# Complex System Models and Their Application in Industrial Cluster and Innovation Systems

Authors: Su Yi; Yang Zaoli; Xie Xuemei; Garg Harish
Journal: Complexity
 (2022)

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## Body

## 1. Model Extension and Algorithm Optimization

Y. Wei et al. optimized the traditional PageRank algorithm by incorporating a text similarity approach (TSA) into their study and validated the algorithm using Chinese environmental health standards. F. Zhang et al. optimized an industrial product service system. They incorporated affinity propagation (AP), quality function development (QFD), and axiomatic design (AD) to solve many difficulties such as uncertain customer requirements, subjective experience design, and long debugging time. J. Liu et al. examined university ranking from the perspective of complex systems and proposed an innovative university ranking method. L. Zhang and W. Chen used complex network theory as the basis to construct a multilayer knowledge sharing network environment and proposed a knowledge sharing model in the complex network environment. H. Jiang et al. expanded their research, reconstructed the echo model, and extended the theory of complex adaptive systems.


## 2. Industry Cluster Innovation and Regional Innovation

Y. Zhao et al. explored how R&D alliance networks relate to knowledge attributes and organizational innovation capabilities based on a multilevel network approach. They studied 86 groups in a network of R&D alliances in five high-tech industries in China and found that centrality in R&D alliance networks plays a limited role in the relationship between knowledge attributes and organizational innovation capacity. B. Zhang et al. explored the upgrading process of China's automobile industry and provided a scientific basis for industrial policy adjustment and industrial structure optimization. F. Zhou and B. Zhang detected and visualized the Beijing-Tianjin-Hebei city cluster innovation community based on a patent cooperation network and concluded that Beijing-Tianjin-Hebei collaborative innovation should strengthen the cooperation between Tianjin and Hebei. Y. Su constructs a tripartite evolutionary game model with government, parent company, and subsidiary to study reverse knowledge transfer in cross-border M&A of Chinese high-tech industries.


## 3. Multicriteria Decision Making

A new CNPI team member selection model was constructed by J. Su et al. using fuzzy sets and social network analysis. They verified its validity with real cases. Y. Zhang et al. proposed an evolutionary game model from the perspective of behavioral deviance. It can provide help for corporate strategy selection.


## 4. Corporate Innovation and Growth

X. Yu et al. found that digital companies exhibit discontinuous growth as they move from existing core businesses to new ones. This growth pattern often ends in failure. This is mainly because companies devote most of their resources to maintaining the value network of their existing core business, which eventually leads to a “lock-in” effect. They use fractal theory to eliminate the threats posed by this phenomenon and provide effective countermeasures and suggestions for business growth.


## 5. Innovation Ecosystem

Y. Chen et al. discuss how to build a good innovation ecosystem, using the example of a representative Chinese artificial intelligence company, KDDI. W. Yang et al. analyzed the digital transformation of the innovation ecosystem in the era of digital economy.The above studies make effective use of complex system models. These studies can provide a more scientific, accurate, and comprehensive response to the internal interaction behaviors of social complex systems and provide contributions to the study of social complex systems.



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*Source: 9790151-2022-04-13.xml*