Suchergebnisse

The work was supported by the National Natural Science Foundation of China (71971145, 71771156, 72171158), the Andalusian Government under Project P20-00673, and also by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033. ; Risk assessment is a vital part in project management. It is possible that experts may provide comprehensive linguistic preference information in distinct forms with respect to different aspects of the risk assessment problem in investment management. It is a challenge to model and deal with comprehensive linguistic preference assessments in multiple forms given by experts. In this regard, this paper defines the generalised probabilistic linguistic preference relation (GPLPR) to represent different forms of linguistic preference information in a unified structure. Then, a probability cutting method is proposed to simplify the representation of a GPLPR. Afterwards, a graph-theory-based method is developed to improve the consistency degree of a GPLPR. A group decision making method with GPLPRs is then proposed to carry on the risk assessment in project management. Discussions regarding the comparative analysis and managerial insights are given. ; National Natural Science Foundation of China (NSFC) 71971145 71771156 72171158 ; Andalusian Government P20-00673 ; Spanish Government PID2019-103880RB-I00/AEI/10.13039/501100011033

Human personality plays a crucial role in decision-making and it has paramount importance when individuals negotiate with each other to reach a common group decision. Such situations are conceivable, for instance, when a group of individuals want to watch a movie together. It is well known that people influence each other's decisions, the more assertive a person is, the more influence they will have on the final decision. In order to obtain a more realistic group recommendation system (GRS), we need to accommodate the assertiveness of the different group members' personalities. Although pairwise preferences are long-established in group decision-making (GDM), they have received very little attention in the recommendation systems community. Driven by the advantages of pairwise preferences on ratings in the recommendation systems domain, we have further pursued this approach in this paper, however we have done so for GRS. We have devised a three-stage approach to GRS in which we 1) resort to three binary matrix factorization methods, 2) develop an influence graph that includes assertiveness and cooperativeness as personality traits, and 3) apply an opinion dynamics model in order to reach consensus. We have shown that the final opinion is related to the stationary distribution of a Markov chain associated with the influence graph. Our experimental results demonstrate that our approach results in high precision and fairness. ; Spanish Government PID2019-10380RBI00/AEI/10. 13039/501100011033 ; Andalusian Government P20_00673

In social network group decision making (SNGDM), the consensus reaching process (CRP) is used to help decision makers with social relationships reach consensus. Many CRP studies have been conducted in SNGDM until now. This paper provides a review of CRPs in SNGDM, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) the CRP paradigm based on opinion evolution. Furthermore, identified research challenges are put forward to advance this area of research. ; National Natural Science Foundation of China (NSFC) 71571124 71725001 ; Sichuan University sksyl201705 ; Spanish Ministry of Economy and Competitiveness TIN2016-75850-R ; European Union (EU)

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal–spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min–max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic. ; University of Macau MYRG2016-00069-FST ; FDCT Macau FDCT/126/2014/A3 ; 2018 Guangzhou Science and Technology Innovation and Development of Special Funds ; 201907010001 ; EF003/FST-FSJ/2019/GSTIC

This work was supported in part by the NSF of China under grants 71171160 and 71571124, in part by the SSEM Key Research Center at Sichuan Province under grant xq15b01, in part by the FEDER funds under grant TIN2013-40658-P, and in part by Andalusian Excellence Project under grant TIC-5991. ; The consensus reaching process (CRP) is a dynamic and iterative process for improving the consensus level among experts in group decision making. A large number of non-cooperative behaviors exist in the CRP. For example, some experts will express their opinions dishonestly or refuse to change their opinions to further their own interests. In this study, we propose a novel consensus framework for managing non-cooperative behaviors. In the proposed framework, a self-management mechanism to generate experts' weights dynamically is presented and then integrated into the CRP. This self-management mechanism is based on multi-attribute mutual evaluation matrices (MMEMs). During the CRP, the experts can provide and update their MMEMs regarding the experts' performances (e.g., professional skill, cooperation, and fairness), and the experts' weights are dynamically derived from the MMEMs. Detailed simulation experiments and comparison analysis are presented to justify the validity of the proposed consensus framework in managing the non-cooperative behaviors. ; National Natural Science Foundation of China 71171160 71571124 ; SSEM Key Research Center at Sichuan Province xq15b01 ; European Union (EU) TIN2013-40658-P ; Andalusian Excellence Project TIC-5991

