Analysis of the multi-agent's relationship in collaborative innovation network for science and technology SEMs based on evolutionary game theory

Author(s):  
Haiju Hu ◽  
Guangyou Nan ◽  
Jinyu Wei
2019 ◽  
Vol 12 (1) ◽  
pp. 75 ◽  
Author(s):  
Xiaodan Kong ◽  
Qi Xu ◽  
Tao Zhu

Knowledge sharing behavior based on the cluster innovation network has become the primary measure for enterprises to realize sustainable innovation. In order to promote the proactive knowledge sharing behavior among enterprises in the long term, the dynamic evolutionary process and law of knowledge sharing in the network need to be further studied. As different from the hypothesis of the rational man in the classical game theory, this paper establishes an evolutionary game model of knowledge sharing behavior in the cluster innovation network based on the evolutionary game theory, and discusses how the bounded rational enterprises can achieve the evolutionary equilibrium through continuously adaptive learning and strategy optimization, further explores the influence factors on the evolutionary trajectory. Combined with mathematical derivation and simulation analysis, the following results are obtained: over time, the dynamic evolution of knowledge sharing behavior in the cluster innovation network is influenced by initial states of the system, but can always reach the evolutionary stable equilibrium; factors such as synergy revenue have a positive impact on the evolutionary results, while factors such as opportunity interest have a negative impact on the evolutionary results; the factor of revenue distribution has a U-shape relationship with the evolutionary results, and the factor of direct revenue has no effect on the results. The results are expected to have an implication for improving the sustainable innovation development of enterprises in the cluster innovation network.


Author(s):  
Weiwei Liu ◽  
Jianing Yang ◽  
Kexin Bi

The innovation of the biomedical engineering (BME) industry is inseparable from its cooperation with medical institutions. China has considerable medical institutions. Although private hospitals account for more than half of Chinese medical institutions, they rarely participate in biomedical engineering industry innovation. This paper analyzed the collaborative relationship among biomedical engineering enterprises, universities, research institutes, public hospitals and private hospitals through evolutionary game theory and discussed the influence of different factors on the collaborative innovation among them. A tripartite evolutionary game model is established which regards private hospitals as a stakeholder. The results show that (1) the good credit of private hospitals has a positive effect on their participation in collaborative innovation; (2) it is helpful for BME collaborative innovation to enhance the collaborative innovation ability of partners; (3) the novelty of innovation projects has an impact on BME collaborative innovation. The specific impacts depend on the revenue, cost and risk allocation ratio of innovation partners; (4) the higher the practicability of innovation projects, the more conducive to collaborative innovation.


2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Zhu Bai ◽  
Mingxia Huang ◽  
Shuai Bian ◽  
Huandong Wu

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.


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