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Journal articles on the topic 'Large group decision-making'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

BÄCK, EMMA A., PETER ESAIASSON, MIKAEL GILLJAM, OLA SVENSON, and TORUN LINDHOLM. "Post-decision consolidation in large group decision-making." Scandinavian Journal of Psychology 52, no. 4 (2011): 320–28. http://dx.doi.org/10.1111/j.1467-9450.2011.00878.x.

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2

Wu, Peng, Qun Wu, Ligang Zhou, and Huayou Chen. "Optimal group selection model for large-scale group decision making." Information Fusion 61 (September 2020): 1–12. http://dx.doi.org/10.1016/j.inffus.2020.03.002.

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3

Zhan, Qianshan, Chao Fu, and Min Xue. "Distance-Based Large-Scale Group Decision-Making Method with Group Influence." International Journal of Fuzzy Systems 23, no. 2 (2021): 535–54. http://dx.doi.org/10.1007/s40815-020-00993-9.

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4

Zhong, Xiangyu, Xuanhua Xu, Xiaohong Chen, and Mark Goh. "Large group decision-making incorporating decision risk and risk attitude: A statistical approach." Information Sciences 533 (September 2020): 120–37. http://dx.doi.org/10.1016/j.ins.2020.04.003.

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5

Zhou, Shenghai, Xuanhua Xu, Yanju Zhou, and Xiaohong Chen. "A Large Group Decision-Making Method Based on Fuzzy Preference Relation." International Journal of Information Technology & Decision Making 16, no. 03 (2017): 881–97. http://dx.doi.org/10.1142/s021962201550039x.

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Aiming at the large group decision-making problem in which every decision maker compares pairwise alternatives with fuzzy preference relations, this paper proposes a fuzzy preference relation decision-making method of large group based on conflicts. Firstly, priority should be given to the preference difference under complex large group environment, so we define the conflict degree of two fuzzy preference relations, which contributes to cluster analysis on preferences of the decision maker and thus forms several different clusters. Based on this, we simulate and analyze the threshold of confli

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6

Cai, Chen-guang, Xuan-hua Xu, Pei Wang, and Xiao-hong Chen. "A multi-stage conflict style large group emergency decision-making method." Soft Computing 21, no. 19 (2016): 5765–78. http://dx.doi.org/10.1007/s00500-016-2155-5.

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7

Xu, Xuan-hua, and Xintong Luo. "Information entropy risk measure applied to large group decision-making method." Soft Computing 23, no. 13 (2018): 4987–97. http://dx.doi.org/10.1007/s00500-018-3162-5.

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8

Dyer, John R. G., Anders Johansson, Dirk Helbing, Iain D. Couzin, and Jens Krause. "Leadership, consensus decision making and collective behaviour in humans." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1518 (2008): 781–89. http://dx.doi.org/10.1098/rstb.2008.0233.

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This paper reviews the literature on leadership in vertebrate groups, including recent work on human groups, before presenting the results of three new experiments looking at leadership and decision making in small and large human groups. In experiment 1, we find that both group size and the presence of uninformed individuals can affect the speed with which small human groups (eight people) decide between two opposing directional preferences and the likelihood of the group splitting. In experiment 2, we show that the spatial positioning of informed individuals within small human groups (10 peo

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Finch, John H., and Nicola C. Dinnei. "Capturing Knightian Advantages of Large Business Organisations Through Group Decision-making Processes." International Journal of the Economics of Business 8, no. 3 (2001): 379–403. http://dx.doi.org/10.1080/13571510110081187.

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10

Wang, Pei, Xuanhua Xu, Shuai Huang, and Chenguang Cai. "A Linguistic Large Group Decision Making Method Based on the Cloud Model." IEEE Transactions on Fuzzy Systems 26, no. 6 (2018): 3314–26. http://dx.doi.org/10.1109/tfuzz.2018.2822242.

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11

Brown, James D. "The Town Council Meeting: Decision Making through a Large Group Role Play." Health Education 16, no. 5 (1985): 49. http://dx.doi.org/10.1080/00970050.1985.10615878.

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12

Zhang, Zhen, Chonghui Guo, and Luis Martinez. "Managing Multigranular Linguistic Distribution Assessments in Large-Scale Multiattribute Group Decision Making." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 11 (2017): 3063–76. http://dx.doi.org/10.1109/tsmc.2016.2560521.

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13

Ding, Xue-Feng, Li-Xia Zhu, Mei-Shun Lu, Qi Wang, and Yi-Qi Feng. "A Novel Linguistic Z-Number QUALIFLEX Method and Its Application to Large Group Emergency Decision Making." Scientific Programming 2020 (June 26, 2020): 1–12. http://dx.doi.org/10.1155/2020/1631869.

