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Planning and complexity: Engaging with temporal dynamics, uncertainty and complex adaptive systems

Ulysses Sengupta et al.

Environment and Planning B: Urban Analytics and City Science2016https://doi.org/10.1177/0265813516675872article
ABDC A*
Weight
0.53

What the paper says

The nature of complex systems as a transdisciplinary collection of concepts from physics and economics to sociology and ecology provides an evolving field of inquiry (Laszlo and Krippner, 1998) for urban planning and urban design.As a result, planning theory has assimilated multiple concepts from the complexity sciences over the past decades.The seemingly chaotic or non-linear urban phenomena resulting from the combination of hard and soft systems (Checkland, 1989) or physical and environmental aspects of the city with human intervention, motivation and perception have been of particular interest in the context of increasing criticism of top-down approaches.Processes such as self-organisation, temporal dynamics and transition, previously ignored or assumed problematic within equilibrium centred conceptualisations or mechanistic theories, have found their way back into planning through complexity theories of cities (CTC) (Allen, 1997;Batty, 2007;De Roo and Silva, 2010;Marshall, 2012; Portugali, 2011b).While there is an overlap with Structuralist-Marxist and

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https://doi.org/https://doi.org/10.1177/0265813516675872

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@article{ulysses2016,
  title        = {{Planning and complexity: Engaging with temporal dynamics, uncertainty and complex adaptive systems}},
  author       = {Ulysses Sengupta et al.},
  journal      = {Environment and Planning B: Urban Analytics and City Science},
  year         = {2016},
  doi          = {https://doi.org/https://doi.org/10.1177/0265813516675872},
}

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Planning and complexity: Engaging with temporal dynamics, uncertainty and complex adaptive systems

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Evidence weight

0.53

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.46 × 0.4 = 0.19
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.