<p>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.</p> <p>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.</p> <p>At its core, DES proposes that:</p> <ul> <li> <p>A decision is not an isolated choice or outcome.</p></li> <li> <p>A decision is not equivalent to a prediction or model output.</p></li> <li> <p>A decision is a structured transformation of information under constraints, uncertainty, and time.</p></li> </ul> <p>This founding paper establishes:</p> <ol> <li> <p>The formal definition of Decision Engineering Science™</p></li> <li> <p>The ontological foundations of decision objects</p></li> <li> <p>The structural layers of engineered decision systems</p></li> <li> <p>A framework for measuring decision quality beyond outcomes</p></li> <li> <p>The distinction between local optimization and systemic resilience</p></li> <li> <p>The boundaries separating DES from adjacent disciplines</p></li> <li> <p>The macroeconomic extension toward the Cognitive Economy</p></li> </ol> <p>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.</p> <p>The paper introduces the Decision Engineering Stack:</p> <p>Ontology → Metrics → Engineering Core → Stability Layer → Macroeconomic Layer</p>