Mental models of cities and their relevance to urban resilience

Magnus Moglia, Fabio Boschetti, Nicola Grigg, Iain WALKER

Research output: Contribution to journalEditorial

Abstract

We believe that cities are important for humans as essential forms of social organisation in contemporary human life. Currently, the integrity of cities as enduring systems faces many challenges — ‘exogenous’ factors such as unsustainable consumption of energy and other resources and ‘endogenous’ factors such as ‘liveability’ and the ‘human scale’ of cities. Therefore we must
work to ensure their future, hence the emerging importance of the concept of resilience. But how do we ensure the future of cities? Current slow, de-centralised and business-as-usual urban development is problematic. Instead, a planned approach to urban development is necessary, but how do we plan for cities to be resilient? Planning must inevitably rest on an understanding of how a city functions, and this leads us to thinking of developing mental or computational models of cities. In this paper we explore a number of mental models of cities, which could form the basis for directed urban planning. We identify three types of urban models, urban-state models, urban-learning models, and urban-systems models. Furthermore, we argue that all the current urban models are piecemeal and/or impractical and either do not adequately consider the complexity of the
city or are not suitable for the interface with governance, We suggest that the best way forward is to embed multiple urban models within an adaptive governance framework, thereby providing a way for urban decision makers and planning
organisations to better handle the complexity of their cities. To enable this, further work is required to identify suitable urban systems archetypes.
Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalEmergence: Complexity and Organization
Publication statusPublished - 2018

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Resilience
Mental models
Governance
Urban system
Factors
Urban development
Integrity
Archetypes
Energy
Resources
Computational model
Decision maker
Urban planning
Social organization
Learning model
Planning
System model

Cite this

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title = "Mental models of cities and their relevance to urban resilience",
abstract = "We believe that cities are important for humans as essential forms of social organisation in contemporary human life. Currently, the integrity of cities as enduring systems faces many challenges — ‘exogenous’ factors such as unsustainable consumption of energy and other resources and ‘endogenous’ factors such as ‘liveability’ and the ‘human scale’ of cities. Therefore we mustwork to ensure their future, hence the emerging importance of the concept of resilience. But how do we ensure the future of cities? Current slow, de-centralised and business-as-usual urban development is problematic. Instead, a planned approach to urban development is necessary, but how do we plan for cities to be resilient? Planning must inevitably rest on an understanding of how a city functions, and this leads us to thinking of developing mental or computational models of cities. In this paper we explore a number of mental models of cities, which could form the basis for directed urban planning. We identify three types of urban models, urban-state models, urban-learning models, and urban-systems models. Furthermore, we argue that all the current urban models are piecemeal and/or impractical and either do not adequately consider the complexity of thecity or are not suitable for the interface with governance, We suggest that the best way forward is to embed multiple urban models within an adaptive governance framework, thereby providing a way for urban decision makers and planningorganisations to better handle the complexity of their cities. To enable this, further work is required to identify suitable urban systems archetypes.",
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Mental models of cities and their relevance to urban resilience. / Moglia, Magnus; Boschetti, Fabio; Grigg, Nicola; WALKER, Iain.

In: Emergence: Complexity and Organization, 2018, p. 1-6.

Research output: Contribution to journalEditorial

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