Uncertainty measurements for Pythagorean fuzzy set and their applications in multiple-criteria decision making

Wang, Zhe; Xiao, Fuyuan; Cao, Zehong

doi:10.1007/s00500-022-07361-9

Cited by 37 publications

(12 citation statements)

References 60 publications

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“…But it is necessary to consider the fairness of weight distribution. In general, the entropy weight method can reduce the uncertainty of weight distribution (Wang et al 2022 ). The larger the entropy value, the fairer the weight distribution.…”

Section: Related Workmentioning

confidence: 99%

An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making

Sun

Chiclana

et al. 2022

Artif Intell Rev

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In social network group decision making (SN-GDM) problem, subgroup weights are mostly unknown, many approaches have been proposed to determine the subgroup weights. However, most of these methods ignore the weight manipulation behavior of subgroups. Some studies indicated that weight manipulation behavior hinders consensus efficiency. To deal with this issue, this paper proposes a theoretical framework to prevent weight manipulation in SN-GDM. Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the power index (PI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’ manipulation behavior. The minimum adjustment rule aims for ‘efficiency’ while the maximum entropy rule aims for ‘justice’ . The experimental results show that the proposed model can guarantee the rationality of weight distribution to reach consensus efficiently, which is achieved by maintaining a balance between ‘efficiency’ and ‘justice’ in the mechanism of assigning weights. Finally, the detailed numerical and simulation analyses are carried out to verify the validity of the proposed method.

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Section: Related Workmentioning

confidence: 99%

An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making

Sun

Chiclana

et al. 2022

Artif Intell Rev

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“…Singh and Ganie ( 2020 ) developed a few PF metrics of correlation with their utility. PFSs have been examined and implemented by numerous researchers (Garg 2019a , b ; Khan et al 2019a , b ; Akram and Ali 2020 ; Ejegwa 2020a , b ; Rahman et al 2020 ; Zhang et al 2021 ; Olgun et al 2021 ; Mishra et al 2021 ; Zeb et al 2022 ; Wang et al 2022 ; Ganie 2022 , 2023 ; Ganie et al 2022 ; Akram and Bibi 2023 ; Ejegwa et al 2023 ; Aldring and Ajay 2023 ; Kirişci 2023 ; Akram et al 2023b ; Akram et al 2023a , c ) in various uncertain situations. Chen and Chiou ( 2015 ) and Zeng et al ( 2020 ) discussed the applicability of interval-valued IFSs to MADM problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure

et al. 2023

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“…Gu et al L3 explained the risk assessment of landslide hazards in shiwangmiao. Wang et al [25] described uncertainty measurement for Pythagorean set. Verma and Mittal [23] worked on multiple attribute group decision-making based on novel probabilistic ordered weighted cosine similarity operators with Pythagorean fuzzy information.…”

Section: Introductionmentioning

confidence: 99%

Certain Operations on Complex Picture Fuzzy Graphs

et al. 2022

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A complex picture fuzzy set (Com-PFS) is a motivating tool for more precisely interpreting fuzzy notions. Recently, all extensions of complex fuzzy graphs (Com-FGs) have become a growing research topic as they handle ambiguous situations more explicitly than complex intuitionistic fuzzy graphs (Comp-IFG) and picture fuzzy graphs (PFG). The primary goal of this study is to demonstrate the foundation of Com-PFG due to the drawback of the complex neutral membership function in Com-IFG. This paper introduces the concept of Com-PFGs and explains many of the approaches to their development. A Com-PFG has three complex membership functions. We define the order, size, degree of vertex, and total degree of vertex in Com-PFGs. We elaborate on primary operations including: complement, join, union, Cartesian product, direct product, and composition of Com-PFG. Finally, we discuss its use in decision-making problems.

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Uncertainty measurements for Pythagorean fuzzy set and their applications in multiple-criteria decision making

Cited by 37 publications

References 60 publications

An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making

An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making

Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure

Certain Operations on Complex Picture Fuzzy Graphs

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