"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; (ii) Separating the modelling and the design of algorithms in two isolated stages is the main cause of ineffectiveness and inefficiency in the optimization of complex systems, and therefore incorporating the compatibility to optimization algorithms into the modelling stage of complex systems may bring ground breaking results. Under the above two hypotheses, the proposed study has two major research themes. One is, inspired by natural ripple-spreading phenomenon, to design some problem-specific RSMA methods for some risk systems, the other is a spatial extension of temporal RHC to develop an more effective risk management strategy in terms of optimality and scalability. They are two distinct yet complementary strands to this proposal."