Exploring Dimensions of Resilience in Complex Systems

5.2. Resilience dimensions.

According to Asprone et al (2015) two approaches can be adopted when examining the concept of resilience to complex systems, such as cities,: (1) the resilience of ecosystems and (2) engineering resilience. In the first approach, developed by Holling (1973, 1986, 2001), resilience can be defined as “the ability of a system in dynamic equilibrium that is subjected to external shocks to move to a different stage of dynamic equilibrium”. Engineering resilience, on the other hand, developed by Pimm (1984) and Bruneau et al. (2003), can be defined as the ability of a system to assimilate an external shock and returning short time to its pre-shock stage.

Resilience for both physical and social systems can be further defined

Policy formulation.

Policy formulation involves recognizing and/or crafting a set of alternatives to solve a problem or improve an identified situation, and restrict the available solutions in preparation for an optimal set of decisions.

According to Cochran and Malone, policy formulation takes up the “what” questions: “What is the plan for dealing with the problem? What are the goals and priorities? What options are available to achieve those goals? What are the costs and benefits of each of the options? What externalities, positive or negative, are associated with each alternative?” (1999, 46). This focus to policy formulation, assumes that stakeholders in the policy process have discerned and delimited a problem or examined situation, and moved it onto a public agenda. Formulating the set of options thus involves selecting a range of extensive approaches to a problem or analyzed situation, and then identifying and designing the specific sets of tools for boarding each approach. It comprises making a blueprint of the legislative or regulatory language for each option—that is, describing the instruments (for example., sanctions, awards, prohibitions, rights, and the like) and enunciating when they will become activated and to whom or to what they will be applied. Making a choice of smaller set of possible solutions from among those from which decision makers actually will choose involves applying some set of criteria to the alternatives(e.g. judging their feasibility,

2005; Vaziri et al. 2010). These models “are applications of mathematical programming techniques to minimize the expected net present value of the costs of a regional mitigation strategy”. Application\ of this type of model has been summarized by Davidson et al. (2005) and Vaziri (2009) for assessing equity issues associated with mitigation investments. In recognizing the need for such analysis, a scenario approach for a particular earthquake and a measure of wealth distribution are required (Davidson et al.