What is it about?
Due to the interaction among attributes, such as complementarity and repeatability, the classical weighted arithmetic mean method is often invalid in the process of multi-attribute decision making (MADM) . In this paper, the proposed method defines the interactivity between attributes by using the super-modular game theory, so that the interaction be-tween attributes is easier to explain and understand, which lays a solid foundation for experts to qualita-tively assess the interactivity between attributes. The proposed method allows the experts to assess the interactivity between attributes by using the IFN, which better preserves the assessment information of experts and embodies the fuzziness and hesitation of interactivity assessment. Furthermore, the pro-posed method uses the IFWA to aggregate the opin-ions of all experts, which considers the weights of experts, thereby ensuring the rationality of decision-making. In addition, the proposed method uses the score function of IFN to define and calculate the intuitionistic fuzzy interaction degree between at-tributes, so the transformation from qualitative de-scription to quantitative characterization is finally realized.
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Why is it important?
Using the IFSs, this work successfully solves the problem that it is difficult to quantitatively assess the interactivity between attributes in the identifica-tion process of 2-order additive fuzzy measure. The proposed method has broad application prospects in the field of multi-attribute decision making.
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This page is a summary of: A 2-order additive fuzzy measure identification method based on intuitionistic fuzzy sets and its application in credit evaluation, Journal of Intelligent & Fuzzy Systems, June 2021, IOS Press, DOI: 10.3233/jifs-201368.
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