scholarly journals An Evolution Analysis of Collaborative Innovation Network considering Government Subsidies and Supervision

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Yingying Xu ◽  
Liangqun Qi ◽  
Xichen Lyu ◽  
Xinyu Zang
Keyword(s):  
Evolutionary Game ◽  
Innovation Networks ◽  
Innovation Network ◽  
Scale Free Network ◽  
Government Subsidies ◽  
Scale Free ◽  
Evolution Analysis ◽  
Collaborative Innovation ◽  
Free Network ◽  
The Government

Collaborative innovation networks have the basic attributes of complex networks. The interaction of innovation network members has promoted the development of collaborative innovation networks. Using the game-based theory in the B-A scale-free network context, this paper builds an evolutionary game model of network members and explores the emergence mechanism from collaborative innovation behavior to the macroevolution of networks. The results show that revenue distribution, compensation of the betrayer, government subsidies, and supervision have positively contributed to the continued stability of collaborative innovation networks. However, the effect mechanisms are dissimilar for networks of different scales. In small networks, the rationality of the revenue distribution among members that have similar strengths should receive more attention, and the government should implement medium-intensity supervision measures. In large networks, however, compensation of the betrayer should be attached greater importance to, and financial support from the government can promote stable evolution more effectively.

scholarly journals The Evolutionary Game Theoretic Analysis for Sustainable Cooperation Relationship of Collaborative Innovation Network in Strategic Emerging Industries

2018 ◽  
Vol 10 (12) ◽  
pp. 4585 ◽  
Author(s):  
Weiwei Liu ◽  
Jianing Yang
Keyword(s):  
Financial Incentives ◽  
Evolutionary Game ◽  
Innovation Network ◽  
Theory Approach ◽  
Non Profit ◽  
Emerging Industries ◽  
Research Institutes ◽  
Collaborative Innovation ◽  
The Government ◽  
Cooperation Relationship

Strategic emerging industries (SEIs) represent the future direction of industrial developments and are crucial in stimulating the overall and long-term development for economy and society. The government plays a key role in promoting the development of SEIs. This paper, for the first time, investigates the cooperation relationship among innovation members, such as enterprises, universities and research institutes in a collaborative innovation network of strategic emerging industries under government intervention. A three-population evolutionary game theory approach was employed under different scenarios for the government acting as the stakeholder, considering the non-profit, definite fit as well as uncertain profit when incentive and punishment policies are adopted. A novel evolutionary game model of the cooperation relationship among collaborative innovation network members under government’s intervention is established. The results of the simulation experiments show that government’s intervention significantly influences the cooperation relationship between enterprises, universities and research institutes. When the sum of financial incentives and punishments is greater than the total additional cost (TAC), enterprises, universities and research institutes should pay for collaborative innovation. Moreover, government’s financial intervention can effectively promote the cooperation between enterprises, universities and research institutes.

The influence of extortion strategies on the evolution of cooperation in the scale-free network

2018 ◽  
Vol 32 (30) ◽  
pp. 1850334 ◽  
Author(s):  
Ai-Zhong Shen ◽  
Jin-Li Guo ◽  
Jun-Fang Wang ◽  
Qi Suo
Keyword(s):  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Evolution Of Cooperation ◽  
Uniform Structure ◽  
Model Parameters ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
Cooperative Behaviors ◽  
The Stability

Extortion strategies can unilaterally transcend any opponent’s expected payoffs and promote cooperative behaviors in an iterated prisoner’s dilemma game. However, extortion strategies have the evolutionary instability if the players game with uniform structure. In this paper, we study the influence of extortion on the evolution of cooperation in the scale-free network with the player’s game payoffs calculated by average payoffs and the strategy update rule according to the replicator dynamics rule. Firstly, we study the stability of evolutionary game results after introducing the extortion strategy and the influence of evolution extortion on cooperation. In addition, we compare the results of our model with the donation games of the accumulated payoff in the BA networks. Moreover, we study the influence of the model parameters on game results. The results show that extortion can form long-term stable relationships with neighbors and the average payoffs’ inhibiting effect of cooperative behaviors disappear after introducing the extortion strategies in the scale-free network. The smaller value of the extortion actor and the benefit factor have a greater effect on the stability density of the strategies but the initial strategy density does not.

scholarly journals Stay with your community: Bridges between clusters trigger expansion of COVID-19

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242766
Author(s):  
Yukio Ohsawa ◽  
Masaharu Tsubokura
Keyword(s):  
Time Series ◽  
Real Life ◽  
Urban Life ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Government ◽  
The Impact ◽  
Second Wave ◽  
Infection Spread

In this study, the spread of virus infection was simulated using artificial human networks. Here, real-space urban life was modeled as a modified scale-free network with constraints. To date, the scale-free network has been adopted for modeling online communities in several studies. However, in the present study, it has been modified to represent the social behaviors of people where the generated communities are restricted and reflect spatiotemporal constraints in real life. Furthermore, the networks have been extended by introducing multiple cliques in the initial step of network construction and enabling people to contact hidden (zero-degree) as well as popular (large-degree) people. Consequently, four findings and a policy proposal were obtained. First, “second waves” were observed in some cases of the simulations even without external influence or constraints on people’s social contacts or the releasing of the constraints. These waves tend to be lower than the first wave and occur in “fresh” clusters, that is, via the infection of people who are connected in the network but have not been infected previously. This implies that the bridge between infected and fresh clusters may trigger a new spread of the virus. Second, if the network changes its structure on the way of infection spread or after its suppression, a second wave larger than the first can occur. Third, the peak height in the time series of the number of infected cases depends on the difference between the upper bound of the number of people each member actually meets and the number of people they choose to meet during the period of infection spread. This tendency is observed for the two kinds of artificial networks introduced here and implies the impact of bridges between communities on the virus spreading. Fourth, the release of a previously imposed constraint may trigger a second wave higher than the peak of the time series without introducing any constraint so far previously, if the release is introduced at a time close to the peak. Thus, overall, both the government and individuals should be careful in returning to society where people enjoy free inter-community contact.

Research on new evolution model of scale-free network

2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO
Keyword(s):  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

scholarly journals Dynamic Evolution Model of a Collaborative Innovation Network from the Resource Perspective and an Application Considering Different Government Behaviors

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 138 ◽  
Author(s):  
Wu ◽  
Shao ◽  
Feng
Keyword(s):  
Network Evolution ◽  
Evolution Model ◽  
Dynamic Evolution ◽  
Innovation Network ◽  
Transfer Policy ◽  
Collaborative Innovation ◽  
Innovation Output ◽  
The Government ◽  
Small Change ◽  
Innovative Behaviors

The evolution of a collaborative innovation network depends on the interrelationships among the innovation subjects. Every single small change affects the network topology, which leads to different evolution results. A logical relationship exists between network evolution and innovative behaviors. An accurate understanding of the characteristics of the network structure can help the innovative subjects to adopt appropriate innovative behaviors. This paper summarizes the three characteristics of collaborative innovation networks, knowledge transfer, policy environment, and periodic cooperation, and it establishes a dynamic evolution model for a resource-priority connection mechanism based on innovation resource theory. The network subjects are not randomly testing all of the potential partners, but have a strong tendency to, which is, innovation resource. The evolution process of a collaborative innovation network is simulated with three different government behaviors as experimental objects. The evolution results show that the government should adopt the policy of supporting the enterprises that recently entered the network, which can maintain the innovation vitality of the network and benefit the innovation output. The results of this study also provide a reference for decision-making by the government and enterprises.

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