City planners and urban policy makers require simulation models to understand, predict, design and manage urban areas so that cities can become more sustainable, equitable and efficient. Recently, the idea that one might build ‘digital twins’ of cities has captured the imagination of many scientists, engineers and policy makers. To unleash the full potential of data, science, and technology, such an approach requires a clear idea of how similar a digital twin would have to be to the system of interest and in what way. We thus argue that we urgently need theories and methods from complexity science to guide the development and use of digital twins. Different applications-such as the avoidance of traffic congestion or the simulation of emergent social segregation-may actually require different kinds of data and different kinds of twins. Hence, the complexity science approach considers different perspectives on cities which-to some extent-evolve and self-organize themselves like living systems.
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