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用于研究可持续发展挑战的计算智能：应对深度不确定性和复杂性的工具与方法。

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

作者信息

Molina-Perez Edmundo, Esquivel-Flores Oscar A, Zamora-Maldonado Hilda

机构信息

Tecnologico de Monterrey, Escuela de Ciencias Sociales y Gobierno, Monterrey, Mexico.

Institute for Research in Applied Mathematics and Systems, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico.

出版信息

Front Robot AI. 2020 Sep 17;7:111. doi: 10.3389/frobt.2020.00111. eCollection 2020.

DOI:10.3389/frobt.2020.00111
PMID:33501278
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7805846/
Abstract

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.

摘要

对可持续发展挑战的研究需要考虑多个相互关联的系统，这些系统往往很复杂且具有高度不确定性。因此，传统分析方法对于如何最好地应对此类挑战的见解有限。通过分析全球气候变化缓解的案例，本文表明，高性能计算、数学建模以及计算智能工具（如优化和聚类算法）的结合能带来更丰富的分析见解。本文最后提出了一个可应用于其他可持续发展挑战的计算工具分析层次结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/61cdc32ec14d/frobt-07-00111-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/612bcea9d473/frobt-07-00111-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/00f20022944b/frobt-07-00111-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/ca94033f9a27/frobt-07-00111-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/954cf7e40727/frobt-07-00111-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/461dc2a45d31/frobt-07-00111-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/924d8214fcca/frobt-07-00111-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/4f4f02a4d7c6/frobt-07-00111-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/24aafa9395c2/frobt-07-00111-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/61cdc32ec14d/frobt-07-00111-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/612bcea9d473/frobt-07-00111-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/00f20022944b/frobt-07-00111-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/ca94033f9a27/frobt-07-00111-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/954cf7e40727/frobt-07-00111-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/461dc2a45d31/frobt-07-00111-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/924d8214fcca/frobt-07-00111-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/4f4f02a4d7c6/frobt-07-00111-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/24aafa9395c2/frobt-07-00111-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afb8/7805846/61cdc32ec14d/frobt-07-00111-g0009.jpg

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