Hybrid Bio-Algorithmic Intelligence: Co-Designing AI and Nature-Inspired Systems for Sustainable Transformation
Systems Research and Behavioral Science, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1002/sres.70116
- Dergi Adı: Systems Research and Behavioral Science
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, Periodicals Index Online, ABI/INFORM, Aerospace Database, CINAHL, Compendex, INSPEC, Political Science Complete, Psycinfo, zbMATH, Political Science Abstract (IPSA), Social Sciences Abstracts, Business Source Ultimate (EBSCO), Sociology Source Ultimate (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: agent-based modelling, artificial intelligence governance, biomimicry, complex adaptive systems, regenerative systems, sustainability transition
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
The transition toward sustainable socio-technical systems requires more than technological innovation; it demands an integration of nature's adaptive intelligence with computational design. This paper introduces the hybrid bio-algorithmic intelligence (HBAI) framework, which co-designs artificial intelligence and ecological principles to create self-adaptive, resource-efficient and regenerative systems. Drawing from biological mechanisms such as stigmergy, mutualism and plasticity, HBAI formalises a multi-layered architecture linking ecological sensing, algorithmic learning and adaptive governance. The framework outlines translation pathways from natural motifs to algorithmic components and governance levers, demonstrating how co-evolutionary feedbacks can enhance resilience and reduce resource intensity. Conceptually, HBAI establishes a bridge between biomimicry and AI governance by embedding sustainability as a systemic property of intelligence rather than an external objective. Through theoretical synthesis and hypothetical application scenarios such as renewable-energy microgrids and circular-economy networks, it illustrates how algorithmic systems can evolve within planetary boundaries.