The objects of knowledge systems and innovation systems are all complex systems composed of self-organized heterogeneous subjects, which combine the characteristics of self-organization, adaptability, non-linearity, and emergence. The complexity characteristics of knowledge systems and innovation systems make it difficult to explore the operating mechanism and to evaluate the degree of systems’ sustainable development. Reductionism is applicable to the simple linear system; however, it cannot explain the emergence of multi-agent and multi-element dynamic interaction behaviour. With the effective application of complex system theory in natural science research fields, an increasing number of scholars also regard it as an effective tool for the knowledge and innovation management research field. The application of complex sciences in the field of natural sciences is impressive, but its application in knowledge and innovation management is still in its infancy. Many studies are still at the level of concept and qualitative analysis, while quantitative analysis and model building are just emerging in recent years.

With the development of system theory, new theories such as quantum theory, synergetic theory, dissipative structure theory, and catastrophe theory have become effective tools for studying complex systems. They can solve complex characteristics which cannot be explained by reductionism. The dynamic interaction between intelligent individuals is an important feature of complex systems such as knowledge systems and innovation systems. It is a non-linear open system, which can exchange information and energy with the outside world. Complex system theory can use the energy level transition model to explain the causes of knowledge innovation and technological orbital transitions. Through this metaphor, the nonlinear system model is constructed to find the key order parameter of the system. It can be used to study how self-organizing complex systems break through the threshold to achieve the ordered equilibrium state from the chaotic state. It can explain the sudden qualitative change of the system due to the continuous change of external elements of the system. Complex system models can effectively describe knowledge systems’ and innovation systems’ character of self-organization, adaptability, non-linearity, and emergence. It is helpful to explain the complex environment in the innovation management research field and reveal its essence and law. Therefore, the application of complex system models to solve the problems in knowledge and innovation management research is conducive to the in-depth study of social science and promotes the development of knowledge and innovation management.

This Special Issue aims to collate original research articles that report on recent advancements in complex system models and their application in knowledge systems and innovation systems, and practical achievements in the broad field. Review articles are also welcomed.

Potential topics include but are not limited to the following:

• Application of complex system models in innovation ecosystems
• Quantum theory and its application in knowledge transfer
• Application of synergetic theory in knowledge systems
• Complex applicable system and its application in knowledge alliance
• Application of social network analysis in social innovation
• Application of acoustic wave propagation model in knowledge innovation
• Catastrophe theory and its application in green technology innovation
• Hydrodynamic model and its application in knowledge creation
• Application of system dynamics model in sustainable development
• Dissipative structure theory and its application in innovation networks
• Chaos theory and its application in regional innovation systems
• Fractal theory and its application in innovation management
• Ecological niche model in enterprise knowledge innovation