IRGSCIMA · Integrated Risk Governance via Spatial Complexity Inspired Models and Algorithms

Based on the concept of Spatial Complexity Inspired Models and Algorithms (SCIMA), this proposed research aims to develop a methodological platform of models and algorithms for the study of Integrated Risk Governance (IRG) problems. The methods to be developed will take into account important spatial and/or temporal features in real-world risk systems, and integrate risk management considerations into the model development stage, in order to develop more effective and efficient tools to analyze risk systems and improve risk management.The overall aim of this study is to, through intensive, systematic training and study of IRG knowledge, develop appropriate SCIMA methods as a natural form of representation for IRG problems that has good compatibility with optimization algorithms. This will be achievable based on two overarching hypotheses: (i) Efficiency, robustness and even intelligence may exist in many natural spatial processes and phenomena, and learning from nature will lead to innovative modelling and algorithms for complex systems such as IRG problems