This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergen...This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergence in social systems.By integrating mathematical models,agent-based modeling,network dynamic analysis,and hybrid modeling approaches,the study applies CAS theory to case studies in economic markets,political decision-making,and social interactions.The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena,such as market fluctuations,opinion polarization,and sudden outbreaks of social movements.This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture,but also offers valuable decision-support tools for public policy formulation,social governance,and risk management.Emphasizing the importance of interdisciplinary approaches,this work outlines future research directions in high-performance computing,artificial intelligence,and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.展开更多
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 nove...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”.展开更多
文摘This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergence in social systems.By integrating mathematical models,agent-based modeling,network dynamic analysis,and hybrid modeling approaches,the study applies CAS theory to case studies in economic markets,political decision-making,and social interactions.The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena,such as market fluctuations,opinion polarization,and sudden outbreaks of social movements.This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture,but also offers valuable decision-support tools for public policy formulation,social governance,and risk management.Emphasizing the importance of interdisciplinary approaches,this work outlines future research directions in high-performance computing,artificial intelligence,and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.
基金the National Key Research and Development Program of China(2021YFF0900800)the National Natural Science Foundation of China(61972276,62206116,62032016)+2 种基金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)Tianjin University Talent Innovation Reward Program for Literature and Science Graduate Student(C1-2022-010)。
文摘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”.
