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

Xiao Xue; Deyu Zhou; Xiangning Yu; Gang Wang; Juanjuan Li; Xia Xie; Lizhen Cui; Fei-Yue Wang

doi:10.1109/JAS.2024.124221

Volume 11 Issue 4

Apr. 2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > 11(4): 1022-1038

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

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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

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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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Author Bio:
Xiao Xue (Member, IEEE) received the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences in 2007. From 2008 to 2012, he was a Post Doctor with the Department of Automation, Tsinghua University. From 2013 to 2014, he was a Visiting Scholar with the School of Computer Sciences, Carnegie Mellon University, USA. He is currently a Professor with the School of Computer Software, College of Intelligence and Computing, Tianjin University. He is also an Adjunct Professor with the Information School, Xi’an University of Financial and Economics. His research interests include service computing, computational experiment, and social computing

Deyu Zhou (Graduate Student Member, IEEE) is a Ph.D. candidate in artificial intelligence at the School of Software, Shandong University. Her research interests include service computing and computational experiment

Xiangning Yu (Graduate Student Member, IEEE) is a Ph.D. candidate at the College of Intelligence and Computing, Tianjin University. Her research interests include service computing and computational experiments

Gang Wang is a Professor at the Information School, Xi’an University of Financial and Economics. He is also a Postdoctoral Fellow for University of Texas at San Antonio, USA. His current research interests include trust management, privacy security and social computing, cloud computing and AIoT

Juanjuan Li (Member, IEEE) received the Ph.D. degree in control science and engineering from Beijing Institute of Technology in 2021. She is currently an Associate Professor with the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences. Her research interests include blockchain, DAO, and decision intelligence

Xia Xie is a Professor at Hainan University in China. She received the Ph.D. degree in computer architecture from Huazhong University of Science and Technology in 2006. Her research interests include data mining and knowledge graph

Lizhen Cui (Member, IEEE) received the bachelor, M.Sc., and Ph.D. degrees from Shandong University in 1999, 2002, and 2005, respectively.In 2013, he was a Visiting Scholar with Georgia Tech, Atlanta, GA, USA. He is currently a Professor with the School of Software and the Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, and also a Visiting Professor with Nanyang Technological University, Singapore. He has authored or coauthored over 100 articles in journals and refereed conference proceedings. His research interests include big data management and analysis and AI theory and application

Fei-Yue Wang (Fellow, IEEE) received the Ph.D. degree in computer and systems engineering from the Rensselaer Polytechnic Institute, Troy, NY, USA, in 1990. He joined the University of Arizona in 1990 and became a Professor and the Director of the Robotics and Automation Laboratory and the Program in Advanced Research for Complex Systems. In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Chinese Talents Program from the State Planning Council, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS, and Vice President of Institute of Automation, CAS in 2006. He found CAS Center for Social Computing and Parallel Management in 2008, and became the State Specially Appointed Expert and the Founding Director of the State Key Laboratory for Management and Control of Complex Systems in 2011. His current research focuses on methods and applications for parallel intelligence, social computing, and knowledge automation. He is a Fellow of INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China, numerous best papers awards from IEEE Transactions, and became an Outstanding Scientist of ACM for his work in intelligent control and social computing. He received the IEEE ITS Outstanding Application and Research Awards in 2009, 2011, and 2015, respectively, the IEEE SMC Norbert Wiener Award in 2014, and became the IFAC Pavel J. Nowacki Distinguished Lecturer in 2021.Since 1997, he has been serving as the General or Program Chair of over 30 IEEE, INFORMS, IFAC, ACM, and ASME conferences. He was the President of the IEEE ITS Society from 2005 to 2007, the IEEE Council of RFID from 2019 to 2021, the Chinese Association for Science and Technology, USA, in 2005, the American Zhu Kezhen Education Foundation from 2007 to 2008, the Vice President of the ACM China Council from 2010 to 2011, the Vice President and the Secretary General of the Chinese Association of Automation from 2008 to 2018, the Vice President of IEEE Systems, Man, and Cybernetics Society from 2019 to 2021. He was the Founding Editor-in-Chief (EiC) of the International Journal of Intelligent Control and Systems from 1995 to 2000, IEEE ITS Magazine from 2006 to 2007, IEEE/CAA Journal of Automatica Sinica from 2014−2017, China’s Journal of Command and Control from 2015−2021, and China’s Journal of Intelligent Science and Technology from 2019 to 2021. He was the EiC of the IEEE Intelligent Systems from 2009 to 2012, IEEE Transactions on Intelligent Transportation Systems from 2009 to 2016, and IEEE Transactions on ComputationaL Social Systems from 2017 to 2020. Currently, he is the President of CAA’s Supervision Council and the EiC of IEEE Transaction on Intelligent Vehicles
Corresponding author: Xiao Xue, e-mail: jzxuexiao@tju.edu.cn
Xiao Xue and Deyu Zhou contributed equally to this work.
Received Date: 2023-10-16
Revised Date: 2023-12-07
Accepted Date: 2023-12-25

Available Online: 2024-02-21

Abstract

Abstract

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”.
- Agent-based modeling,
- computational experiments,
- cyber-physical-social systems (CPSS),
- generative deduction,
- generative experiments,
- meta model

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Xiao Xue and Deyu Zhou contributed equally to this work.

References(42)

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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

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

doi: 10.1109/JAS.2024.124221

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通讯作者: 陈斌, bchen63@163.com

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