3,599 research outputs found
An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A novel framework to prevent manipulation behaviour
in consensus reaching process under social network
group decision making is proposed, which is based on a theoretically
sound optimal feedback model. The manipulation
behaviour classification is twofold: (1) âindividual manipulationâ
where each expert manipulates his/her own behaviour to achieve
higher importance degree (weight); and (2) âgroup manipulationâ
where a group of experts force inconsistent experts to adopt
specific recommendation advices obtained via the use of fixed
feedback parameter. To counteract âindividual manipulationâ, a
behavioural weights assignment method modelling sequential
attitude ranging from âdictatorshipâ to âdemocracyâ is developed,
and then a reasonable policy for group minimum adjustment cost
is established to assign appropriate weights to experts. To prevent
âgroup manipulationâ, an optimal feedback model with objective
function the individual adjustments cost and constraints related
to the threshold of group consensus is investigated. This approach
allows the inconsistent experts to balance group consensus and
adjustment cost, which enhances their willingness to adopt the
recommendation advices and consequently the group reaching
consensus on the decision making problem at hand. A numerical
example is presented to illustrate and verify the proposed optimal
feedback model
Trust Based Consensus Model for Social Network in an Incomplete Linguistic Information Context
A theoretical framework to consensus building within a networked social group is put forward. This article investigates a trust based estimation and aggregation methods as part of a visual consensus model for multiple criteria group decision making with incomplete linguistic information. A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network and the trust score induced order weighted averaging operator is presented to aggregate the orthopairs of trust/distrust values obtained from different trust paths. Then, the concept of relative trust score is defined, whose use is twofold: (1) to estimate the unknown preference values and (2) as a reliable source to determine experts' weights. A visual feedback process is developed to provide experts with graphical representations of their consensus status within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feedback process also includes a recommendation mechanism to provide advice to those experts that are identified as contributing less to consensus on how to change their identified preference values. It is proved that the implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process
Goal programming approaches to deriving interval fuzzy preference relations
This article investigates the consistency of interval fuzzy preference relations based on interval arithmetic, and new definitions are introduced for additive consistent, multiplicative consistent and weakly transitive interval fuzzy preference relations. Transformation functions are put forward to convert normalized interval weights into consistent interval fuzzy preference relations. By analyzing the relationship between interval weights and consistent interval fuzzy preference relations, goal-programming-based models are developed for deriving interval weights from interval fuzzy preference relations for both individual and group decision-making situations. The proposed models are illustrated by a numerical example and an international exchange doctoral student selection problem
A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme
Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of expertsâ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of expertsâ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of expertsâ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version
Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of B.W.M. and interval valued intuitionistic fuzzy T.O.D.I.M.
open access articleDeveloping and accepting industry 4.0 influences the industry structure and customer willingness. To a successful transition to industry 4.0, implementation strategies should be selected with a systematic and comprehensive view to responding to the changes flexibly. This research aims to identify and prioritise the strategies for implementing industry 4.0. For this purpose, at first, evaluation attributes of strategies and also strategies to put industry 4.0 in practice are recognised. Then, the attributes are weighted to the expertsâ opinion by using the Best Worst Method (BWM). Subsequently, the strategies for implementing industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the Interval Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of âTechnologyâ, âQualityâ, and âOperationâ have respectively the highest importance. Furthermore, the strategies for ânew business models developmentâ, âImproving information systemsâ and âHuman resource managementâ received a higher rank. Eventually, some research and executive recommendations are provided. Having strategies for implementing industry 4.0 is a very important solution. Accordingly, multi-criteria decision-making (MCDM) methods are a useful tool for adopting and selecting appropriate strategies. In this research, a novel and hybrid combination of BWM-TODIM is presented under IVIF information
Predicting missing pairwise preferences from similarity features in group decision making
In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise preferences in
the form of a matrix. Within the field of GDM, the problem of estimating missing values is of utmost
importance, since many experts provide incomplete preferences. In this paper, we propose a new
method called the entropy-based method for estimating the missing values in the FPR. We compared
the accuracy of our algorithm for predicting the missing values with the best candidate algorithm
from state of the art achievements. In the proposed entropy-based method, we took advantage of
pairwise preferences to achieve good results by storing extra information compared to single rating
scores, for example, a pairwise comparison of alternatives vs. the alternativeâs score from one to five
stars. The entropy-based method maps the prediction problem into a matrix factorization problem, and
thus the solution for the matrix factorization can be expressed in the form of latent expert features
and latent alternative features. Thus, the entropy-based method embeds alternatives and experts in
the same latent feature space. By virtue of this embedding, another novelty of our approach is to
use the similarity of experts, as well as the similarity between alternatives, to infer the missing values
even when only minimal data are available for some alternatives from some experts. Note that current
approaches may fail to provide any output in such cases. Apart from estimating missing values, another
salient contribution of this paper is to use the proposed entropy-based method to rank the alternatives.
It is worth mentioning that ranking alternatives have many possible applications in GDM, especially
in group recommendation systems (GRS).Andalusian Government P20 00673
PID2019-103880RB-I00
MCIN/AEI/10.13039/50110001103
A chi-square method for priority derivation in group decision making with incomplete reciprocal preference relations
This paper proposes a chi-square method (CSM) to obtain a priority vector for group decision making (GDM) problems where decision-makersâ (DMsâ) assessment on alternatives is furnished as incomplete reciprocal preference relations with missing values. Relevant theorems and an iterative algorithm about CSM are proposed. Saatyâs consistency ratio concept is adapted to judge whether an incomplete reciprocal preference relation provided by a DM is of acceptable consistency. If its consistency is unacceptable, an algorithm is proposed to repair it until its consistency ratio reaches a satisfactory threshold. The repairing algorithm aims to rectify an inconsistent incomplete reciprocal preference relation to one with acceptable consistency in addition to preserving the initial preference information as much as possible. Finally, four examples are examined to illustrate the applicability and validity of the proposed method, and comparative analyses are provided to show its advantages over existing approaches
Two-Fold Personalized Feedback Mechanism for Social Network Consensus by Uninorm Interval Trust Propagation
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 71971135, Grant 71571166, and Grant 71910107002; and in part by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033. This article was recommended by Associate Editor F. J. Cabrerizo.A twofold personalized feedback mechanism is established
for consensus reaching in social network group decisionmaking
(SN-GDM). It consists of two stages: (1) generating the
trusted recommendation advice for individuals; and (2) producing
personalized adoption coefficient for reducing unnecessary
adjustment costs. This is achieved by means of a uninorm
interval-valued trust propagation operator to obtain indirect
trust. The trust relationship is used to generate personalized
recommendation advice based on the principle of âa recommendation
being more acceptable the higher the level of trust
it derives fromâ. An optimization model is built to minimise
the total adjustment cost of reaching consensus by determining
personalized feedback adoption coefficient based on individualsâ
consensus levels. Consequently, the proposed twofold personalized
feedback mechanism achieves a balance between group
consensus and individual personality. An example to demonstrate
how the proposed twofold personalized feedback mechanism
works is included, which is also used to show its rationality by
comparison with the traditional feedback mechanism in GDM.National Natural Science Foundation of China (NSFC) 71971135
71571166
71910107002Spanish Government PID2019-103880RB-I00/AEI/10.13039/50110001103
Biased experts and similarity based weights in preferences aggregation
In a group decision making setting, we consider the potential impact an expert can have on the overall ranking by providing a biased assessment of the alternatives that differs substantially from the majority opinion. In the framework of similarity based averaging functions, we show that some alternative approaches to weighting the experts\u27 inputs during the aggregation process can minimize the influence the biased expert is able to exert
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