On consensus models with utility preferences and limited budget

Gong, Zaiwu; Xu, Xiaoxia; Lu, Fengling; Li, Lianshui; Xu, Chao

doi:10.1016/j.asoc.2015.03.019

Cited by 61 publications

(28 citation statements)

References 30 publications

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“…Let f k be the unit cost to revise the decision maker e k 's attribute weight. The unit cost is a basic concept of minimum cost GDM models [17], [18], [24], [51], [53] and refers to the cost for the decision makers adjusting the unit opinions. Usually, the unit cost can be measured by money, time, and so on.…”

Section: A Basic Ideas and Modelmentioning

confidence: 99%

“…Based on the non-negative property of distance functions, we can obtain different distance formula for the different ranges of w * , i.e., − w k j )z k j1 + (w k j − I k,+ j )z k j3 . According to Lemmas 1 and 2, plug models (15)- (23) and (17)- (25) into model (14) and transform the optimization models into the mixed 0-1 linear programming models (26)- (42) and (26), (28)- (44), respectively.…”

Section: Appendix a Proofsmentioning

confidence: 99%

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

Liu

Dong

Liang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

111

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In multiple attribute decision making (MADM), strategic weight manipulation is understood as a deliberate manipulation of attribute weight setting to achieve a desired ranking of alternatives. In this paper, we study the strategic weight manipulation in a group decision making (GDM) context with interval attribute weight information. In GDM, the revision of the decision makers' original attribute weight information implies a cost. Driven by a desire to minimize the cost, we propose the minimum cost strategic weight manipulation model, which is achieved via optimization approach, with the mixed 0-1 linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic attribute weight based on the ranking range under interval attribute weight information are proposed. Finally, numerical analysis and simulation experiments are provided with a twofold aim: 1) to verify the validity of the proposed models and 2) to show the effects of interval attribute weights information and the unit cost, respectively, on the cost to manipulate strategic weights in the MADM in a group decision context.

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Section: A Basic Ideas and Modelmentioning

confidence: 99%

Section: Appendix a Proofsmentioning

confidence: 99%

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

Liu

Dong

Liang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

111

View full text Add to dashboard Cite

show abstract

“…Step 3. According to equations (10)- (12), the attitude trust consensus model of inconsistent experts is established, and the trust weight of inconsistent experts under different parameters is calculated by the equation (11).…”

Section: The Process Of Consensus Based On Attitudinal Trust Indumentioning

confidence: 99%

“…The term 'group independence' can be captured with what we call the 'harmony degree' (HD), which will measure the extent of the difference between the original opinion and the revised opinion after adopting the group advices [41]. The bigger the HD index is, the lower the adjustment cost of the inconsistent experts will be [12], [13], [40]. Furthermore, the HD index is proved to be bigger than the average degree of conformity of an expert in the group (ACD index), which means that the inconsistent expert prefers to keep their opinion rather than modify it towards the group opinion in GDM consensus.…”

Section: Introductionmentioning

confidence: 99%

An Attitudinal Trust Recommendation Mechanism to Balance Consensus and Harmony in Group Decision Making

Chiclana

et al. 2019

IEEE Trans. Fuzzy Syst.

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This article puts forward a trust based framework for building a recommendation mechanism for consensus in group decision making with interval-valued intuitionistic fuzzy information. To do that, it first presents an attitudinal trust model where experts assign trust weights to others considering the concept of attitude of the group. This approach allows for the implementation of the group attitude in a continuous scale ranging from a pessimistic attitude to an indifferent attitude. Thus, it can express the continuous trust status, and consequently it generalizes the traditional simplified trust model: 'trusting' and 'distrusting'. In particular, three typical policies are defined as: 'extreme trust policy', 'bounded trust policy' and 'indifferent trust policy'. Secondly, the attitudinal trust induced recommendation mechanism is established by a reasonable rule: the closer the experts, the higher their trust degree. This can guarantee that the consensus level of the inconsistent expert is increased after adopting the recommended advices. In addition to group consensus, experts envisage to keep their original opinions as much as possible. A harmony degree (HD) is defined to determine the extent of the difference between an original opinion and the corresponding revised opinion after adopting the recommended advices. Combining the HD index and the consensus index, a sensitivity analysis with attitudinal parameter is proposed to verify the rationality of the proposed attitudinal trust recommendation mechanism. In practice this will facilitate the inconsistent experts to achieve a balance between consensus degree and harmony degree by selecting an appropriate attitudinal parameter.

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“…roup decision making (GDM) is a scenario where a group of decision makers bring together their preferences to arrive at a common solution from a set of feasible solutions or alternatives [18], [19], [20], [21], [29], [31], [35], [38], [45]. To ensure that all decision makers will accept the GDM solution, two processes are usually included in the resolution process [2], [14], [22], [26], [36], [37]: the consensus reaching process (CRP) and the selection process.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Feedback Mechanism Based on Bounded Confidence Learning to Support Consensus Reaching in Group Decision Making

Zha

Dong

Zhang

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

114

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Different feedback mechanisms have been reported in consensus reaching models to provide advices for preference adjustment to assist decision makers to improve their consensus levels. However, the existing feedback mechanisms seldom discuss the willingness of decision makers to accept the advices they receive. In the discipline of opinion dynamics, the bounded confidence model justifies well that in the process of interaction decision makers take only into account the preferences that differ from theirs not more than a certain confidence level. Inspired by this idea, this paper proposes a new consensus reaching model with personalized feedback mechanism to help decision makers with bounded confidences in achieving consensus. Specifically, the personalized feedback mechanism produces more acceptable advices in the two cases where bounded confidences are known or unknown. Finally, numerical and simulation analysis are presented to explore the effectiveness of the proposed model in reaching consensus.

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On consensus models with utility preferences and limited budget

Cited by 61 publications

References 30 publications

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

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

An Attitudinal Trust Recommendation Mechanism to Balance Consensus and Harmony in Group Decision Making

A Personalized Feedback Mechanism Based on Bounded Confidence Learning to Support Consensus Reaching in Group Decision Making

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