Computational Intelligence for Studying Sustainability Challenges: Tools and Methods for Dealing With Deep Uncertainty and Complexity - Molina-Perez, Edmundo; Esquivel-Flores, Oscar A.; Zamora-Maldonado, Hilda (Frontiers in Robotics and AI, 2020)
The study of sustainability 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 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 other sustainability challenges.