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A platform for research: civil engineering, architecture and urbanism
Planning and complexity:Engaging with temporal dynamics, uncertainty and complex adaptive systems
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 humanistic perspectives (Portugali, 2011c) and a continuity from an older science of cities (Batty, 2013), it is interesting to observe the engagement with bottom-up phenomena, structural and functional co-evolution and resultant adaptable and self-organisational systems within complexity planning. It has taken time for planning to adopt complexity thinking beyond metaphor or common usage of the term, but we now appear to be at a tipping point where complexity planning is exploring methods of engagement and cognition, rather than the question of whether cities are complex.
Planning and complexity:Engaging with temporal dynamics, uncertainty and complex adaptive systems
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 humanistic perspectives (Portugali, 2011c) and a continuity from an older science of cities (Batty, 2013), it is interesting to observe the engagement with bottom-up phenomena, structural and functional co-evolution and resultant adaptable and self-organisational systems within complexity planning. It has taken time for planning to adopt complexity thinking beyond metaphor or common usage of the term, but we now appear to be at a tipping point where complexity planning is exploring methods of engagement and cognition, rather than the question of whether cities are complex.
Planning and complexity:Engaging with temporal dynamics, uncertainty and complex adaptive systems
Sengupta, Ulysses (author) / Rauws, Ward S. (author) / de Roo, Gert (author)
2016-11-01
Sengupta, U, Rauws, W S & de Roo, G 2016, 'Planning and complexity : Engaging with temporal dynamics, uncertainty and complex adaptive systems', Environment and planning b-Planning & design, vol. 43, no. 6, pp. 970-974. https://doi.org/10.1177/0265813516675872
Article (Journal)
Electronic Resource
English
Planning and complexity: Engaging with temporal dynamics, uncertainty and complex adaptive systems
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