4,533 research outputs found
Consensus-based clustering under hesitant qualitative assessments
Producción CientíficaIn this paper, we consider that agents judge the feasible alternatives through linguistic terms – when they are confident in their opinions – or linguistic expressions formed by several consecutive linguistic terms – when they hesitate. In this context, we propose an agglomerative hierarchical clustering process where the clusters of agents are generated by using a distance-based consensus measure.Ministerio de Economía, Industria y Competitividad (ECO2012-32178)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA066U13
Consensus-Based Agglomerative Hierarchical Clustering
Producción CientíficaIn this contribution, we consider that a set of agents assess a set of alternatives
through numbers in the unit interval. In this setting, we introduce a measure
that assigns a degree of consensus to each subset of agents with respect to every
subset of alternatives. This consensus measure is defined as 1 minus the outcome
generated by a symmetric aggregation function to the distances between
the corresponding individual assessments. We establish some properties of the
consensus measure, some of them depending on the used aggregation function.
We also introduce an agglomerative hierarchical clustering procedure that is generated
by similarity functions based on the previous consensus measuresMinisterio de Economía, Industria y Competitividad (ECO2012-32178)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA066U13
Ordinal proximity measures in the context of unbalanced qualitativescales and some applications to consensus and clustering
Producción CientíficaIn this paper, we introduce ordinal proximity measures in the setting of unbalanced qualitative scales by comparing the proximities between linguistic terms without numbers, in a purely ordinal approach. With this new tool, we propose how to measure the consensus in a set of agents when they assess a set of alternatives through an unbalanced qualitative scale. We also introduce an agglomerative hierarchical clustering procedure based on these consensus measures.Ministerio de Economía, Industria y Competitividad (ECO2012-32178)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA066U13
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 experts’ 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 experts’ 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 experts’ 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
A consensus reaching process in the context of non-uniform ordered qualitative scales
Producción CientíficaIn this paper, we consider that a group of agents judge a set of alternatives by
means of an ordered qualitative scale. The scale is not assumed to be uniform,
i.e., the psychological distance between adjacent linguistic terms is not necessarily
always the same. In this setting, we propose how to measure the consensus in
each subset of at least two agents over each subset of alternatives. We introduce
a consensus reaching process where some agents may be invited to change their
assessments over some alternatives in order to increase the consensus. All the
steps are managed in a purely ordinal way through ordinal proximity measures.Ministerio de Economía, Industria y Competitividad (ECO2012-32178)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA066U13
A contribution to consensus modeling in decision-making by means of linguistic assessments
Decision-making is an active field of research. Specifically, in recent times, a lot of contributions have been presented on decision-making under linguistic assessments. To tackle this kind of processes, hesitant fuzzy linguistic term sets have been introduced to grasp the uncertainty inherent in human reasoning when expressing preferences. This thesis introduces an extension of the set of hesitant fuzzy linguistic term sets to capture differences between non-compatible assessments. Based on this extension, a distance between linguistic assessments is defined to quantify differences between several opinions. This distance is used in turn to present a representative opinion from a group in a decision-making process. In addition, different consensus measures are introduced to determine the level of agreement or disagreement within a decision-making group and are used to define a decision maker’s profile to keep track of their dissension with respect to the group as well as their level of hesitancy. Furthermore, with the aim of allowing decision makers to choose the linguistic terms that they feel more comfortable with, the concept of free double hierarchy hesitant fuzzy linguistic term set is developed in this thesis. Finally, a new approach of the TOPSIS methodology for processes in which the assessments are given by means of free double hierarchy hesitant fuzzy information is presented to rank alternatives under these circumstances.Postprint (published version
Large-scale consensus with endo-confidence under probabilistic linguistic circumstance and its application
In real decision-making problems, decision makers (DMs) usually
select the most potential project from several ones. However,
they unconsciously show different confidence levels in decisionmaking process because they come from various backgrounds
and have different experiences, etc., which affects the decision
results. Moreover, the probabilistic linguistic term set, which not
only includes the linguistic expressions used by DMs in their daily
life but also contains the probability for each linguistic term, can
well portray the real perceptions of DMs for the projects.
Furthermore, large-scale consensus has gradually been a popular
way to effectively solve complex decision-making problems. To
sum up, in this paper, we are dedicated to constructing a largescale consensus model considering the confidence levels of DMs
under probabilistic linguistic circumstance. Firstly, the endo-confidence is defined and measured by DM’s probabilistic linguistic
information. Then, the DMs are clustered according to the similarities of both evaluation information and the endo-confidence levels. Both evaluation of the non-consensus cluster and evaluation
integrated by the clusters with higher endo-confidence level than
this non-consensus cluster are used as the reference to adjust its
evaluation information. Then, a case study and the comparative
analysis are carried out. Finally, some conclusions and future work
are given
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) ‘individual manipulation’
where each expert manipulates his/her own behaviour to achieve
higher importance degree (weight); and (2) ‘group manipulation’
where a group of experts force inconsistent experts to adopt
specific recommendation advices obtained via the use of fixed
feedback parameter. To counteract ‘individual manipulation’, a
behavioural weights assignment method modelling sequential
attitude ranging from ‘dictatorship’ to ‘democracy’ is developed,
and then a reasonable policy for group minimum adjustment cost
is established to assign appropriate weights to experts. To prevent
‘group manipulation’, 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
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039
71421001,71910107002,71771037,71874023
71871149Sichuan University sksyl201705
2018hhs-5
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