Decision Engineering Science™ — Canonical Definitions and Formal Constructs v1.0

Decision Engineering Science™ - Canonical Definitions and Formal Constructs v1.0 establishes the canonical conceptual and formal foundation of Decision Engineering Science™ (DES), developed by Aleksandra Pinar, REGEN AI Institute. The publication defines a structured approach to representing, governing, evaluating, authorizing, executing, and reconstructing consequential decisions across human, AI-assisted, autonomous, and multi-agent decision environments. At the center of DES is the Decision Object, a persistent and governable representation of a decision and its associated context, evidence, constraints, authority, risk, governance state, execution, and outcome. The framework separates concepts that are frequently conflated in operational AI and decision systems, including capability, authority, admissibility, authorization, permission, execution, and outcome. The publication consolidates the canonical DES construct system, including Decision Objects, Decision Systems, Decision Graphs and dependency semantics, Evidence Surface and evidence adequacy, Governance Gates, Decision Admissibility, Decision Authorization, Decision Permits, validity and revalidation, recall, quarantine and failure containment, Decision Impact Graphs, Governance Coverage Maps, Decision-System Health, Governance SLAs, and system-scale, throughput, and saturation semantics. It also establishes formal status semantics separating canonical status, scientific maturity, novelty status, implementation status, and conformance status, providing a controlled basis for subsequent empirical validation, systematic novelty assessment, implementation, interoperability, and conformance research. The publication introduces explicit semantic boundaries and non-merger rules intended to preserve distinctions between related but non-equivalent constructs, including: Decision Object ≠ Decision Permit ≠ Decision Passport Capability ≠ Authority ≠ Admissibility ≠ Authorization ≠ Permission Decision Validity ≠ Permit Validity Revalidation ≠ Reauthorization HOLD ≠ Quarantine ≠ Recall ≠ Containment Decision Graph ≠ Decision Impact Graph ≠ Causal Graph ≠ Lineage Decision Quality ≠ Outcome Quality Version 1.0 serves as the canonical baseline for the DES research and architecture program and provides the reference vocabulary, construct definitions, formal relationships, invariants, lifecycle semantics, and governance boundaries upon which subsequent DES specifications, research questions, metrics, architectures, experiments, and implementations can be developed. Scientific status note: designation of a construct as CANONICAL indicates its normative status within DES v1.0. It does not, by itself, establish empirical validation, independent replication, novelty or priority, implementation maturity, standards conformance, certification, safety, or legal/regulatory compliance.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-25
DOI
https://doi.org/10.5281/zenodo.22958725
Primary Topic
Innovation, Sustainability, Human-Machine Systems
Type
article
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Decision Engineering Science™ — Canonical Definitions and Formal Constructs v1.0

Aleksandra Pinar
Zenodo (CERN European Organization for Nuclear Research)
Innovation, Sustainability, Human-Machine Systems
article

Decision Engineering Science™ — Canonical Definitions and Formal Constructs v1.0

Aleksandra Pinar
article en

Abstract

Decision Engineering Science™ - Canonical Definitions and Formal Constructs v1.0 establishes the canonical conceptual and formal foundation of Decision Engineering Science™ (DES), developed by Aleksandra Pinar, REGEN AI Institute. The publication defines a structured approach to representing, governing, evaluating, authorizing, executing, and reconstructing consequential decisions across human, AI-assisted, autonomous, and multi-agent decision environments. At the center of DES is the Decision Object, a persistent and governable representation of a decision and its associated context, evidence, constraints, authority, risk, governance state, execution, and outcome. The framework separates concepts that are frequently conflated in operational AI and decision systems, including capability, authority, admissibility, authorization, permission, execution, and outcome. The publication consolidates the canonical DES construct system, including Decision Objects, Decision Systems, Decision Graphs and dependency semantics, Evidence Surface and evidence adequacy, Governance Gates, Decision Admissibility, Decision Authorization, Decision Permits, validity and revalidation, recall, quarantine and failure containment, Decision Impact Graphs, Governance Coverage Maps, Decision-System Health, Governance SLAs, and system-scale, throughput, and saturation semantics. It also establishes formal status semantics separating canonical status, scientific maturity, novelty status, implementation status, and conformance status, providing a controlled basis for subsequent empirical validation, systematic novelty assessment, implementation, interoperability, and conformance research. The publication introduces explicit semantic boundaries and non-merger rules intended to preserve distinctions between related but non-equivalent constructs, including: Decision Object ≠ Decision Permit ≠ Decision Passport Capability ≠ Authority ≠ Admissibility ≠ Authorization ≠ Permission Decision Validity ≠ Permit Validity Revalidation ≠ Reauthorization HOLD ≠ Quarantine ≠ Recall ≠ Containment Decision Graph ≠ Decision Impact Graph ≠ Causal Graph ≠ Lineage Decision Quality ≠ Outcome Quality Version 1.0 serves as the canonical baseline for the DES research and architecture program and provides the reference vocabulary, construct definitions, formal relationships, invariants, lifecycle semantics, and governance boundaries upon which subsequent DES specifications, research questions, metrics, architectures, experiments, and implementations can be developed. Scientific status note: designation of a construct as CANONICAL indicates its normative status within DES v1.0. It does not, by itself, establish empirical validation, independent replication, novelty or priority, implementation maturity, standards conformance, certification, safety, or legal/regulatory compliance.

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