A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust

Wu, Jian; Dai, Li; Chiclana, Francisco; Fujita, Hamido; Herrera‐Viedma, Enrique

doi:10.1016/j.inffus.2017.09.012

Cited by 320 publications

(111 citation statements)

References 52 publications

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“…The MEMCDM problems have been widely studied, and consensus is an important issue to guarantee that the group members agree to support a decision to obtain the best interest or common goal of the whole group (Lee, 2002;Li, Dong, Herrera, & Herrera-Viedma, 2017;Wu, Dai, Chiclana, Fujita, & Herrera-Viedma, 2018). In general, consensus can be divided into two categories, i.e., the hard consensus and soft consensus (Herrera-Viedma, Martínez, Mata, & Chiclana, 2005).…”

Section: A Cardinal Consensus Reaching Process For Hesitant Fuzzy Linmentioning

confidence: 99%

Hesitant Fuzzy Linguistic Dnma Method With Cardinal Consensus Reaching Process for Shopping Mall Location Selection

Nie

Liao

et al. 2019

International Journal of Strategic Property Management

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The hesitant fuzzy linguistic term set is an effective tool to express qualitative evaluations since it is close to human reasoning and expressing habits. In this paper, we propose a multi-expert multi-criterion decision-making method integrating the double normalization-based multi-aggregation (DNMA) method with a cardinal consensus reaching process, where the assessments of alternatives over multiple criteria are expressed as hesitant fuzzy linguistic term sets. To do so, the DNMA method involving double normalizations and three aggregation tools is extended to deal with the hesitant fuzzy linguistic information and derive the ranking of alternatives with respect to each expert. In addition, a cardinal consensus reaching process is introduced to help experts reach an acceptable consensus level. In other words, the soft consensus is considered in the multi-expert multi-criterion decision-making process. Subsequently, an extended Borda rule is developed to aggregate the subordinate ranks and integrated scores of alternatives, and then deduce the comprehensive ranking of alternatives. A case study is given to illustrate the practicability of the proposed method for selecting the optimal geographical location of a larger-scale shopping mall in the new urbanization for a construction investment agency. The proposed method is compared with other ranking methods to illustrate its advantages.

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Section: A Cardinal Consensus Reaching Process For Hesitant Fuzzy Linmentioning

confidence: 99%

Hesitant Fuzzy Linguistic Dnma Method With Cardinal Consensus Reaching Process for Shopping Mall Location Selection

Nie

Liao

et al. 2019

International Journal of Strategic Property Management

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“…However, in most cases, the feedback parameter is discretionarily used in the interaction process of consensus without proper justification of its selected value [28]. In addition, the inconsistent experts have no idea of their resultant new consensus status when they adopt the provided feedback advices, which in many cases can lead to higher adjustment cost than required to achieve their aimed consensus threshold value [15]. Therefore, these 'traditional' feedback mechanisms share the common limitation of 'forcing' inconsistent experts to adopt the feedback advices, which could end in a lack of willingness with regarding the adjustment of their individual original opinions [4], [15], [29].…”

Section: Introductionmentioning

confidence: 99%

A Personalized Consensus Feedback Mechanism Based on Maximum Harmony Degree

Cao

Chiclana

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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This article proposes a framework of personalized feedback mechanism to help multiple inconsistent experts to reach consensus in group decision making by allowing to select different feedback parameters according to individual consensus degree. The general harmony degree (GHD) is defined to determine the before/after feedback difference between the original and revised opinions. It is proved that the GHD index is monotonically decreasing with respect to the feedback parameter, which means that higher parameters values will result in higher changes of opinions. An optimisation model is built with the GHD as the objective function and the consensus thresholds as constraints, with solution being personalized feedback advices to the inconsistent experts that keep a balance between consensus (group aim) and independence (individual aim). This approach is, therefore, more reasonable than the unpersonalized feedback mechanisms in which the inconsistent experts are forced to adopt feedback generated with only consensus target without considering the extent of the changes acceptable by individual experts. Furthermore, the following interesting theoretical results are also proved: (1) the personalized feedback mechanism guarantees that the increase of consensus level after feedback advices are implemented; (2) the GHD by the personalized feedback mechanism is higher than that of the unpersonalized one; and (3) the personalized feedback mechanism generalises the unpersonalized one as it is proved the latter is a particular type of the former. Finally, a numerical example is provided to model the feedback process and to corroborates these results when comparing both feedback mechanism approaches.

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“…With the increasing complexity in decision‐making problems, multicriteria group decision‐making (MCGDM) has been developed because a single DM or expert cannot analyse such problems across the board (Kabak & Ervural, ). Recently, numerous researchers have paid attention to linguistic computational models, and an increasing number of outcomes have been applied to MCGDM (Garg, ; Garg & Kumar, ; Garg & Nancy, ; Tian, Wang, & Zhang, ; Wu, Dai, et al, ). Although these models improved the fuzzy linguistic methods, limitations exist in capturing linguistic information.…”

Section: Introductionmentioning

confidence: 99%

“…attention to linguistic computational models, and an increasing number of outcomes have been applied to MCGDM (Garg, 2018c;Garg & Kumar, 2018b;Garg & Nancy, 2018;Wu, Dai, et al, 2018). Although these models improved the fuzzy linguistic methods, limitations exist in capturing linguistic information.…”

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confidence: 99%

Signed distance‐based ORESTE for multicriteria group decision‐making with multigranular unbalanced hesitant fuzzy linguistic information

et al. 2018

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The objective of this study is to develop an integrated approach for solving multicriteria group decision‐making problems with multigranular unbalanced hesitant fuzzy linguistic term sets (HFLTSs). Firstly, a signed distance‐based transformation function is proposed to unify multigranular unbalanced hesitant fuzzy linguistic (HFL) assessments. Secondly, a mathematical programming model based on the maximum consensus is constructed to allocate decision‐makers (DMs)' weights objectively. Thirdly, a new signed distance‐based preference score function is defined to aggregate HFL assessments and determine the weak ranking of alternatives, and a novel preference, indifference, and incomparability test framework is constructed to identify the subtle relations among alternatives. On these bases, a signed distance‐based ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) method, in which knowledge regarding criterion values and weights are expressed as multigranular unbalanced HFLTSs, is developed to obtain the ranking of alternatives. Finally, an illustrative example, followed by sensitivity and comparative analyses, is presented to verify the feasibility and effectiveness of the proposed approach.

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A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust

Cited by 320 publications

References 52 publications

Hesitant Fuzzy Linguistic Dnma Method With Cardinal Consensus Reaching Process for Shopping Mall Location Selection

Hesitant Fuzzy Linguistic Dnma Method With Cardinal Consensus Reaching Process for Shopping Mall Location Selection

A Personalized Consensus Feedback Mechanism Based on Maximum Harmony Degree

Signed distance‐based ORESTE for multicriteria group decision‐making with multigranular unbalanced hesitant fuzzy linguistic information

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