Machine Learning in Social Complex Systems

Social systems are all complex. Specifically, any social system has the following characteristics: First, it has spontaneous order, where social orders can be generated from the behavior of a combination of self-interested individuals who are not intentionally trying to create order through planning. Second, social systems have nonlinear dynamics. The system exhibits highly sensitive behavior depending on initial conditions.

Machine learning, as part of artificial intelligence, is the study of computer algorithms that can improve automatically through experience and by the use of data. Therefore, it is a powerful tool to discover hidden laws in complex systems, which usually have spontaneous order and nonlinear dynamics.

Previous works show that machine learning has very interesting and fruitful findings in both natural and social complex systems. Frontier in Physics has already had a good summary of machine learning in natural complex systems. Social systems are more complex than natural systems because human behaviors cannot be abstracted into identical elements, which gives more potential for machine learning techniques to bring light to fundamental laws in major social systems. This is exactly what this Research Topic targets. Specifically, topics of interest include but are not limited to:
• Social complex networks analysis through machine learning approaches
• Discovery of dynamics in social complex systems by machine learning methods
• Data-driven investigation on intelligent transportation, financial market, epidemic spreading, and other social systems

Topic Editor Dr. Wanfeng Yan is a Partner of Zhicang Technologies. The other Topic Editors declare no competing interests with regard to the Research Topic subject.