期刊
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2023.3243894
关键词
Computational modeling; Biological system modeling; Solid modeling; Adaptation models; Analytical models; Mathematical models; Cultural differences; Artificial society; artificial stock market (ASM); computational experiments; domain model; epidemic model; model docking
With the advancement of information technology, complex systems are increasingly exhibiting the characteristics of cyber-physical-social systems (CPSS). Due to cost, legal, and institutional constraints, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, mapping diverse domain models to artificial society models has become a key challenge. This article proposes a docking framework between the domain model and the artificial society model, with a specification for model docking from three aspects. The effectiveness of the framework is verified through two classic cases.
Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber-physical-social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.
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