Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory

Jin, Feifei; Liu, Jinpei; Zhou, Ligang; Martínez, Luis

doi:10.1007/s10726-021-09736-z

Cited by 83 publications

(16 citation statements)

References 66 publications

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“…On the basis of the similarity matrix SM pq = (sm represents the consensus degree between DMs e p and e q . In addition, consensus matrix B = (b pq ) l×l is a matrix with symmetry [49].…”

Section: Consensus Measures and Consensus Model For Dlfprsmentioning

confidence: 99%

“…Therefore, in the future, we should further study the multi-granularity terms of different experts [56], multi-standard decision-making methods [57], large-scale group decision-making [58,59], and consider the psychological characteristics of regret aversion of experts [49], so that we can expand the application scope of decision support model and apply it to more complex and diverse situations.…”

Section: Data Availability Statement: Not Applicablementioning

confidence: 99%

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Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis

et al. 2021

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The development of distribution linguistic provides a new research idea for linguistic information group decision-making (GDM) problems, which is more flexible and convenient for experts to express their opinions. However, in the process of using distribution linguistic fuzzy preference relations (DLFPRs) to solve linguistic information GDM problems, there are few studies that pay attention to both internal consistency adjustment and external consensus of experts. Therefore, this study proposes a fresh decision support model based on consistency adjustment algorithm and consensus adjustment algorithm to solve GDM problems with distribution linguistic data. Firstly, we review the concept of DLFPRs to describe the fuzzy linguistic evaluation information, and then we present the multiplicative consistency of DLFPRs and a new consistency measurement method based on the distance, and investigate the consistency adjustment algorithm to ameliorate the consistency level of DLFPRs. Subsequently, the consensus degree measurement is carried out, and a new consensus degree calculation method is put forward. At the same time, the consensus degree adjustment is taken the expert cost into account to make it reach the predetermined level. Finally, a distribution linguistic fuzzy group decision making (DLFGDM) method is designed to integrate the evaluation linguistic elements and obtain the final evaluation information. A case of the evaluation of China’s state-owned enterprise equity incentive model is provided, and the validity and superiority of the proposed method are performed by comparative analysis.

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“…On the basis of the similarity matrix SM pq = (sm represents the consensus degree between DMs e p and e q . In addition, consensus matrix B = (b pq ) l×l is a matrix with symmetry [49].…”

Section: Consensus Measures and Consensus Model For Dlfprsmentioning

confidence: 99%

Section: Data Availability Statement: Not Applicablementioning

confidence: 99%

Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis

et al. 2021

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“…It is extremely difficult to eliminate the impact of psychological characteristics of DMs in the process of GEDM under a major epidemic. At present, several behavioral theories have been incorporated into MCGDM, such as prospect theory [ 26 ], cumulative prospect theory (CPT) [ 27 – 29 ], the interactive and multiple attribute decision making (TODIM) [ 24 , 30 – 32 ], and regret theory [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

A Group Emergency Decision-Making Method for Epidemic Prevention and Control Based on Probabilistic Hesitant Fuzzy Prospect Set Considering Quality of Information

Mao

et al. 2022

Int J Comput Intell Syst

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Epidemics can bring huge impacts to economic operation and human health, a practical and effective emergency decision-making (EDM) method is of great significance to reduce all kinds of losses and slow the spread of epidemics. In the process of EDM, decision information is usually uncertain and vague, and the psychological behaviors and various perspectives of decision makers (DMs) should be considered. Hence, this paper develops a group emergency decision-making (GEDM) method under risk based on the probabilistic hesitant fuzzy set (PHFS) and cumulative prospect theory (CPT), in which probabilistic hesitant fuzzy prospect set (PHFPS) that combines PHFS and CPT is developed to portray the vagueness of decision information and psychologies of DMs. Moreover, experts’ creditability in evaluation criteria is generally different because of the differences of their own knowledge structures, practical experience, individual preference and so on. A formula is proposed to measure the quality of decision information provided by experts for revising the expert weights. In addition, the evaluation criteria supporting the GEDM of epidemics are given. Finally, the proposed method is demonstrated by an empirical case study of COVID-19, and the comparison analysis based on the rank-biased overlap model and the sensitivity analysis are conducted to the illustrate the validity of the proposed method.

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“…1 Large-scale group decision-making (LSGDM) shines a light in solving complex decision-making problems. 2,3 LSGDM problems are characterized by large-scale groups, cooperation and competition among DMs, interrelated and contradictory attributes, 4 and what attracted extensive attention of scholars. [5][6][7][8][9] The solutions of LSGDM usually include the following processes 5,10-12 : (1) the clustering of DMs for dividing the large group into several subgroups to achieve dimensionality reduction of LSGDM problems; (2) consensus reaching process (CRP) for improving the consensus level of large-scale group through feedback regulation strategy; and (3) solution selection for obtaining satisfactory group solutions.…”

mentioning

confidence: 99%

“…9,17,19 Due to the limitation of knowledge and the inherent fuzziness of thinking, it is difficult for DMs to give accurate preference information. The existing researches have studied from the perspectives of hesitant fuzzy set, [34][35][36] intuitionistic fuzzy set, 37 probabilistic linguistic information, 4,15,38,39 and collective linguistic information. 40 Nevertheless, when facing the problem of lack of subjective estimation or historical data, the expression based on probability or fuzzy theory shows its limitation.…”

mentioning

confidence: 99%

Information consistent degree‐based clustering method for large‐scale group decision‐making with linear uncertainty distributions information

Gong

Wei

et al. 2021

Int J of Intelligent Sys

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Clustering analysis is a key technique in reducing the dimensionality of high volume irregular data containing large‐scale group decision‐making (LSGDM) information. Uncertainty theory is suitable for subjective estimation or situation, such as lack of historical data, and it can be employed to effectively express the uncertainty of trust and preference information in LSGDM problems. This paper studies the dimensionality reduction and subgroup optimization in LSGDM by utilizing linear uncertain variables in social networks. A clustering method is proposed to decompose the large group into several subgroups of higher consilience degrees and higher preference similarities, and lower the dimension of information for LSGDM. In the clustering process, two measurement attributes, trust relationship and preference relationship of decision‐makers, are combined, and information consistent degree is utilized as the clustering indicator. This approach does not need to preset the threshold and the number of subgroups, and can be employed to obtain subgroups with similar preferences and stable trust relationship. Through the clustering reliability evaluation of subgroups, the rationality of large‐scale group clustering results is verified. Subgroup consensus contribution is used to identify superior subgroups and quantify the role of subgroups in improving the consensus level. An example of emergency decision‐making and comparative analysis is provided to explain the feasibility and advantages of the proposed method.

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Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory

Cited by 83 publications

References 66 publications

Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis

Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis

A Group Emergency Decision-Making Method for Epidemic Prevention and Control Based on Probabilistic Hesitant Fuzzy Prospect Set Considering Quality of Information

Information consistent degree‐based clustering method for large‐scale group decision‐making with linear uncertainty distributions information

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