Neuroarchitectonics of Digitalization: Adaptive Management of Socio-Economic Systems in the Paradigm of the Circular Economy
Purpose – The rapid transition toward a circular economy requires a fundamental reconfiguration of how socio-economic systems process material and informational resource workflows. This study introduces the concept of the "Neuroarchitectonics of Digitalization" as an advanced cybernetic infrastructure for organization design, leveraging Stafford Beer's Viable System Model (VSM) to enable adaptive management in circular economic configurations. Design/methodology/approach – Utilizing a retrospective empirical evaluation of secondary macro-economic datasets derived from the global Circularity Gap Report and Eurostat Monitoring Framework, the research maps systemic informational bottlenecks across multi-tiered socio-economic networks. Findings – The findings demonstrate that integrating Fourth Industrial Revolution technologies (Artificial Intelligence, Big Data, and Distributed Ledgers) functions as an artificial nervous system that matches environmental variety and downregulates entropy. The resulting architecture translates systemic waste into productive resource inputs via real-time autopoietic feedback mechanisms. Originality – This investigation culminates in a formalized cybernetic control protocol, moving the scholarship away from fragmented digitalization initiatives toward an integrated, self-regulating structural architecture for sustainable economic governance.
Authors
- Nataliia CHUKHRAIEVA (ORCID: https://orcid.org/0000-0003-1537-4841)
- Vita KOVTUN (ORCID: https://orcid.org/0000-0001-7212-6700)
- LARYSA LIGONENKO
- Марія Теплюк (ORCID: https://orcid.org/0000-0001-6823-336X)
- Inna Riepina (ORCID: https://orcid.org/0000-0001-9141-0117)
- Наталія Шевчук
- Inna Semenets-Orlova
Institutions
- Interregional Academy of Personnel Management (UA)
- Kyiv National Economic University named after Vadym Hetman (UA)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-24
- DOI
- https://doi.org/10.5281/zenodo.22940779
- Primary Topic
- Digital Transformation in Industry
- Type
- preprint