Computational Experiments: A New Analysis Method for Cyber-Physical-Social Systems

Given the complex nature of cyber-physical-social systems (CPSSs), understanding their mechanism is essential for analyzing and controlling their actions while minimizing potential harm. However, studying CPSS in the real world is costly and constrained by legal and institutional factors. Computatio...

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Veröffentlicht in:IEEE transactions on systems, man, and cybernetics. Systems Jg. 54; H. 2; S. 813 - 826
Hauptverfasser: Xue, Xiao, Shen, Yifan, Yu, Xiangning, Zhou, De-Yu, Wang, Xiao, Wang, Gang, Wang, Fei-Yue
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Actuators
Behavioral sciences
Cloud computing
Cloud manufacturing service ecosystem (CMSE)
Complex systems
Complexity theory
computational experiments
Computational modeling
cyber-physical-social systems (CPSSs)
Design of experiments
Digital twins
Ecosystems
experiment analysis
experiment design
Organizational aspects
Systems theory
ISSN:2168-2216, 2168-2232
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Zusammenfassung:Given the complex nature of cyber-physical-social systems (CPSSs), understanding their mechanism is essential for analyzing and controlling their actions while minimizing potential harm. However, studying CPSS in the real world is costly and constrained by legal and institutional factors. Computational experiments have emerged as a new method for quantitative analysis, and this article proposes a method of using computational experiments for analyzing CPSS, which consists of model docking, experiment design, and experiment analysis. The cloud manufacturing service ecosystem (CMSE) is used as a typical case study to verify the effectiveness of the proposed method by simulating different operation strategies. The results show that the computational experiments method is effective in providing new means and ideas for analyzing CPSS.
Bibliographie:ObjectType-Article-1
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2023.3322402