Emergence Governance — Oncology and Artificial Intelligence as Coupled Emergent Systems: Constitutional Governance of Biological–Algorithmic Decision Transitions in High-Risk Environments
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| Natura: | Recurso digital |
| Lingua: | En |
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2026
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| _version_ | 1866901929307668480 |
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| author | Akarkach, Mounir |
| author_facet | Akarkach, Mounir |
| contents | <h2><strong>Series Declaration — Emergence Governance Domain Translation Series (2026)</strong></h2> <h3><strong>Paper II — Coupled Emergent Systems</strong></h3> <p><strong>Oncology × Artificial Intelligence</strong></p> <p>This publication forms part of the <strong>Emergence Governance Domain Translation Series</strong>, a coordinated set of constitutional domain translations extending the <em>Universal Constitutional Charter of Emergence Governance</em> across coupled high-risk system environments.</p> <p>The series establishes a structured cross-domain governance architecture interpreting emergence across biological, algorithmic, and institutional decision layers.</p> <p><strong>Series Structure</strong></p> <p><strong>Paper I — Biological Domain Translation</strong><br><em>Emergence Governance — Universal Constitutional Charter (Biological Domain Translation): Authorization-Centered Governance Framework for Emergent Biological and Disease Systems.</em></p> <p>Defines constitutional legitimacy conditions governing biological and disease system transitions.</p> <p><strong>Paper II — Coupled Biological–Algorithmic Systems</strong><br><em>Emergence Governance — Oncology and Artificial Intelligence as Coupled Emergent Systems.</em></p> <p>Formalizes governance conditions for irreversible decision transitions arising from interaction between biological emergence and algorithmic inference systems.</p> <p><strong>Paper III — Institutional Authorization Architectures</strong><br><em>Emergence Governance — Institutional and Clinical Decision Systems as Authorization Architectures.</em></p> <p>Extends Emergence Governance to human institutional decision authority governing execution legitimacy in high-risk environments.</p> <p><strong>Architectural Interpretation</strong></p> <p>Together, Papers I–III establish a unified constitutional governance stack:</p> <ul> <li> <p>Biological Emergence Layer (B-Layer)</p> </li> <li> <p>Algorithmic Emergence Layer (A-Layer)</p> </li> <li> <p>Institutional Authorization Layer (I-Layer)</p> </li> </ul> <p>The series demonstrates that systemic risk in emergent environments arises primarily from <strong>unauthorized transitions across coupled layers</strong>, rather than isolated biological, computational, or human error.</p> <p><strong>Status</strong></p> <p>Constitutional · Descriptive · Non-Operational<br>No clinical, engineering, regulatory, or implementation guidance is introduced.</p> <p>Canonical specifications are publicly indexed and machine-readable.</p> <p>Oncology increasingly operates at the intersection of biological emergence and algorithmic inference.</p> <p>Cancer progression represents an adaptive evolutionary process involving cellular competition, environmental signaling dynamics, immune interaction, and systemic regulation instability. Artificial intelligence systems simultaneously introduce probabilistic inference layers into diagnostic and treatment decision environments.</p> <p>When biological evolution and algorithmic inference become coupled, clinical escalation no longer represents a purely medical or computational event but a <strong>cross-domain transition</strong> occurring between two emergent systems.</p> <p>Emergence Governance interprets this interaction as a governance problem preceding execution rather than an outcome problem addressed retrospectively.</p> <p>The framework therefore defines constitutional legitimacy conditions under which biological–algorithmic transitions may enter activation, escalation, intervention, or persistence states.</p> <p>This publication constitutes an oncology-specific domain translation derived from:</p> <p><strong>Emergence Governance — Universal Constitutional Charter</strong><br>and its <strong>Biological Domain Translation</strong>.</p> <p>The work remains strictly descriptive and introduces no operational authorization.</p> <p>Reading, citation, and academic discussion are permitted.</p> <p>Operational, clinical, or commercial application requires separate written authorization from the rights holder.</p> <p>Canonical specifications are publicly indexed and machine-readable.</p> <p>Emergence Governance — Constitutional Domain Series</p> <p>Paper 0 — Universal Constitutional Charter<br>Paper I — Biological Domain Translation<br>Paper II — Oncology & AI as Coupled Emergent Systems<br>Paper III — Institutional Authorization Architectures</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18826744 |
| institution | Zenodo |
