From metasynthesis to edge intelligence: A novel entropy-regulated layered multi-agent coordination (ER-LMAC) paradigm for the management of open complex giant systems

Open complex giant systems (OCGSs) represent some of the most challenging problems in contemporary engineering and governance. Although Qian Xuesen’s OCGS methodology and metasynthesis system approach have provided a rich theoretical foundation, there remains a persistent gap between theory and scalable engineering practice, which we refer to as the entropy-increase dilemma. In this paper, we propose the Entropy-Regulated Layered Multi-Agent Coordination (ER-LMAC) paradigm as a modern, engineering-realizable framework for managing OCGSs. ER-LMAC synthesizes (i) entropy regulation as a cybernetic control objective, (ii) layered multi-agent coordination as a scalable decision mechanism, and (iii) edge intelligence implemented on an end–edge–cloud architecture as the computational substrate. As an empirical validation, we instantiate ER-LMAC in the domain of urban intelligent transportation systems by designing and deploying a decentralized, nonhistorical adaptive traffic signal control (ATSC) system in Xiangyang, China. The system covers 448 intersections in the main urban area and has operated continuously for more than three years. Longitudinal operational data indicate that road traffic efficiency increased by more than 20%, congestion duration decreased by more than 30%, and traffic accident rates were reduced by more than 60%. The city has maintained the lowest congestion index in its province for over 40 consecutive months, despite having one of the highest vehicle ownership levels. These results demonstrate that ER-LMAC can effectively resolve the entropy-increase dilemma in a real-world OCGS and suggest its broader applicability to domains such as smart grids, supply chain logistics, industrial Internet of Things (IIoT), and epidemic prevention and control.