Magnus is a scientist at CSIRO Land and Water and his current research focus is on urban transformations building on a systems perspective. In this area he has researched transitions to Sustainable Urban Water Management; the socio-technical processes that lead to diffusion of technology, innovation and behavior within a city, and how strategic conversations and scenario planning can support urban governance that better account for complexity, tradeoffs and uncertainty.



Mental models of cities and their relevance to urban resilience
Volume: 20, Issue 1
We believe that cities are important for humans as essential forms of social organization 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 'livability' 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-centralized 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 organizations to better handle the complexity of their cities. To enable this, further work is required to identify suitable urban systems archetypes.