Decision Engineering Science - Designing Decision Systems for Business and Scientific Inquiry

Decision Engineering Science™ (DES) introduces a new engineering discipline focused on the systematic design, evaluation, and governance of decision systems in business, artificial intelligence, and scientific research. While traditional fields such as decision theory, management science, data science, and AI governance analyze choices, optimize models, or regulate algorithms, they do not treat the decision process itself as a first-class engineered object. Decision Engineering Science™ addresses this gap. At its core, DES proposes that: A decision is not an isolated choice or outcome. A decision is not equivalent to a prediction or model output. A decision is a structured transformation of information under constraints, uncertainty, and time. This founding paper establishes: The formal definition of Decision Engineering Science™ The ontological foundations of decision objects The structural layers of engineered decision systems A framework for measuring decision quality beyond outcomes The distinction between local optimization and systemic resilience The boundaries separating DES from adjacent disciplines The macroeconomic extension toward the Cognitive Economy DES positions decision capability as a form of strategic infrastructure. As organizations increasingly rely on AI-assisted and automated systems, failures in decision architecture scale into systemic fragility. The discipline therefore shifts focus from model accuracy to structural decision robustness. The paper introduces the Decision Engineering Stack: Ontology → Metrics → Engineering Core → Stability Layer → Macroeconomic Layer This architecture integrates: Decision Object Theory™ Structural signal processing Outcome limitation analysis Cognitive stability modeling Decision quality metrics (process-based, not outcome-based) The founding paper formally defines DES as: An engineering discipline dedicated to the design, measurement, and governance of decision systems to ensure structural robustness, adaptive capacity, and long-term resilience across organizational and societal scales. By publishing this integration framework, the paper establishes DES as a coherent scientific ecosystem rather than a collection of isolated models or working papers.