Group Decision-making Model Research Articles

Accelerating Consensus Reaching Through Top Persuaders: A Social Persuasion Model in Social Network Group Decision Making

In traditional group decision-making models, it is commonly assumed that all decision makers exert equal influence on one another. However, in real-world social networks, such as Twitter and Facebook, certain individuals—known as top persuaders—hold a disproportionately large influence over others. This study formulates the consensus-reaching problem in social network group decision making by introducing a novel framework for predicting top persuaders. Building on social network theories, we develop a social persuasion model that integrates social influence and social status to quantify individuals’ persuasive power more comprehensively. Subsequently, we propose a new CRP that leverages the influence of top persuaders. Our simulations and comparative analyses demonstrate that: (1) increasing the number of top persuaders substantially reduces the iterations required to achieve consensus; (2) establishing trust relationships between top persuaders and other individuals accelerates the consensus process; and (3) top persuaders retain a high and stable level of influence throughout the entire CRP rounds. Our research provides practical insights into identifying and strategically guiding top persuaders to enhance the efficiency in consensus reaching and reduce social management costs within social networked environments.

An applied study of multi-stage multi-attribute group decision-making based on intuitionistic fuzzy sets—selection of hydrogen energy projects in the context of “dual-carbon"

In group decision-making scenarios involving intuitionistic fuzzy numbers for attribute values, attribute weights tend to be subjective. To enhance objectivity and scientific rigor in decision-making, this study develops a multi-stage, multi-attribute group decision-making model based on intuitionistic fuzzy sets. The model integrates both subjective and objective attribute weights and uses a product method to calculate comprehensive weights. A new “double-consideration” method is proposed to determine time weights, making it particularly suitable for multi-stage decision-making. The model also aggregates multi-stage information through an intuitionistic fuzzy weighted average operator and employs Pearson’s correlation coefficient to assign expert weights, promoting group consensus. The improved TOPSIS (G-TOPSIS) method is then applied to the selection of hydrogen energy projects within the “dual-carbon” framework. Comparative and sensitivity analyses verify the model’s feasibility and effectiveness, offering valuable guidance for hydrogen energy project selection in alignment with “dual-carbon” goals.

Interval-Valued Intuitionistic Fuzzy Yager Power Operators and Possibility Degree-Based Group Decision-Making Model

As an extended form of intuitionistic fuzzy set, the theory of interval-valued intuitionistic fuzzy set (IVIFS) can describe fuzziness more flexibly. This study aims to develop a group decision-making model based on the distance measure, Yager power aggregation operators and the possibility measure in the context of IVIFSs. For this purpose, new distance measure is proposed to quantify the dissimilarity between two IVIFSs. In addition, comparison with existing distance measures is performed to illustrate the efficiency of introduced measure. Combining the Yager’s triangular norms with the proposed distance-based power operators, a series of interval-valued intuitionistic fuzzy (IVIF) Yager power aggregation operators are introduced with their desirable properties. Moreover, a possibility measure is developed for pairwise comparisons of IVIFSs, which overcomes the shortcomings of existing IVIF-score function, IVIF-accuracy function, and IVIF-possibility measures. The developed possibility measure is further utilized to compute the weights of criteria. To prove the practicality and effectiveness of introduced model, it is applied to a case study of manufacturing plant location selection problem with IVIF information. Finally, sensitivity and comparative analyses are carried out to test the stability and robustness of the proposed method under the setting of IVIFSs.

Multi-criteria group decision-making with extended ELECTRE III method and regret theory based on probabilistic interval-valued intuitionistic hesitant fuzzy information

Multi-criteria group decision-making with extended ELECTRE III method and regret theory based on probabilistic interval-valued intuitionistic hesitant fuzzy information

Retraction Note: A DES-based group decision model for group decision making with large-scale alternatives

Retraction Note: A DES-based group decision model for group decision making with large-scale alternatives

A Two-Stage Multi-attribute Group Decision-Making Model Based on Regret Theory and Three-Way Decision Method with R-Numbers

A Two-Stage Multi-attribute Group Decision-Making Model Based on Regret Theory and Three-Way Decision Method with R-Numbers

Prospect theory driven-minimum cost ordinal consensus model for large-scale group decision-making

Prospect theory driven-minimum cost ordinal consensus model for large-scale group decision-making

A large-scale group decision making model with a clustering algorithm based on a locality sensitive hash function

With the development of science and technology, an expanding array of decision-makers across various fields, including engineering and medicine, have been participating in collaborative decision-making for complex scenarios, such as earthquake relief and disease containment. The rapidly changing dynamics of real-world decision-making and the high complexity of consensus reaching among decision-makers require the development of more sophisticated models to handle these challenges. Considering the diversity and stability of group categories, this study proposes a large-scale group decision-making model based on a locality sensitive hash function. First, the volatility of attributes in real scenarios is considered, and a time-series decision matrix is constructed based on the average growth rates to make the results closer to reality. Then, hash functions are used to map the decision opinions to different dimensions and express the similarity through the Hamming distance, yielding clustering results with high stability and cohesion. To determine whether the decision-making group can reach a consensus, this study conducts hypothesis testing, adopting the idea of small probability counterfactuals to provide objective and fair standards for threshold judgment. Finally, through the case study and comparative analysis, it is proved that the proposed method improved 26.4% and 4.2% under the criteria of integrated cohesion and global consensus degree, respectively, with better clustering effect.

