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Metaphorical Description and Model Description of Complex Systems

From metaphorical description to model description is the symbol of the maturity of complexity science. This paper focuses on two model description methods of complex systems: formal system model and relevance theory model, and compares their advantages and disadvantages. These two models are descri...

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Detalles Bibliográficos
Autores principales: Huang, Huaxin, Li, Yuhang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283014/
https://www.ncbi.nlm.nih.gov/pubmed/35845892
http://dx.doi.org/10.1155/2022/3094010
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author Huang, Huaxin
Li, Yuhang
author_facet Huang, Huaxin
Li, Yuhang
author_sort Huang, Huaxin
collection PubMed
description From metaphorical description to model description is the symbol of the maturity of complexity science. This paper focuses on two model description methods of complex systems: formal system model and relevance theory model, and compares their advantages and disadvantages. These two models are descriptions of complex systems from different perspectives and have important and irreplaceable significance in the theoretical research and practical application of complex systems. The future research direction of complex systems is not the “integration” of the two models but the alternation of the two models, which scientifically reveals the essence of complex systems from multiple perspectives. In view of the complexity of education and the scientificity of educational evaluation, a fuzzy evaluation method is proposed. Fuzzy evaluation is a mathematical thinking mode based on fuzzy theory and mathematical methods, which studies fuzzy and complex phenomena, and provides quantitative relationship and description for educational evaluation.
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spelling pubmed-92830142022-07-15 Metaphorical Description and Model Description of Complex Systems Huang, Huaxin Li, Yuhang Comput Intell Neurosci Research Article From metaphorical description to model description is the symbol of the maturity of complexity science. This paper focuses on two model description methods of complex systems: formal system model and relevance theory model, and compares their advantages and disadvantages. These two models are descriptions of complex systems from different perspectives and have important and irreplaceable significance in the theoretical research and practical application of complex systems. The future research direction of complex systems is not the “integration” of the two models but the alternation of the two models, which scientifically reveals the essence of complex systems from multiple perspectives. In view of the complexity of education and the scientificity of educational evaluation, a fuzzy evaluation method is proposed. Fuzzy evaluation is a mathematical thinking mode based on fuzzy theory and mathematical methods, which studies fuzzy and complex phenomena, and provides quantitative relationship and description for educational evaluation. Hindawi 2022-07-07 /pmc/articles/PMC9283014/ /pubmed/35845892 http://dx.doi.org/10.1155/2022/3094010 Text en Copyright © 2022 Huaxin Huang and Yuhang Li. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Huang, Huaxin
Li, Yuhang
Metaphorical Description and Model Description of Complex Systems
title Metaphorical Description and Model Description of Complex Systems
title_full Metaphorical Description and Model Description of Complex Systems
title_fullStr Metaphorical Description and Model Description of Complex Systems
title_full_unstemmed Metaphorical Description and Model Description of Complex Systems
title_short Metaphorical Description and Model Description of Complex Systems
title_sort metaphorical description and model description of complex systems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283014/
https://www.ncbi.nlm.nih.gov/pubmed/35845892
http://dx.doi.org/10.1155/2022/3094010
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