Reusable Cognitive Digital Twins as a Foundational Paradigm for Intelligent Digital Ecosystems

Digital twins are increasingly used to support monitoring, prediction, and decision-making in complex cyber–physical systems; however, most existing digital twin implementations remain domain-specific, model-centric, and weakly integrated with human expertise. The aim of this study is to examine how digital twins can be designed as reusable cognitive architectures capable of consistent reasoning, semantic interpretation, and human-centered decision support across heterogeneous application domains. To achieve this aim, the paper proposes the reusable cognitive digital twin (RCDT) paradigm, which combines a reusable architectural core containing structural, behavioral, functional, and cognitive invariants with a cognitive orchestration layer implementing four coordinated reasoning modalities: structural, generative, analytical, and operational. The methodology is architectural and conceptual, supported by formal operator-based modeling and illustrated through two contrasting case studies—a safety-critical aviation system and a large-scale smart city environment. The results demonstrate that the same reusable cognitive modules and evaluation indices can be instantiated across both domains, enabling explicit management of semantic consistency, scenario adequacy, and decision confidence, as well as systematic integration of human expertise. These findings indicate that RCDTs provide a transferable and interpretable cognitive foundation for intelligent digital ecosystems, extending traditional digital twin capabilities beyond domain-bound and purely data-driven approaches.