Article
Management
Zhibin Wu, Rong Yuan, Jiancheng Tu
Summary: This study explores the issue of individual decision consistency and proposes an optimization model to address the challenge of both ordinal and cardinal consistencies coexisting. Additionally, a framework is designed to provide a complete strategy for consistency control.
GROUP DECISION AND NEGOTIATION
(2021)
Article
Computer Science, Artificial Intelligence
Huayan Wen, Xinxing Wu, Gul Deniz Cayli
Summary: In this paper, we focus on new characterizations of uninorms allowed to act on more general bounded lattices. Several necessary and sufficient conditions are presented to verify the construction approaches, yielding a uninorm on bounded lattices.
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Zhen Ming Ma, Ze Shui Xu, Zun Wei Fu, Wei Yang
Summary: This paper solves the problem of deriving priorities of objects from fuzzy preference relations using representable uninorms. It provides alternative definitions of representable uninorm-based consistency for complete and incomplete fuzzy preference relations. A procedure to evaluate incomplete fuzzy preference relations with arbitrary n - 1 independent preference values is also provided. Furthermore, a novel acceptable consistency is defined and a method to check and reach consistency of fuzzy preference relations is proposed.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
E. Torres-Manzanera, S. Diaz, F. Chiclana, S. Montes
Summary: This study completes an incomplete fuzzy weak preference relation in a consistent way to derive a preference relation that preserves the original information. A complete transitive preference relation is obtained if the decision maker is coherent, while a degree of transitivity is defined to provide the most coherent preference relation when preference values violate transitivity.
INFORMATION FUSION
(2022)
Article
Computer Science, Artificial Intelligence
Laszlo Gyarmati, Eva Orban-Mihalyko, Csaba Mihalyko, Zsombor Szadoczki, Sandor Bozoki
Summary: This paper investigates and compares pairwise comparison models and the stochastic Bradley-Terry model, and proves that they provide the same priority vectors for consistent comparisons. For incomplete comparisons, all filling in levels are considered. The simulations show that the optimal subsets and sequences for the Bradley-Terry model and the Thurstone model are also the same.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Weiwei Guo, Zaiwu Gong, Xiaoxia Xu, Enrique Herrera-Viedma
Summary: This article explores consistency and ranking problems with incomplete interval fuzzy preference relations, treating them as linear uncertain preference relations. By introducing belief degree and inverse uncertainty distributions, the study proposes models to solve weight vectors under both consistency scenarios.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Mathematical & Computational Biology
Georgios Seitidis, Stavros Nikolakopoulos, Ioannis Ntzoufras, Dimitris Mavridis
Summary: The reliability of network meta-analysis (NMA) results depends on the plausibility of the transitivity assumption, which assumes that the distribution of effect modifiers is similar across treatment comparisons. Different methods have been proposed to evaluate consistency, and our method, stochastic search inconsistency factor selection (SSIFS), uses variable selection techniques to determine the inclusion of inconsistency factors in the model. Our approach quantifies the posterior inclusion probability of each inconsistency factor and incorporates differences between direct and indirect evidence. We also construct an informative prior based on historical data from 201 published network meta-analyses.
STATISTICS IN MEDICINE
(2023)
Article
Economics
Christopher P. Chambers, Federico Echenique, Nicolas S. Lambert
Summary: This study focuses on preferences estimated from finite choice experiments and establishes sufficient conditions for convergence to a unique underlying true preference. The weak conditions provided are valid in a wide range of economic environments, unifying the revealed preference tradition with models that account for errors.
Article
Computer Science, Artificial Intelligence
Tao Li, Liyuan Zhang, Zhenglong Zhang
Summary: In this paper, the linguistic q-rung orthopair fuzzy preference relations (Lq-ROFPRs) are studied and applied to a multi-criteria decision-making (MCDM) problem. The paper introduces the multiplicative consistency of Lq-ROFPRs, establishes a consistency-based model for deriving the normalized linguistic q-rung orthopair fuzzy priority weight vector. The paper also defines the concept of acceptably multiplicative consistency and formulates an optimization model for repairing the unacceptably multiplicative consistent Lq-ROFPR and deals with the incomplete Lq-ROFPR using a programming model.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Rong Yuan, Zhibin Wu, Jiancheng Tu
Summary: An optimization model is proposed that incorporates both cardinal consistency and ordinal consistency to estimate unknown preferences in incomplete fuzzy preference relations (IFPRs). The model explicitly controls ordinal consistency, minimizes the extent of modifications to preferences, and manages cardinal and ordinal consistencies when a predefined level of consensus is achieved to a greater extent than prevalent approaches. The proposed model provides more reliable individual and group preference relations compared to current methods.
