Decision Objects: The Fundamental Unit of Decision Engineering Science™

This working paper introduces the Decision Object as a fundamental unit of analysis within Decision Engineering Science (DES). It addresses a growing limitation in contemporary AI and decision-support systems: while substantial attention is given to models, predictions, agents, and outputs, the decision itself is rarely represented as a persistent, structured, and governable object. The paper proposes a formal representation of a Decision Object that captures the essential components of a bounded decision instance, including signals, context, objectives, evidence, constraints, alternatives, rationale, the resulting decision, execution, and observed outcomes. This representation creates a bridge between decision formation, governance, execution, and learning. A lifecycle model is introduced to describe how Decision Objects evolve from initiation and evidence collection through alternative evaluation, governance, decision commitment, action, outcome observation, and feedback, while maintaining a stable identity over time. The paper also introduces the Decision Object Evidence Model (DOEM) to structure the relationship between information sources, signals, evidence, alternative evaluation, rationale, and final decisions. By treating decisions rather than models as primary governance objects, the Decision Object concept provides a foundation for decision traceability, accountability, observability, auditability, and decision-quality assessment across human, AI, and hybrid decision systems. The paper positions Decision Objects as an architectural and theoretical building block for Decision Engineering Science and establishes a foundation for future research into Decision Object Graphs, decision admissibility, governance gates, decision quality metrics, and governed autonomous systems. Keywords: Decision Engineering Science, Decision Objects, Decision Governance, AI Governance, Decision Intelligence, Decision Architecture, Decision Quality, Decision Evidence, Human-AI Decision-Making, Agentic AI, Autonomous Systems