Submission Deadline: 31 May 2022 (closed) View: 137
Complexity is an inherent attribute of the material world. With the development of science and the deepening of the exploration of the material world, the complexity of objective things themselves has become more and more obvious. In today's multi-polar coexistence of politics, diversified development of information, and rapid development of science and technology, the complexity of the social system is abounding, and it is more difficult to solve the problems. Due to the complexity of social practice, the complexity of people and their activities, the diversity of social system elements, the level of complexity, the diversity of social system goals and functions, and the existence of complexity outside the social system make the social system appear self-organizing, Openness, nonlinearity, emergence and other complex features. The traditional reductionist thinking cannot give a comprehensive and accurate explanation of the complex structure and complex relationships of the social system. The proposal of complexity science provides a new direction for the study of complex social systems. Based on the theory of complexity science, computer modeling and simulation as tools to study complex systems can effectively reveal the internal mechanism, operating mechanism, and evolutionary laws of social complex systems.
The root of the complexity of the social system lies in the existence of "adaptive subjects" with "learning" capabilities in the system. Such subjects can exhibit autonomous adaptive responses under external stimuli and the interaction of other subjects in an open system. They can continuously adjust their own behavior and state, and they are subjects with the ability to learn and reflect. In this interaction, the self-adaptive subject "accumulates experience" through continuous "learning" to change its own behavior and develop in a direction that is beneficial to itself, highlighting the subject's adaptability to the environment. Compared with traditional research methods, computer modeling and simulation can simulate complex systems more comprehensively and accurately. The rapid development of modern computer technology has made computer modeling and simulation an important research tool in many fields. It is widely used in engineering technology, new energy technology, aerospace, transportation, ecological environment, communication network, economics, management, mathematics, physics and other fields. Computer modeling and simulation can be based on the simulation model of the preset system, by running the specific simulation model and analyzing the computer output information, it can realize the comprehensive evaluation and prediction of the actual system operation status and change law, and then realize the real system Improvement or optimization of design and structure. Therefore, the application of computer modeling and simulation in social sciences can be further enriched, especially the application of complex social systems, in order to better promote the development of complex science, reveal the complex characteristics and laws of social systems, and promote social development.
This Special Issue aims to collate original research papers and research articles that report on recent advancements in application of Computer Modeling and Simulation in Social Complex System.
Potential topics include but are not limited to the following:
• Application of multi-agent modeling in regional innovation system.
• Application of multi-agent modeling in green innovation system.
• Application of multi-agent modeling in sustainable development system.
• Application of multi-agent modeling in transportation system.
• Application of intelligent optimization algorithm in innovation ecosystem.
• Application of intelligent optimization algorithm in crisis response system.
• Application of machine learning in project management.
• Application of machine learning in innovation cooperation network.
• Application of SIR model in knowledge innovation system.
• Application of SIR model in network public opinion management system.
• Application of BP neural network model in risk control system.
• Application of BP neural network model in financial system.
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