Evolutionary Paradigms of Digital Twins for Complex Industrial and Social Systems: From Precision Perception to Multi-Scenario Decision Support

Abstract

The paradigm of Digital Twins is undergoing a fundamental shift as applications transition from isolated mechanical components to deeply interconnected, complex industrial and social socio-technical systems. This paper critically examines this evolutionary trajectory, tracing its development from baseline precision hardware perception and the subsequent constraints of real-time network optimization to the overarching imperative of multi-scenario decision support. By analyzing the inherent uncertainties within high-fidelity physical data collection and the systemic bottlenecks of heterogeneous data transmission, we highlight how traditional linear modeling often fails to encapsulate the emergent behaviors of socio-technical networks. While the integration of edge computing and predictive analytics offers potential pathways to mitigate these latencies, empirical anomalies encountered during multi-scenario simulations suggest that absolute optimization remains elusive. Consequently, this study shifts focus toward adaptive decision-making frameworks that accommodate bounded rationality and fluctuating environmental variables. Ultimately, these insights underscore that future DT frameworks must transcend deterministic representations, opening up new avenues for resilient, decentralized governance in uncertain, large-scale systems.