411
Views
18
CrossRef citations to date
0
Altmetric
Articles

A fuzzy mapping framework for risk aggregation based on risk matrices

ORCID Icon, ORCID Icon & ORCID Icon
Pages 539-561 | Received 26 Jan 2016, Accepted 12 Jul 2016, Published online: 26 Aug 2016
 

Abstract

Extant research has focused upon assessing individual risks with the aid of risk matrices. Although risk aggregation is an important issue in risk management, aggregation of risks measured by risk matrices remains unresolved despite the wide usage of risk matrices. This paper proposes a framework to resolve the problem. We start from modifying the two notions of non-aggregatability of risk matrices, namely, qualitative description of inputs and non-comparability of different types of consequences. Then, we explicate the strong connection between risk matrices and fuzzy sets and propose that the transformation from risk matrices to fuzzy sets clear some confusions encountered in the aggregation process. A framework which covers membership analysis, composing different risk and defuzzification of the aggregated membership function, is proposed to aggregate different risks. In the framework, we pay maximum attention to accurate estimation of memberships of different risks. Besides, technical problems of composition and defuzzification are solved in detail. At last, an illustrative example is presented to show the feasibility of our method where we report the ranking of the aggregated risks of different scenarios.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 420.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.