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Symmetry 2019, 11(1), 85; https://doi.org/10.3390/sym11010085

Distribution-Based Approaches to Deriving Weights from Dual Hesitant Fuzzy Information

1
Command & Control Engineering College, Army Engineering University of PLA, Nanjing 210007, China
2
Fundamental Education Department, Army Engineering University of PLA, Nanjing 210007, China
3
Business School, Sichuan University, Chengdu 610065, China
*
Author to whom correspondence should be addressed.
Received: 7 December 2018 / Revised: 5 January 2019 / Accepted: 8 January 2019 / Published: 14 January 2019
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Abstract

Modern cognitive psychologists believe that the decision act of cognitive bias on decision results is universal. To reduce their negative effect on dual hesitant fuzzy decision-making, we propose three weighting methods based on distribution characteristics of data. The main ideas are to assign higher weights to the mid arguments considered to be fair and lower weights to the ones on the edges regarded as the biased ones. The means and the variances of the dual hesitant fuzzy elements (DHFEs) are put forward to describe the importance degrees of the arguments. After that, these results are expanded to deal with the hesitant fuzzy information and some examples are given to show their feasibilities and validities. View Full-Text
Keywords: dual hesitant fuzzy set; hesitant fuzzy set; distance measure; similarity measure; weight vector; normal distribution dual hesitant fuzzy set; hesitant fuzzy set; distance measure; similarity measure; weight vector; normal distribution
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Su, Z.; Xu, Z.; Zhao, H.; Liu, S. Distribution-Based Approaches to Deriving Weights from Dual Hesitant Fuzzy Information. Symmetry 2019, 11, 85.

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