DECISION ENGINEERING SCIENCE™ Manifesto of a New Discipline

This work introduces Decision Engineering Science™ (DES) as a foundational interdisciplinary discipline dedicated to the formalization, design, measurement, and optimization of decision systems across human and artificial agents. Despite decades of progress in data systems, analytics, and artificial intelligence, organizations continue to experience persistent failures in decision-making. This paradox reveals a structural gap: while data and predictive systems have been extensively engineered, decisions themselves remain largely implicit, fragmented, and unmeasured. This manifesto advances a central claim:decisions—not data—are the fundamental unit of value in modern systems. Building on this premise, Decision Engineering Science™ establishes decisions as first-class, structured, and measurable entities. It introduces decision systems as core architectural constructs and defines a dedicated Decision Layer within modern computational and organizational stacks, positioned between AI systems and execution. The document outlines: a formal definition of Decision Engineering Science™ a structural model of decisions as engineered objects the concept of decision systems as interconnected networks of decisions the separation of decision quality from outcome quality the introduction of decision-level metrics, including the Decision Quality Index (DQI) key failure modes of decision systems, such as signal distortion, cognitive drift, and feedback breakdown the role of human–AI co-decision systems in modern environments the economic implications of decision systems, including the emergence of the Cognitive Economy By reframing decision-making as an engineering discipline, this work provides a conceptual and structural foundation for aligning data, artificial intelligence, and execution within complex systems. Decision Engineering Science™ is not an extension of data science, business intelligence, or decision intelligence. It is introduced as a distinct and necessary layer in modern system design—one that governs how decisions are structured, evaluated, and continuously improved. This manifesto serves as an initial foundation for further research, system development, and institutional adoption of decision-centric architectures.