Abstract
Many researchers have been studying the resilience in urban cities. However, due to the complexity of the system involving human activities, it is difficult to define the resilience of an urban area quantitatively. We introduce an abstract model that represents an urban system through a set of variables and a utility function (or dually, a cost function) evaluating the “quality” of the states of the variables. This cost function depends on the criterion of interest for evaluating the resilience of the system, and can be easily defined in a succinct way. Then, our contribution is mainly twofold. First, we propose several performance metrics that evaluate how resilient a given system has been in some specific scenario, that is, in the past. Second, assuming we are given some knowledge about the dynamics of the system, we model its possible evolutions by embedding it into a discrete state transition machine, and show how we can adapt the performance metrics to this framework to predict the resilience of the system in the future. Such an adaptation of a performance metric to our dynamic model is called here a performance-based competency metric. This new kind of metric is useful to validate existing competency metrics (Ilmola in Competency metric of economic resilience. Urban resilience: a transformative approach. Springer, 2016) by aligning these competency metrics with our performance-based ones.
Keywords
- Gross Domestic Product
- System State
- Unemployment Rate
- Performance Metrics
- Urban System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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- 1.
Given a set E, the notation #E stands for the number of elements in E.
- 2.
Used in the sense of system dynamics (Scheffer and Carpenter 2003) a “regime” is the set of states that define a domain of attraction. In a regime the system has the same essential structure, function, feedbacks and, therefore, identity (Walker et al. 2004). A regime shift occurs when a system crosses a threshold into an alternate domain of attraction.
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Schwind, N., Minami, K., Maruyama, H., Ilmola, L., Inoue, K. (2016). Computational Framework of Resilience. In: Yamagata, Y., Maruyama, H. (eds) Urban Resilience. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-39812-9_12
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DOI: https://doi.org/10.1007/978-3-319-39812-9_12
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