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.
We analyze four scenarios commonly encountered in social processes undergoing competitive pressures: resource depletion by individuals acting greedily (‘tragedy of the commons’), wasted opportunity due to over protective players (‘tragedy of the anti-commons’), crowd following (‘majority wins’) and competition for niches (‘minority wins’). We show that these scenarios are extremes of a continuous resource exploitation problem and that complex and counter-intuitive behaviors are found at the transitions between ‘pure’ scenarios. We discuss the likely community behaviors and under what conditions a centralised management intervention may play a role in the resource and community resilience.
This paper presents a discussion of the possible influence of incomputability and the incompleteness of mathematics as a source of apparent emergence in complex systems. The suggestion is made that the analysis of complex systems as a specific instance of a complex process may be subject to inaccessible ‘emergence’. We discuss models of computation associated with transcending the limits of traditional Turing systems, and suggest that inquiry into complex systems in the light of the potential limitations of incomputability and incompleteness may be worthwhile.