A minimum cost and maximum fairness-driven multi-objective optimization consensus model for large-scale group decision-making

The rise of social media and e-democracy has driven a paradigm shift in decision-making, notably reflected in the changing ways of public participation and policymaking within decision-making processes. The increased focus on fairness and efficiency not only complicates the consensus-building process among diverse interests and perspectives, but also significantly adds to the complexity of decision-making and its implementation. In this context, balancing the interests of all parties while ensuring fairness and improving decision-making effectiveness becomes crucial. To address these challenges, this study develops a multi-objective optimization consensus framework for large-scale group decision-making (LSGDM) that integrates the interests of decision-makers (DMs) and a moderator, providing a more comprehensive tool. Specifically, this study first designs a DM weight determination method based on structural hole theory within fuzzy social networks. The proposed DM weight determination method effectively leverages the flexibility of fuzzy social networks and the comprehensiveness of structural hole theory to enhance the accuracy and reliability of weight assignment. Building on this, a novel clustering method based on the maximum group consensus level is developed, taking into account the varying importance of different DMs. Furthermore, a minimum cost and maximum fairness-driven multi-objective optimization LSGDM consensus model, referred to as MCMF-MO-LSGDM, is explored in this study. Finally, the utility and superiority of the constructed model are confirmed through comparative analysis and simulation experiments against existing related works.

Minimum deviation distribution ranking model and fairness concern-based consensus building for group decision-making

Group decision-making refers to the collective decision-making of multiple decision-makers to solve a common problem. However, decision-makers typically exhibit different psychological attitudes and behaviors in the decision-making process, which leads to actual decisions deviating from the solution recommendations obtained by traditional quantitative methods. More importantly, most existing studies on consensus fail to simultaneously consider the internal process of consensus building and psychological behaviors of decision-makers. To address this gap, this paper proposes a novel group decision-making method using a minimum deviation distribution ranking model and fairness concern-based consensus building. First, a fairness utility function is established to compare evaluation information from different decision-makers capturing fairness concern between individuals. Then, an optimization model is developed to determine decision-makers’ weights. Additionally, a fairness-based consensus model is proposed, which considers decision-makers’ acceptable adjustment range to avoid excessive information loss. To derive the priority vector, we develop a ranking model based on minimum deviation distribution by controlling the deviation in the minimum range. Finally, we provide a numerical example and comparative analysis to evaluate the effectiveness and superiority of our method, and conduct a robustness test to verify the stability and reliability of the ranking model.

Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information

In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic q-rung orthopair fuzzy (DHLq-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank t-norms and t-conorms are defined for DHLq-ROF numbers. A wide range of generalized aggregation operators, such as DHLq-ROF Frank weighted averaging, DHLq-ROF Frank weighted geometric, DHLq-ROF Frank generalized weighted averaging, and DHLq-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne & Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields.

A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined Weights

A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined Weights

A data-driven minimum cost consensus model for group decision making with personality traits prediction

The minimum cost consensus model (MCCM) proposes an effective method for reaching group consensus in group decision-making problems. Conventional MCCM and its advanced models focus on the different behaviors and psychologies of decision-makers, but, it ignores the heterogeneity of decision-makers that activated them. Therefore, they need to assume the compromise limits and unit adjustment costs of decision-makers, which may be difficult to achieve in practice. To resolve this problem, this study will propose a novel data-driven minimum cost consensus model of different compromise limits and unit costs based on online Big Five personality traits prediction. First, this study uses the Convolutional Neural Network (CNN) and Bi-directional Long-Short Term Memory model (BiLSTM) to obtain the decision-maker's probability of agreeableness based on their Weibo online reviews. Second, a novel minimum cost consensus model considering the decision-maker's personality traits (MCCM-P) is established. To do that, the unit adjustment cost and the personalized compromise limits of decision-makers and their interrelations are defined based on the personality traits prediction. Finally, the MCCM-P is applied in a real group decision-making case study of a university student club activity selection. The result and comparative analysis show that the proposed MCCM model can obtain lower consensus reaching costs than the traditional method.

Applications of multi-attribute group decision-making models under triangular interval-type 2 fuzzy to risk preference

Applications of multi-attribute group decision-making models under triangular interval-type 2 fuzzy to risk preference

A Relative Projection-Based Multiattribute Group Decision-Making Model With Noncooperative Behavior Management and Application to NEV Supplier Selection.