FUZZY SETS AND SYSTEMS
(2023)
Article
Economics
Fernanda Senra de Moura, Gil Riella
Summary: The study shows that in the case of incomplete preferences, dynamic consistency can be explained through the subjective state space and the comparative theory of flexibility. The agent's behavior after receiving an objective signal is influenced by changes in preferences for flexibility before and after the signal.
THEORY AND DECISION
(2021)
Article
Computer Science, Information Systems
Zhiming Zhang, Shyi-Ming Chen
Summary: This paper proposes a novel group decision making method for incomplete q-rung orthopair fuzzy preference relations (q-ROFPRs) environments. The method includes an additive consistency definition and models for obtaining missing judgments. The proposed method aims to improve consensus degrees and outperform existing methods for GDM in incomplete q-ROFPRs environments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Shuping Wan, Huwei Yuan, Jiuying Dong
Summary: This paper investigates decision making with incomplete IMPRs and proposes a new method to estimate missing values in incomplete IMPRs by minimizing a consistency index. Optimization models are constructed to obtain the most pessimistic and optimistic acceptably multiplicative consistent IMPRs, taking into account decision makers' risk attitudes.
INFORMATION SCIENCES
(2021)
Article
Cell Biology
Dongyuan Li, Le Chang
Summary: The study investigates how face-selective cells represent incomplete faces, including face fragments and occluded faces. The results show that the preferred face regions identified with these two types of stimuli are dissociated in many face cells, which can be explained by the nonlinear integration of information from different face parts. Additionally, identity-related facial features are represented in a subspace orthogonal to the nonlinear dimension of face completeness, supporting a condition-general code of facial identity.
Article
Computer Science, Artificial Intelligence
Zhibin Wu, Jiancheng Tu
Summary: This paper proposes optimization models to achieve transitive preferences for solving individual consistency and group consensus problems, considering ordinal consistency in a controlled manner. The models provide an optimal way to minimize modifications in deriving transitive preferences, compared to existing methods. Future research should focus on managing optimization models considering ordinal consistency and classical cardinal consistency indices.
INFORMATION FUSION
(2021)
Article
Engineering, Industrial
Yucheng Dong, Siqi Wu, Xiaoping Shi, Yao Li, Francisco Chiclana
Summary: This article investigates the clustering of failure modes based on their risks in FMEA practice. It proposes the additive N-clustering problem and explores the characteristics of exogenous clustering methods and endogenous clustering methods. It also introduces the Consensus-based ENdogenous Clustering Method (CENCM) as a solution for cases where accurate category thresholds are difficult to provide, and validates its effectiveness through comparisons and simulation experiments.
Article
Automation & Control Systems
Cristina Zuheros, Eugenio Martinez-Camara, Enrique Herrera-Viedma, Francisco Herrera
Summary: The wisdom of the crowd theory states that a nonexpert crowd makes smarter decisions than a reduced set of experts. Evaluations from social networks can enhance the quality of decision-making models. We propose a crowd decision-making model guided by sentiment analysis, which incorporates all the evaluation shades and tackles the lack of information using sparse representation. The results show that integrating the wisdom of the crowd and the different shades of the evaluations enhances the quality of the decision.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Alejandro Pena, Juan C. Tejada, Juan David Gonzalez-Ruiz, Lina Maria Sepulveda-Cano, Francisco Chiclana, Fabio Caraffini, Mario Gongora
Summary: This paper presents a model for a serial robotic system with flexible joints (RFJ) using Euler-Lagrange equations. It also proposes a Stochastic Flexible-Adaptive Neural Integrated System (SF-ANFIS) for identifying and controlling the RFJ. The SF-ANFIS model shows better performance in both identification and control stages compared to the MADALINE model, with improved statistical indices and the ability to cancel oscillations.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Qianlei Jia, Enrique Herrera-Viedma
Summary: In this article, a new layout coordinate system is defined to map Z-numbers to q-ROFSs. The genetic algorithm is adopted to derive the potential probability distribution. An approach for calculating the weighted information entropy of Z-numbers is proposed and proven to be rational. A linguistic Z-PFS weighted aggregation operator is presented, and a score function is defined in the coordinate system. Finally, a decision-making model is constructed based on the new solution.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiang Deng, Jianming Zhan, Enrique Herrera-Viedma, Francisco Herrera
Summary: Behavioral decision theory modifies classic decision-making theories to make them more applicable in realistic scenarios. Regret theory, an important component of behavioral decision theories, has been widely used in theories and applications. In this study, a generalized three-way decision method is proposed based on regret theory for incomplete multiscale decision information systems. Experimental results show that the decision-making results of the proposed method maintain over 97% consistency in incomplete information systems.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Shubo Wang
Summary: This article proposes a novel nonlinear uncertainty estimator-based time-varying sliding mode control (SMC) scheme for servo systems with prescribed performance. The scheme uses a nonlinear uncertainty estimator to handle unknown nonlinearities and a robust integral of the sign of the error (RISE) feedback to handle estimation errors and uncertainties. A modified prescribed performance function (PPF) is incorporated into the control design to restrict tracking errors within predefined boundaries, and a time-varying sliding mode (TVSM) controller is developed to improve control performance. The validity and feasibility of the proposed scheme are verified through simulations and experiments based on a motor driving system.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Mathematics, Applied
Tamas Galli, Francisco Chiclana, Francois Siewe
Summary: This paper builds upon previous research on software product quality modelling and aims to provide a practical approach for software professionals. It explains how to interpret and utilize the established taxonomy of software product quality models, and how to determine the validity of statements based on these models. It also discusses tailoring and adjusting quality models to fit the specific needs of a project.
