Journal of Computing and Natural Science

A Critical Analysis on Complex Urban Systems and Complex Systems Theory

Journal of Computing and Natural Science

Received On : 08 June 2022

Revised On : 20 July 2022

Accepted On : 08 September 2022

Published On : 05 January 2023

Volume 03, Issue 01

Pages : 024-034


Deep neural complexity theory has recently received new attention, particularly in the study of climate and the environment. According to the majority of the research on urban climate resilience, cities are complex adaptive systems, and as such, urban governance and design should take cues from the study of complex adaptive systems. This means that climate change governance, in order to mitigate the problems presented by climate change's unpredictability, has to be flexible, participatory, and adaptive. This article provides a critical literature review on the topic of Complex Urban Systems, i.e., climate change governance in the context of complexity theory. The paper argues that the current hype around complexity theory exaggerates the theory's relevance. Complexity theory falls short in explaining urbanization and environmental change since they are highly contested social phenomena. However, it serves a significant purpose in bringing attention to the uncertainty realities in the process of policy-making, which are certainly fundamental in the context of climate change, including the changing ecologies on which cities rely. Many critics of complexity theory point out that it tends to showcase urban developments are happening through neutral evolutionary forces, which can be comprehended, and governed by individuals engaged in governance for a particular objective.


Complexity Theory, Complex Urban Systems, Complex Adaptive Systems

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Cite this article

Madeleine Wang Yue Dong, “A Critical Analysis on Complex Urban Systems and Complex Systems Theory”, Journal of Computing and Natural Science, vol.3, no.1, pp. 024-034, January 2023. doi: 10.53759/181X/JCNS202303003.


© 2023 Madeleine Wang Yue Dong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.