimization Dynamics

This working paper introduces Decision Engineering Science™ (DES), a formal framework for analyzing and engineering layered decision systems. The paper develops the principle of Architectural Primacy, which establishes a hierarchical ordering between normative design, predictive simulation, and optimization dynamics in AI-driven and enterprise decision environments. Modern decision systems increasingly integrate predictive models and optimization algorithms. However, performance metrics such as forecast accuracy or computational efficiency are frequently treated as proxies for decision quality. This paper argues that such conflation leads to structural instability. In coherent decision architectures, normative objectives and governance constraints must precede and constrain predictive and operational mechanisms. Formally, the paper distinguishes three layers: Normative Layer — objective functions and constraint regimes. Predictive Layer — probabilistic or model-based transition structures. Operational Layer — optimization and action selection dynamics. The manuscript introduces formal representations of: Expected utility under uncertainty Feasible action sets Dynamic optimization over time Decision drift as divergence between explicit and implicit objectives The Architectural Primacy Principle (APP) is proposed as a structural stability condition for AI systems and enterprise governance. The paper further develops a Formal Layer Interaction Model describing allowable and restricted cross-layer feedback mechanisms. The framework contributes to decision theory, AI governance, reinforcement learning architecture, and organizational systems design. It provides a structural basis for evaluating AI alignment, preventing KPI capture, and mitigating objective drift in dynamic optimization environments. This work serves as a foundational document within the Decision Engineering Science™ research program and establishes a formal basis for architecture-centric AI system design.