Leena Ilmola

Approaches to Measurement of Urban Resilience

Measurement is a prerequisite for systematic development. Resilience measurement approaches have been developed for assessment, planning and follow up resilience development. In this chapter I will review several different resilience assessment systems that either measure resilience performance (past incident and the urban system’s reaction in that) or resilience as competence (city’s perceived capability to adapt, recover and benefit of shocks). The methods analyzed are Rockefeller Foundations 100 resilient cities measurement framework, UN Habitat disaster measurement system, New Zeland based method, the system produced by the Strategy Alliance and the method developed in the Global X Network. None of these approaches are ‘good’ or ‘bad’ but they have been developed for a specific purpose, they have different objectives, principles, methods and data used. The analysis framework consisted of five systems theoretical dimensions: structure, interaction, coordination, renewal and resources. The analysis revealed that the approaches can be divided into three clusters; firstly survey based method that collect perceptions, second existing statistical data based methods and third multimethod approaches. One of the main conclusions was that none of these methods paid any or thorough attention on interaction between urban system components. Even if the methods try to assess resilience, the main source of adaptation—interaction dynamics—is not covered. But even if the existing resilience measurement methods have weaknesses, I think that the comparison presented in this chapter provides resilience developers a conceptual framework for assessment criteria for deciding which method they should use.

Computational Framework of Resilience

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.

Soft Social Systems and Shocks: An Experiment with an Agent Based Model

In this chapter, we will elaborate on the challenge of uncertainty emerging from increasing complexity and how to deal with that in decision making. We will present an example of a decision making tool that supports the analysis of the potential futures and provides a decision maker with an idea of the proper actions to be taken.