Data_Sheet_3_Computational Intelligence for Studying Sustainability Challenges: Tools and Methods for Dealing With Deep Uncertainty and Complexity.ZIP

The study of sustainability challenges requires the consideration of multiple coupled systems that are often complex and deeply uncertain. As a result, traditional analytical methods offer limited insights with respect to how to best address such challenges. By analyzing the case of global climate change mitigation, this paper shows that the combination of high-performance computing, mathematical modeling, and computational intelligence tools, such as optimization and clustering algorithms, leads to richer analytical insights. The paper concludes by proposing an analytical hierarchy of computational tools that can be applied to other sustainability challenges.

可持续性挑战的相关研究需考量诸多复杂且高度不确定的耦合系统。因此，传统分析方法在最优应对此类挑战的路径上，所能提供的洞察较为有限。本文以全球气候变化减缓议题为分析案例，研究表明：高性能计算、数学建模与计算智能（computational intelligence）工具（如优化算法、聚类算法）的结合，可产生更具深度的分析洞察。文末提出一套计算工具分析层级体系，可推广应用于其他可持续性挑战问题的研究。