Fuzzy Importance Measures for Ranking Key Interdependent Sectors Under Uncertainty

Abstract

In the field of reliability engineering, several approaches have been developed to identify those components that are important to the operation of the larger interconnected system. We extend the concept of component importance measures to the study of industry criticality in a larger system of economically interdependent industry sectors that are perturbed when underlying infrastructures are disrupted. We provide measures of (i) those industries that are most vulnerable to disruptions and (ii) those industries that are most influential to cause interdependent disruptions. However, difficulties arise in the identification of critical industries when uncertainties exist in describing the relationships among sectors. This work adopts fuzzy measures to develop criticality indices, and we offer an approach to rank industries according to these fuzzy indices. Much like decision makers with the knowledge of the most critical components in a physical system, the identification of these critical industries provides decision makers with priorities for resources. We illustrate our approach with an interdependency model driven by US Bureau of Economic Analysis data to describe industry interconnectedness.

A comparative approach of economic sectors in Sinaloa, Mexico, based on multicriteria decision aiding

“…Tounsi et al (2013), by using input-output analysis and the Unweighted Rassmussen Approach, performed the sectors of the Moroccan economy via the intensity of their links with other sectors. Oliva et al (2014) extended the concept of component importance measures to the study of industry criticality in a larger system of economically interdependent industry sectors and they developed measures of impact analysis to those industries. The proposed work provided an approach to rank industries according to fuzzy indices.…”

Critical clusters in interdependent economic sectors

“…In the literature a relevant effort has been spent in finding the most critical elements in a scenario composed of several tightly interconnected economic sectors or critical infrastructures (see, among others, [1][2][3]). Traditional approaches focus on finding the single sectors or infrastructures which are comparatively more vulnerable or critical to the whole system; however, to date, no satisfactory solution has been provided to find critical groups of elements or subsystems in the context of economic inputoutput analysis or critical infrastructure protection.…”

“…The analysis of strongly coupled clusters can be used to complement key sector analyses [1,3], allowing to identify key clusters.…”