Article
Computer Science, Artificial Intelligence
Xinli You, Fujun Hou, Francisco Chiclana
Summary: Large-scale group decision-making problems have been a subject of interest for scholars. The distribution linguistic preference relation is a practical tool to describe decision makers' preferences. Opinion conflict and non-cooperative behaviors can occur among large-scale decision makers, making it necessary to establish a consensus reaching process.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Qi Sun, Francisco Chiclana, Jian Wu, Yujia Liu, Changyong Liang, Enrique Herrera-Viedma
Summary: This article proposes a novel framework for managing the noncooperative behavior of subgroups in large-scale group decision making using weight penalty. The framework defines a trust-consensus index (TCI) by combining trust score and consensus degree and uses an algorithm to detect subgroups in a large network. A weight penalty feedback model is established to manage subgroups that are detected as discordant and noncooperative. The article also provides a detailed analysis on computing the optimal penalty parameter to prevent excessive penalization and includes numerical and comparative analyses to verify the proposed method's validity.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Hengjie Zhang, Shenghua Liu, Yucheng Dong, Francisco Chiclana, Enrique Enrique Herrera-Viedma
Summary: This study presents a framework called minimum cost consensus-based failure mode and effect analysis (MCC-FMEA) that considers experts' limited compromise and tolerance behaviors. It introduces two types of behaviors, limited compromise behavior and tolerance behavior, to the MCC-FMEA. The study develops and analyzes a minimum compromise adjustment consensus model and a maximum consensus model with limited compromise behaviors, resulting in an interactive MCC-FMEA framework.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yejun Xu, Qianqian Wang, Francisco Chiclana, Enrique Herrera-Viedma
Summary: This article presents a new inconsistency identification and modification (IIM) method to improve the consistency of inconsistent fuzzy reciprocal preference relations (FPRs) while retaining the original preference values. The method also addresses the issue of inconsistent FPRs with missing values and provides an estimation approach for the missing preferences. Numerical examples, simulation experiments, and comparisons with existing methods demonstrate the correctness, effectiveness, and robustness of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Hengjie Zhang, Yucheng Dong, Francisco Chiclana
Summary: This study investigates conflict resolution and mediation in technology transfer disputes in new product collaborative development using a graph model and minimum cost. It analyzes the stakeholders, their options, feasible states, and decision makers' preferences using a graph model theory. It also designs an inverse graph model with minimum cost to specify which decision-makers' preferences lead to a desired solution, facilitating mediation or third-party influence in the conflict. The methodology is applied to a technology transfer dispute case study.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Zhen-Song Chen, Zhengze Zhu, Xian-Jia Wang, Francisco Chiclana, Enrique Herrera-Viedma, Miroslaw J. Skibniewski
Summary: In this study, a multiobjective optimization-driven approach is proposed for generating collective opinion in decision analysis. The notion of fairness concern utility is adapted to reflect fairness concerns among individuals, and the extended Bonferroni mean is used to aggregate individual fairness concern utilities. The effectiveness and efficiency of the proposed approach are demonstrated through its application in maturity assessment tasks.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Jose Ramon Trillo, Enrique Herrera-Viedma, Juan Antonio Morente-Molinera, Francisco Javier Cabrerizo
Summary: Debate is a process of arriving at a reasoned opinion, requiring individuals to defend their judgments. It has been used in group decision-making (GDM) to improve decisions, but aggressive language during debates can hinder consensus. To address this, a new method incorporating sentiment analysis techniques is proposed to identify aggressive comments. Two procedures are developed based on information extracted during debates to assign weights to experts and introduce new consensus measures for the final decision. This method utilizes extracted information throughout the decision process, aligning with real-world GDM processes.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xinli You, Fujun Hou, Francisco Chiclana
Summary: This study aims to develop a reputation-based trust model for establishing trust relationships among experts in a group decision-making framework. The research achieves this by defining a trust credibility measure, designing direct trust feedback, and proposing a global reputation model.
INFORMATION FUSION
(2024)