This paper presents a survey of some fuzzy linguistic information access systems. The review shows information retrieval systems, filtering systems, recommender systems, and web quality evaluation tools, which are based on tools of fuzzy linguistic modelling. The fuzzy linguistic modelling allows us to represent and manage the subjectivity, vagueness and imprecision that is intrinsic and characteristic of the processes of information searching, and, in such a way, the developed systems allow users the access to quality information in a flexible and user-adapted way. ; European Union (EU) TIN2007-61079 PET2007-0460 ; Ministry of Public Works 90/07 ; Excellence Andalusian Project TIC5299

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 'a recommendation being more acceptable the higher the level of trust it derives from'. An optimization model is built to minimise the total adjustment cost of reaching consensus by determining personalized feedback adoption coefficient based on individuals' 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 71910107002 ; Spanish Government PID2019-103880RB-I00/AEI/10.13039/501100011033

This work was supported in part by the National Natural Science Foundation of China under Grant 71991460, Grant 71991465, Grant 71871149, Grant 71910107002, and Grant 71725001; in part by the Research Foundation of Education Bureau of Hunan Province, China, under Grant 20B147; and in part by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033. ; Inspired by the continuous opinion and discrete action (CODA) model, bounded confidence and social networks, the bounded confidence evolution of opinions and actions in social networks is investigated and a social network opinions and actions evolutions (SNOAEs) model is proposed. In the SNOAE model, it is assumed that each agent has a CODA for a certain issue. Agents' opinions are private and invisible, that is, an individual agent only knows its own opinion and cannot obtain other agents' opinions unless there is a social network connection edge that allows their communication; agents' actions are public and visible to all agents and impact other agents' actions. Opinions and actions evolve in a directed social network. In the limitation of the bounded confidence, other agents' actions or agents' opinions noticed or obtained by network communication, respectively, are used by agents to update their opinions. Based on the SNOAE model, the evolution of the opinions and actions with bounded confidence is investigated in social networks both theoretically and experimentally with a detailed simulation analysis. Theoretical research results show that discrete actions can attract agents who trust the discrete action, and make agents to express extreme opinions. Simulation experiments results show that social network connection probability, bounded confidence, and the opinion threshold of action choice parameters have strong impacts on the evolution of opinions and actions. However, the number of agents in the social network has no obvious influence on the evolution of opinions and actions. ; National Natural Science Foundation of China (NSFC) 71991460 71991465 71871149 71910107002 71725001 ; Research Foundation of Education Bureau of Hunan Province, China 20B147 ; Spanish Government PID2019-103880RB-I00/AEI/10.13039/501100011033

The consensus reaching process is the most important step in a group decision making scenario. This step is most frequently identified as a process consisting of some discussion rounds in which several decision makers, which are involved in the problem, discuss their points of view with the purpose of obtaining the maximum agreement before making the decision. Consensus reaching processes have been well studied and a large number of consensus approaches have been developed. In recent years, the researchers in the field of decision making have shown their interest in social networks since they may be successfully used for modelling communication among decision makers. However, a social network presents some features differentiating it from the classical scenarios in which the consensus reaching processes have been applied. The objective of this study is to investigate the main consensus methods proposed in social networks and bring out the new challenges that should be faced in this research field. ; European Union (EU) TIN2013-40658-P TIN2016-75850-P ; RUDN University 5-100 ; National Natural Science Foundation of China (NSFC) 71571166