This article reports a novel consensus model where a group of internal and external experts evaluate alternatives under multiple attributes and provide mutual evaluations. First, different from previous studies, the cognitive and interest conflicts of internal and external experts are considered simultaneously. But interest conflict is emphasized for internal experts, and cognitive conflict is mainly considered for external experts. Second, we explore the categorization and management methods of noncooperative behaviors (NCBs) of experts. The relative projection-based indexes are proposed for the first time to measure the degrees of cognitive and interest conflicts by using multiattribute preference matrices (MAPMs) and the weight vectors of attributes. A group of experts are divided into three categories and the corresponding management strategies are developed. Third, we investigate the consensus mechanism among experts with cognitive and interest conflicts. For reaching an acceptable consensus level, an adjustment process is proposed to revise some local entries in MAPMs and mutual evaluation matrix (MEM). A penalty mechanism is further established to dynamically update the weights of experts. An algorithm is designed to capture the consensus reaching process in multiattribute group decision making, where internal and external experts are distinguished by proposing a parameter. Finally, the high-performance battery supplier selection of new energy vehicle is studied to illustrate the proposed model. The results reveal that the efficiency of reaching consensus can be enhanced by using the developed model with effective management of NCBs.

Reliability-based ordinal consensus adjustment model for large scale group decision making

In large scale group decision-making (LSGDM), there are the substantial number of decision makers (DMs) with diverse knowledge, backgrounds, and interests related to the decision-making problem, and it is not possible to assure that all DMs are completely reliable. Thus, in order to enhance the quality of decision-making, it is necessary to analyze the reliabilities of DMs in LSGDM. This paper proposes the method to evaluate the reliabilities of DMs, sorts these DMs according to their degree of reliability, and investigates the consensus reaching process based on categories and an ordinal consensus measure. Considering the DMs' trust network, the uncertainty of a DM's evaluation information represented by a fuzzy preference relation (FPR), the deviation between a DM's FPR and those of the other DMs, and additive consistency of FPRs, the reliability of a DM is assessed using four criteria: PageRank centrality, professional competence, collaborative competence, and additive consistency. Following these reliability assessment criteria, ELECTRE-TRI is employed to sort DMs into three ordered categories according to DMs' different levels of reliability under the four assessment criteria. Furthermore, an improved ordinal consensus measure is designed to consider both the importance weights of positions and the deviation of Borda counts of the same alternative in two rankings. As for the consensus reaching process, due to the varied reliabilities of DMs in different categories, we propose a multiple strategies feedback mechanism for DMs in different categories. Finally, a numerical example is provided to illustrate the rationality and validity of the proposed model.

Consensus model for probabilistic linguistic multi-attribute group decision-making based on incomplete social trust networks

PurposeExperts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).Design/methodology/approachIn this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.FindingsThis study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.Originality/valueThis paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

Enhanced minimum cost consensus model for interval type-2 fuzzy social network group decision making focusing on individual attributes and group attitude

In group decision-making scenarios, consensus reaching is a crucial factor for resolving conflicts of opinion among groups, and social network analysis plays a significant role in fostering group consensus. This paper constructs a social network-driven minimum cost consensus framework for interval type-2 fuzzy group decision-making problems involving different individual attributes. Firstly, this paper proposes a theoretical social network analysis by the implementation of propagation efficiency, propagation reliability and opinion similarity to generate comprehensive trust relationships. The aim is to obtain missing trust relationships and individual centrality. Secondly, a minimum cost consensus model is constructed to give recommendation advice for identified inconsistent decision-makers according to their adjustment willingness. The novelty of the model lies in its capability to consider decision-makers’ individual attributes and group attitude or behavior. Then, this paper proposes an interval type-2 fuzzy Alternative by Alternative Comparison (ABAC) method for ranking multiple alternatives which address the rank reversal problem. Lastly, a case study on the selection of alternative charging point operators illustrates the effectiveness of the proposed method, and comparison and sensitivity analysis show the advantages of the proposed method.

Development and implementation of digital pedagogical support systems in the context of educational equity

In response to the covid-19 pandemic, the digital strategy of education has become a key pedagogical approach for higher engineering disciplines. In this complex environment, the outcome-oriented higher engineering discipline faces the challenge of educational equity in terms of curriculum design and learning outcome assessment for its online engineering practical courses. In order to solve this problem, this study develops a digital teaching assistant system to facilitate the reform of educational equity in higher engineering disciplines. The teaching and learning functions of the system aim to support the interaction between teachers and students in the teaching process and the visual display of learning outcomes, and its intelligent evaluation and analysis function is based on the multi-criterion group decision-making model of Quality Function Deployment (QFD) combined with t-test to realize the evaluation of course learning outcomes. The results show that the academic performance of this system is significantly better than that of other similar systems. The statistical results of the teacher-student satisfaction survey of the two systems show that the teacher-student interaction and multi-dimensional learning evaluation analysis report function provided by the system are the most highly recognized, and the system also has better educational equity in resource recommendation, interaction and collaboration, learning achievement display and learning achievement evaluation, etc. In addition, the system is more in line with the teaching characteristics of higher engineering disciplines, which can provide effective assistance for teachers of higher engineering disciplines to improve teaching plan and cultivate students’ disciplinary practicality, consequently creating favorable conditions for the establishment of teaching equity.

Correction to: A Large-Scale Group Decision-Making Model Considering Expert Authority Degree and Relationship Evolution Under Social Network

Correction to: A Large-Scale Group Decision-Making Model Considering Expert Authority Degree and Relationship Evolution Under Social Network