Multiple Attribute Strategic Weight Manipulation With Minimum Cost in a Group Decision Making Context With Interval Attribute Weights Information

Liu, Yating; Dong, Yucheng; Liang, Haiming; Chiclana, Francisco; Liu, Xinyue

doi:10.1109/tsmc.2018.2874942

Cited by 115 publications

(41 citation statements)

References 53 publications

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“…In this paper it is assumed that individuals express their preferences honestly. However, in real-life consensus reaching process, individuals often hide their true preferences, and express dishonest preferences to exhibit the strategic manipulation behaviours [39]. Meanwhile, in this paper, the robustness of the MAT model is not studied.…”

Section: Discussionmentioning

confidence: 97%

Consensus Reaching With Time Constraints and Minimum Adjustments in Group With Bounded Confidence Effects

Liang

Dong

Ding

et al. 2020

IEEE Trans. Fuzzy Syst.

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In the bounded confidence model it is widely known that individuals rely on the opinions of their close friends or people with similar interests. Meanwhile, the decision maker always hopes that the opinions of individuals can reach a consensus in a required time. Therefore, with this idea in mind, this paper develops a consensus reaching model with time constraints and minimum adjustments in a group with bounded confidence effects. In the proposed consensus approach, the minimum adjustments rule is used to modify the initial opinions of individuals with bounded confidence, which can further influence the opinion evolutions of individuals to reach a consensus in a required time. The properties of the model are studied, and detailed numerical examples and comparative simulation analysis are provided to justify its feasibility. Index Terms-Consensus, time constraints, bounded confidence, minimum adjustments, opinion dynamics, group decision making I. INTRODUCTION onsensus reaching is an important research problem that involves in the preference evolution and aggregation of a group of individuals [1-3], and it is present in a wide range of application areas [4-6], such as

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Section: Discussionmentioning

confidence: 97%

Consensus Reaching With Time Constraints and Minimum Adjustments in Group With Bounded Confidence Effects

Liang

Dong

Ding

et al. 2020

IEEE Trans. Fuzzy Syst.

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“…As a part of the future scope, we plan the following research directions: (i) to present new methods for ranking under pair-wise comparison ideas; (ii) to enhance the consistency of the PLPRs under both additive and multiplicative context; (iii) to develop methods for consensus reaching by gaining motivation from [38,39] and strategic weight calculation inspired by [40,41].…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making

et al. 2018

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With trending competition in decision-making process, linguistic decision-making is gaining attractive attention. Previous studies on linguistic decision-making have neglected the occurring probability (relative importance) of each linguistic term which causes unreasonable ranking of objects. Further, decision-makers' (DMs) often face difficulties in providing apt preference information for evaluation. Motivated by these challenges, in this paper, we set our proposal on probabilistic linguistic preference relation (PLPR)-based decision framework. The framework consists of two phases viz., (a) missing value entry phase and (b) ranking phase. In phase (a), the missing values of PLPR are filled using a newly proposed automatic procedure and consistency of PLPR is ensured using a consistency check and repair mechanism. Following this, in phase (b), objects are ranked using newly proposed analytic hierarchy process (AHP) method under PLPR context. The practicality of the proposal is validated by using two numerical examples viz., green supplier selection problem for healthcare and the automobile industry. Finally, the strength and weakness of the proposal are discussed by comparing with similar methods. Symmetry 2019, 11, 2 2 of 18 weighted geometric aggregation operator for a linguistic domain. Further, He et al. [8] put forward a new entropy measure for linguistic-based group decision-making.Though linguistic decision-making is an attractive concept, DMs still face difficulties in rationally rating the objects. The main reason for this difficulty is the cognitive behavior of the human mind, which encourages pair-wise comparative analysis rather than a standalone rating [9]. Motivated by the power of pair-wise comparison and linguistic term set (LTS), Herrera et al. [10,11] proposed the linguistic preference relation (LPR) concept for group decision-making and investigated some choice functions for the same. Following this, Xu [12] put forward some deviation measures for decision-making process under LPR context. Recently, Molinera et al. [13] developed new fuzzy ontologies for linguistic preference information and applied the same for decision-making. Wang and Xu [14] put forward an interactive algorithm for filling the missing LPR values using consistency measures and repaired the consistency of the same using the repairing mechanism.Inspired by the power of linguistic information and its substantial use in decision-making, Rodriguez [15] proposed the hesitant fuzzy linguistic term set (HFLTS) which is an extension to LTS under hesitant fuzzy environment [16]. The HFLTS allowed DMs to give different choices of preference for the same instance, which managed uncertainty to some extent. Motivated by the power of HFLTS, Zhu and Xu [17] put forward the hesitant fuzzy linguistic preference relation (HFLPR), which is an extension to preference relation under HFLTS context. They also investigated some consistency measures for the same. Following this, Wang and Xu [18] presented the concept of extended hesitant fuzzy prefe...

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“…G Roup decision making (GDM) problems involve a group of decision makers expressing their opinions on a finite set of alternatives on a set of criteria; the individual opinions are then fused or aggregated into a collective one to derive a common group solution [1]- [5]. In usually exists inconsistency or disagreement among the group decision makers because of the individual decision makers' different styles and viewpoints on the decision problem [6]- [11].…”

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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Multiple Attribute Strategic Weight Manipulation With Minimum Cost in a Group Decision Making Context With Interval Attribute Weights Information

Cited by 115 publications

References 53 publications

Consensus Reaching With Time Constraints and Minimum Adjustments in Group With Bounded Confidence Effects

Consensus Reaching With Time Constraints and Minimum Adjustments in Group With Bounded Confidence Effects

Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making

A Personalized Consensus Feedback Mechanism Based on Maximum Harmony Degree

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