A Distance-Based Approach to Fuzzy Cognitive Maps Using Pythagorean Fuzzy Sets

Abstract

Fuzzy Cognitive Maps (FCMs) have been attracting researchers from a wide application area due to being easy to apply and interpret. Since its proposal, the method has been improved to satisfy the diverse needs of practitioners such as solving different types of problems and representing particular types of uncertainty. The classical FCMs depend highly on the decision-maker judgments and the uncertainty inherent in the judgments deserves significant attention. Although there are several fuzzy extensions integrated into FCMs, the uncertainty caused by the lack of knowledge, the hesitancy of decision makers, and also the limited capacity of humans to deal with pre-defined rules should be considered. To address this issue, a new distance-based approach integrating Pythagorean Fuzzy Sets and FCMs is proposed. To the best of our knowledge, this is the first time this extension is integrated into FCMs. Besides allowing to represent the uncertainty until the end of the calculations, the new approach offers decision makers an easier and more flexible way to assess the strength of existing causal relationships. To provide a comparison between the proposed approach and the classical FCMs, two real-life applications are selected as case studies.