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Volume 11 Issue 4
Apr.  2024

IEEE/CAA Journal of Automatica Sinica

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X. Xue, D. Zhou, X. Yu, G. Wang, J. Li, X. Xie, L. Cui, and  F.-Y. Wang,  “Computational experiments for complex social systems: Experiment design and generative explanation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 1022–1038, Apr. 2024. doi: 10.1109/JAS.2024.124221
Citation: X. Xue, D. Zhou, X. Yu, G. Wang, J. Li, X. Xie, L. Cui, and  F.-Y. Wang,  “Computational experiments for complex social systems: Experiment design and generative explanation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 1022–1038, Apr. 2024. doi: 10.1109/JAS.2024.124221

Computational Experiments for Complex Social Systems: Experiment Design and Generative Explanation

doi: 10.1109/JAS.2024.124221
Funds:  This work was supported in part by the National Key Research and Development Program of China (2021YFF0900800), the National Natural Science Foundation of China (61972276, 62206116, 62032016), the New Liberal Arts Reform and Practice Project of National Ministry of Education (2021170002), the Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems (20210101), and Tianjin University Talent Innovation Reward Program for Literature and Science Graduate Student (C1-2022-010)
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  • Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber-physical-social systems (CPSS). In this context, computational experiments method has emerged as a novel approach for the design, analysis, management, control, and integration of CPSS, which can realize the causal analysis of complex systems by means of “algorithmization” of “counterfactuals”. However, because CPSS involve human and social factors (e.g., autonomy, initiative, and sociality), it is difficult for traditional design of experiment (DOE) methods to achieve the generative explanation of system emergence. To address this challenge, this paper proposes an integrated approach to the design of computational experiments, incorporating three key modules: 1) Descriptive module: Determining the influencing factors and response variables of the system by means of the modeling of an artificial society; 2) Interpretative module: Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena; 3) Predictive module: Building a meta-model that is equivalent to artificial society to explore its operating laws. Finally, a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach, which can reveal the social impact of algorithmic behavior on “rider race”.

     

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  • Xiao Xue and Deyu Zhou contributed equally to this work.
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    Highlights

    • It is difficult for traditional design of experiment methods to achieve the generative explanation of system emergence
    • We propose an integrated approach to the design of computational experiments, incorporating three key modules: descriptive module, interpretative module, and predictive module

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