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

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”.

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