Regenerative Artificial Intelligence: A Unified Decision Architecture for Wicked Governance Systems

This working paper introduces the Regen-5 Framework, the first unified scientific architecture for Regenerative Artificial Intelligence. Authored by Aleksandra Pinar (ORCID: 0009-0001-1135-7801), the framework defines a new field of AI research dedicated to decision-making in wicked, complex, and value-conflicted socio-technical systems. The Regen-5 Framework consists of three core components: CARES — Cognitive Alignment & Regenerative Systems RADA — Regenerative Argumentation & Deliberation Architecture CRDP — Continuous Regenerative Decision Process Together, these components form a cognitive–deliberative–temporal architecture enabling AI systems to support long-horizon, multi-stakeholder, regenerative decision-making at institutional, governmental, and global scales. This publication provides: the formal definition of Regenerative AI as a scientific field, the mathematical structure of the Regen-5 model, theoretical foundations based on wicked-problem theory, design science, systems thinking, cognitive systems engineering, and argumentation science, a conceptual blueprint for implementing CARES, RADA, and CRDP in real-world governance, HR systems, sustainability analytics, and public-policy environments. This is the first official publication of the Regen AI Institute and establishes the conceptual and intellectual ownership of the Regen-5 Framework and its sub-models. All subsequent papers in the series (CARES, RADA, CRDP) will expand this foundation. This work is intended for researchers, policymakers, AI architects, sustainability leaders, and institutions developing long-term decision systems in complex environments.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-16
DOI
https://doi.org/10.5281/zenodo.17624127
Primary Topic
Innovation, Sustainability, Human-Machine Systems
Type
article
Field-Weighted Citation Impact
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article

Regenerative Artificial Intelligence: A Unified Decision Architecture for Wicked Governance Systems

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

Regenerative Artificial Intelligence: A Unified Decision Architecture for Wicked Governance Systems

article en

Abstract

This working paper introduces the Regen-5 Framework, the first unified scientific architecture for Regenerative Artificial Intelligence. Authored by Aleksandra Pinar (ORCID: 0009-0001-1135-7801), the framework defines a new field of AI research dedicated to decision-making in wicked, complex, and value-conflicted socio-technical systems. The Regen-5 Framework consists of three core components: CARES — Cognitive Alignment & Regenerative Systems RADA — Regenerative Argumentation & Deliberation Architecture CRDP — Continuous Regenerative Decision Process Together, these components form a cognitive–deliberative–temporal architecture enabling AI systems to support long-horizon, multi-stakeholder, regenerative decision-making at institutional, governmental, and global scales. This publication provides: the formal definition of Regenerative AI as a scientific field, the mathematical structure of the Regen-5 model, theoretical foundations based on wicked-problem theory, design science, systems thinking, cognitive systems engineering, and argumentation science, a conceptual blueprint for implementing CARES, RADA, and CRDP in real-world governance, HR systems, sustainability analytics, and public-policy environments. This is the first official publication of the Regen AI Institute and establishes the conceptual and intellectual ownership of the Regen-5 Framework and its sub-models. All subsequent papers in the series (CARES, RADA, CRDP) will expand this foundation. This work is intended for researchers, policymakers, AI architects, sustainability leaders, and institutions developing long-term decision systems in complex environments.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 32%
Innovation, Sustainability, Human-Machine Systems
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