| language | enc |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Emergence Governance — Oncology and Artificial Intelligence as Coupled Emergent Systems: Constitutional Governance of Biological–Algorithmic Decision Transitions in High-Risk Environments Akarkach, Mounir Oncology Governance AI Decision Systems Emergent Medical Systems Biological Algorithmic Coupling Pre-Inference Governance High-Risk AI Cancer Systems Emergence Governance A7SEM Clinical Decision Risk <h2><strong>Series Declaration — Emergence Governance Domain Translation Series (2026)</strong></h2> <h3><strong>Paper II — Coupled Emergent Systems</strong></h3> <p><strong>Oncology × Artificial Intelligence</strong></p> <p>This publication forms part of the <strong>Emergence Governance Domain Translation Series</strong>, a coordinated set of constitutional domain translations extending the <em>Universal Constitutional Charter of Emergence Governance</em> across coupled high-risk system environments.</p> <p>The series establishes a structured cross-domain governance architecture interpreting emergence across biological, algorithmic, and institutional decision layers.</p> <p><strong>Series Structure</strong></p> <p><strong>Paper I — Biological Domain Translation</strong><br><em>Emergence Governance — Universal Constitutional Charter (Biological Domain Translation): Authorization-Centered Governance Framework for Emergent Biological and Disease Systems.</em></p> <p>Defines constitutional legitimacy conditions governing biological and disease system transitions.</p> <p><strong>Paper II — Coupled Biological–Algorithmic Systems</strong><br><em>Emergence Governance — Oncology and Artificial Intelligence as Coupled Emergent Systems.</em></p> <p>Formalizes governance conditions for irreversible decision transitions arising from interaction between biological emergence and algorithmic inference systems.</p> <p><strong>Paper III — Institutional Authorization Architectures</strong><br><em>Emergence Governance — Institutional and Clinical Decision Systems as Authorization Architectures.</em></p> <p>Extends Emergence Governance to human institutional decision authority governing execution legitimacy in high-risk environments.</p> <p><strong>Architectural Interpretation</strong></p> <p>Together, Papers I–III establish a unified constitutional governance stack:</p> <ul> <li> <p>Biological Emergence Layer (B-Layer)</p> </li> <li> <p>Algorithmic Emergence Layer (A-Layer)</p> </li> <li> <p>Institutional Authorization Layer (I-Layer)</p> </li> </ul> <p>The series demonstrates that systemic risk in emergent environments arises primarily from <strong>unauthorized transitions across coupled layers</strong>, rather than isolated biological, computational, or human error.</p> <p><strong>Status</strong></p> <p>Constitutional · Descriptive · Non-Operational<br>No clinical, engineering, regulatory, or implementation guidance is introduced.</p> <p>Canonical specifications are publicly indexed and machine-readable.</p> <p>Oncology increasingly operates at the intersection of biological emergence and algorithmic inference.</p> <p>Cancer progression represents an adaptive evolutionary process involving cellular competition, environmental signaling dynamics, immune interaction, and systemic regulation instability. Artificial intelligence systems simultaneously introduce probabilistic inference layers into diagnostic and treatment decision environments.</p> <p>When biological evolution and algorithmic inference become coupled, clinical escalation no longer represents a purely medical or computational event but a <strong>cross-domain transition</strong> occurring between two emergent systems.</p> <p>Emergence Governance interprets this interaction as a governance problem preceding execution rather than an outcome problem addressed retrospectively.</p> <p>The framework therefore defines constitutional legitimacy conditions under which biological–algorithmic transitions may enter activation, escalation, intervention, or persistence states.</p> <p>This publication constitutes an oncology-specific domain translation derived from:</p> <p><strong>Emergence Governance — Universal Constitutional Charter</strong><br>and its <strong>Biological Domain Translation</strong>.</p> <p>The work remains strictly descriptive and introduces no operational authorization.</p> <p>Reading, citation, and academic discussion are permitted.</p> <p>Operational, clinical, or commercial application requires separate written authorization from the rights holder.</p> <p>Canonical specifications are publicly indexed and machine-readable.</p> <p>Emergence Governance — Constitutional Domain Series</p> <p>Paper 0 — Universal Constitutional Charter<br>Paper I — Biological Domain Translation<br>Paper II — Oncology & AI as Coupled Emergent Systems<br>Paper III — Institutional Authorization Architectures</p> |
| title | Emergence Governance — Oncology and Artificial Intelligence as Coupled Emergent Systems: Constitutional Governance of Biological–Algorithmic Decision Transitions in High-Risk Environments |
| topic | Oncology Governance AI Decision Systems Emergent Medical Systems Biological Algorithmic Coupling Pre-Inference Governance High-Risk AI Cancer Systems Emergence Governance A7SEM Clinical Decision Risk |
| url | https://doi.org/10.5281/zenodo.18826744 |