Multiple experts decision-making (MEDM) can be regarded as a situation where a group of experts are invited to provide their opinions by evaluating the given alternatives, and then select the optimal alternative(s). As a useful linguistic expression model, linguistic preference orderings (LPOs) were established in which the order of alternatives and the relationships between two adjacent alternatives are fused well. Considering that prospect theory has the superiority in depicting risk attitudes (risk seeking for losses and risk aversion for gains) during the uncertain decision-making process, this paper develops a consensus model based on prospect theory to deal with MEDM problems with LPOs. Firstly, each LPO provided by expert is transformed into the responding DHLPR with complete consistency. Then, the reference point of expert is determined and the prospect preference matrix is established. Moreover, we can obtain the overall prospect consensus degree for a MEDM problem by calculating the similarity degree between individual and collective prospect preference matrix. Furthermore, a consensus improvement method is developed to complete the consensus reaching process. Finally, we apply the proposed method to deal with a practical MEDM problem involving the construction project investment, and make some comparative analyses with existing methods. ; National Natural Science Foundation of China (NSFC) 71771155 ; China Postdoctoral Science Foundation 2020M680151 ; Sichuan Postdoctoral Science special Foundation ; Sichuan University Postdoctoral Interdisciplinary Innovation Startup Foundation ; Fundamental Research Funds for the Central Universities YJ202015 ; European Union (EU) TIN2016-75850-R ; Sichuan Province System Science and Enterprise Development Research Center Xq20B03

This work was supported in part by the NSF of China under Grant 71901182, Grant 71601133, and Grant 71871149; in part by Sichuan University under Grant sksyl201705 and Grant YJ201906; in part by Southwest Jiaotong University under Grant YJSY-DSTD201918; in part by the China Postdoctoral Science Foundation under Grant 2020M673283 and Grant 2682021ZTPY073; and in part by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033. ; Consistency is an important issue in linguistic decision making with various consistency measures and consistency improving methods available in the literature. However, existing linguistic consistency studies omit the fact that words mean different things for different people, that is, decision makers' personalized individual semantics (PISs) over their expressed linguistic preferences are ignored. Therefore, the aim of this article is to propose a novel consistency improving approach based on PISs in linguistic group decision making. The proposed approach combines the characteristics of personalized representation and integrates the PIS-based model in measuring and improving the consistency of linguistic preference relations. A detailed numerical and comparative analysis to support the feasibility of the proposed approach is provided. ; National Natural Science Foundation of China (NSFC) 71901182 71601133 71871149 ; Sichuan University sksyl201705 YJ201906 ; Southwest Jiaotong University YJSY-DSTD201918 ; China Postdoctoral Science Foundation 2020M673283 2682021ZTPY073 ; Spanish Government PID2019-103880RB-I00

The authors thank the editor and anonymous referees for their valuable comments and insightful recommendations, and thank Prof. Yucheng Dong for the helpful suggestions. This research was supported in part by grants from the National Natural Science Foundation of China (#71874023, #U1811462, #71725001, #71771037, #71971042, #71910107002) and Major project of the National Social Science Foundation of China (#19ZDA092). Enrique Herrera-Viedma is supported by the FEDER funds in the project TIN2016-75850-R. ; Non-cooperative behavior is a common situation in large-scale group decision-making (LSGDM) problems. In addition, decision makers in LSGDM often use different preference formats to express their opinions, due to their educational backgrounds, knowledge, and experiences. Heterogeneous preference information and non-cooperative behaviors bring challenges to LSGDM. This study develops a consensus reaching model to address heterogeneous LSGDM with non-cooperative behaviors and discuss its application in financial inclusion. Specifically, the cosine similarity degree is introduced to build a distance measure for different preference structures. Clustering analysis is employed to divide large-scale groups and handle non-cooperative behaviors in LSGDM. A consensus degree and a weighting process are proposed to decrease the influence of non-cooperative behaviors and facilitate the consensus reaching process. The convergence of the proposed approach is proven by theoretical and simulation analyses. Experimental studies are carried out to compare the performances of the proposed approach with existing methods. Finally, a real-life example from the "targeted poverty reduction project" in China is presented to validate the proposed approach. The selection of beneficiaries in finance inclusion is difficult due to the lack of credit history, the large number of participants, and the conflicting views of participants. The results showed that the proposed consensus model can integrate opinions of participants using diverse preference formats and reach an agreement efficiently. ; National Natural Science Foundation of China (NSFC) 71874023 U1811462 71725001 71771037 71971042 71910107002 ; Major project of the National Social Science Foundation of China 19ZDA092 ; European Union (EU) TIN2016-75850-R