Discrete dynamic modeling is an invaluable tool for us to understand the relationship between components of a complex system and to capture the multilevel dynamics of any large complex dynamic system under dynamic external control. The state of a complex system signifies the aggregate dynamics and overall trend of multiple changing parameters.

Lots of quantitative information is needed to determine the system state, but it is difficult to obtain quantitative information from many real-world complex systems. Therefore, the modeling of discrete dynamic complex systemss in the absence of quantitative information has become a research hotspot. In the real world, complex systems cover various fields, including nature, engineering, biology, economy, management, politics, and society. The testing and monitoring of actual complex systems often generate a substantial amount of data. Hence, the quantitative analysis of big data helps to determine the system parameters for discrete dynamic modeling. However, it is no easy task to extract quantitative information out of the large amount of data of complex systems. Therefore, the combination of discrete dynamic modeling with big data presents both opportunities and challenges. Interdisciplinary efforts are imperative for scholars in physics, mathematics, computer science, and complex systems, to name but a few.

This Special Issue aims to bring together original research and review articles addressing the existing and emerging problems in the theory and practice of discrete dynamic modelling of complex systems based on big data. Being aware of the interdisciplinary nature of the problem-solving process, we encourage methodological pluralism and welcome the attempt of multiple approaches, namely, empirical studies, data analysis, agent-based modeling, as well as simulation models.

Potential topics include but are not limited to the following:

• Big data analytics for discrete dynamic modelling
• Complex system modeling based on digital twin
• Data-driven discrete dynamic model
• Data mining in discrete dynamic complex systems
• Discrete methods for the simulation of complex systems
• Evolutionary computing in discrete dynamic complex system
• Qualitative simulation modeling of complex systems