10,584 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) ‘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
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
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
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
Dynamic reference point method with probabilistic linguistic information based on the regret theory for public health emergency decision-making
Group emergency decision-making is an uncertain and dynamic
process, in which the decision makers may be bounded rational
and have a risk appetite. To depict the vague qualitative assessments, the probabilistic linguistic term sets are employed to
express the perceptions of decision makers. First, considering the
regret-aversion of the decision makers’ psychological characteristic, the value function and the regret-rejoice function in the regret
theory are modified to adapt the probabilistic linguistic information. Second, the definition and aggregation operators of the
probabilistic linguistic time variable are proposed to describe and
aggregate the dynamic decision information. Third, the probabilistic linguistic models based on the dynamic reference point
method and the regret theory are studied to maximise the
expectation-levels of alternatives at the relative time point. The
proposed method is applied to select the optimal response strategy for the outbreak of COVID-19 in China. Finally, the comparative analysis is designed to verify the applicability and
reasonability of the proposed method
Algorithms for probabilistic uncertain linguistic multiple attribute group decision making based on the GRA and CRITIC method: application to location planning of electric vehicle charging stations
Electric vehicles (EVs) could be regarded as one of the most
innovative and high technologies all over the world to cope with
the fossil fuel energy resource crisis and environmental pollution
issues. As the initiatory task of EV charging station (EVCS) construction,
site selection play an important part throughout the
whole life cycle, which is deemed to be multiple attribute group
decision making (MAGDM) problem involving many experts and
many conflicting attributes. In this paper, a grey relational analysis
(GRA) method is investigated to tackle the probabilistic uncertain
linguistic MAGDM in which the attribute weights are completely
unknown information. Firstly, the definition of the expected value
is then employed to objectively derive the attribute weights
based on the CRiteria Importance Through Intercriteria Correlation
(CRITIC) method. Then, the optimal alternative is chosen by calculating
largest relative relational degree from the probabilistic
uncertain linguistic positive ideal solution (PULPIS) which considers
both the largest grey relational coefficient from the PULPIS and the
smallest grey relational coefficient from the probabilistic uncertain
linguistic negative ideal solution (PULNIS). Finally, a numerical
case for site selection of electric vehicle charging stations (EVCS) is
designed to illustrate the proposed method. The result shows the
approach is simple, effective and easy to calculate
Wasserstein distance-based probabilistic linguistic TODIM method with application to the evaluation of sustainable rural tourism potential
The evaluation of sustainable rural tourism potential is a key work
in sustainable rural tourism development. Due to the complexity
of the rural tourism development situation and the limited cognition of people, most of the assessment problems for sustainable
rural tourism potential are highly uncertain, which brings challenges to the characterisation and measurement of evaluation
information. Besides, decision-makers (DMs) usually do not exhibit
complete rationality in the practical evaluation process. To tackle
such problems, this paper proposes a new behaviour multi-attribute group decision-making (MAGDM) method with probabilistic
linguistic terms sets (PLTSs) by integrating Wasserstein distance
measure into TODIM (an acronym in Portuguese of interactive
and multicriteria decision making) method. Firstly, a new
Wasserstein-based distance measure with PLTSs is defined, and
some properties of the proposed distance are developed.
Secondly, based on the correlation coefficient among attributes
and standard deviation of each attribute, an attribute weight
determination method (called PL-CRITIC method) is proposed.
Subsequently, a Wasserstein distance-based probabilistic linguistic
TODIM method is developed. Finally, the proposed method is
applied to the evaluation of sustainable rural tourism potential,
along with sensitivity and comparative analyses, as a means of
illustrating the effectiveness and advantages of the new method
Granular computing and optimization model-based method for large-scale group decision-making and its application
In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare
some alternatives, so they often express evaluation information
with incomplete hesitant fuzzy linguistic preference relations.
How to obtain suitable large-scale group decision-making results
from incomplete preference information is an important and
interesting issue to concern about. After analyzing the existing
researches, we find that: i) the premise that complete preference
relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given
by decision makers are greatly possible to be changed during the
consistency improving process. In order to solve these issues, this
work proposes a novel method based on Granular computing
and optimization model for large-scale group decision-making,
considering the original consistency of incomplete hesitant fuzzy
linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the
rationality and advantages of the proposed method: i) semantics
are not changed during the consistency improving process, ii)
completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally
consistent, iv) final large-scale group decision-making result is
acquired by fusing complete preference relations with different weights
Probabilistic double hierarchy linguistic alternative queuing method for real economy development evaluation under the perspective of economic financialization
With the development of science and technology, the new road
of scientific economic and financial development has played a
decisive role in supporting the financial undertaking. To accelerate the economic development, it is very important to increase
the guiding role of financial undertaking in the real economy.
Therefore, it is necessary to promote the development of the real
economy under the perspective of economic financialization
based on some actions. To judge the implementation effect of
these actions, this paper develops a multiple criteria decisionmaking (MCDM) method to evaluate them. First, the decisionmaking matrices are established with the probabilistic double
hierarchy linguistic term set in which the probabilities are added
to all double hierarchy linguistic terms. Additionally, a weightdetermining method is developed to obtain the weight vector of
criteria, and we develop a MCDM method named the probabilistic
double hierarchy linguistic alternative queuing method (PDHLAQM), where the decision-making result is intuitive by a directed
graph or a 0–1 precedence relationship matrix. Furthermore, we
apply the PDHL-AQM to solve a practical MCDM problem involving the real economy development evaluation under the perspective of economic financialization. Finally, some comparative
analyses are made to show the advantages and reasonableness of
the PDHL-AQM
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