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After an unconventional emergency event occurs, a reasonable and effective emergency decision should be made within a short time period. In the emergency decision making process, decision makers’ opinions are often uncertain and imprecise, and determining the optimal solution to respond to an emergency event is a complex group decision making problem. In this study, a novel large group emergency decision making method, called the linguistic Z-QUALIFLEX method, is developed by extending the QUALIFLEX method using linguistic Z-numbers. The evaluations of decision makers on the alternative soluti

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14

Zhang, Linling, Jinjian Yuan, Xinyu Gao, and Dawei Jiang. "Public transportation development decision-making under public participation: A large-scale group decision-making method based on fuzzy preference relations." Technological Forecasting and Social Change 172 (November 2021): 121020. http://dx.doi.org/10.1016/j.techfore.2021.121020.

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15

Singh, Anjali, and Anjana Gupta. "Optimization-Based Consensus Model to Solve Multi-Criteria Large Group Decision Making Problems." International Game Theory Review 22, no. 02 (2020): 2040010. http://dx.doi.org/10.1142/s0219198920400101.

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In this contribution, a consensus model is proposed to acquire a unified and converging solution of multi-criteria large group decision making problems. Unlike the iterative process and feedback mechanism based models, the suggested approach features the optimization theory to establish the consensus in one go only among the efficient experts. The time salvation characteristic of the model makes it expedient for the emergency planning and management decision problems. The algorithm is validated using the hurricane evacuation notification time problem of United States.

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16

Xu, Xuan-Hua, Qian Sun, Bin Pan, and Bingsheng Liu. "Two-layer weight large group decision-making method based on multi-granularity attributes." Journal of Intelligent & Fuzzy Systems 33, no. 3 (2017): 1797–807. http://dx.doi.org/10.3233/jifs-152590.

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17

Tang, Ming, Xiaoyang Zhou, Huchang Liao, Jiuping Xu, Hamido Fujita, and Francisco Herrera. "Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making." Knowledge-Based Systems 180 (September 2019): 62–74. http://dx.doi.org/10.1016/j.knosys.2019.05.019.

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18

Liu, Hu-Chen, Zhaojun Li, Jian-Qing Zhang, and Xiao-Yue You. "A large group decision making approach for dependence assessment in human reliability analysis." Reliability Engineering & System Safety 176 (August 2018): 135–44. http://dx.doi.org/10.1016/j.ress.2018.04.008.

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19

Yin, Xuanpeng, Xuanhua Xu, and Bin Pan. "Selection of Strategy for Large Group Emergency Decision-making based on Risk Measurement." Reliability Engineering & System Safety 208 (April 2021): 107325. http://dx.doi.org/10.1016/j.ress.2020.107325.

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20

Zhou, Xiaokang, Wei Liang, Suzhen Huang, and Miao Fu. "Social Recommendation With Large-Scale Group Decision-Making for Cyber-Enabled Online Service." IEEE Transactions on Computational Social Systems 6, no. 5 (2019): 1073–82. http://dx.doi.org/10.1109/tcss.2019.2932288.

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21

Yin, Xuanpeng, Xuanhua Xu, and Xiaohong Chen. "Risk mechanisms of large group emergency decision-making based on multi-agent simulation." Natural Hazards 103, no. 1 (2020): 1009–34. http://dx.doi.org/10.1007/s11069-020-04023-7.

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22

Tang, Ming, Huchang Liao, Jiuping Xu, Dalia Streimikiene, and Xiaosong Zheng. "Adaptive consensus reaching process with hybrid strategies for large-scale group decision making." European Journal of Operational Research 282, no. 3 (2020): 957–71. http://dx.doi.org/10.1016/j.ejor.2019.10.006.

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23

Wu, Tong, Xinwang Liu, and Jindong Qin. "A linguistic solution for double large-scale group decision-making in E-commerce." Computers & Industrial Engineering 116 (February 2018): 97–112. http://dx.doi.org/10.1016/j.cie.2017.11.032.

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24

Du, Yuan-Wei, Qun Chen, Ya-Lu Sun, and Chun-Hao Li. "Knowledge structure-based consensus-reaching method for large-scale multiattribute group decision-making." Knowledge-Based Systems 219 (May 2021): 106885. http://dx.doi.org/10.1016/j.knosys.2021.106885.

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25

Chen, Zhen-Song, Lan-Lan Yang, Kwai-Sang Chin, et al. "Sustainable building material selection: An integrated multi-criteria large group decision making framework." Applied Soft Computing 113 (December 2021): 107903. http://dx.doi.org/10.1016/j.asoc.2021.107903.

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26

Gao, Yuxuan, and Bingzhen Sun. "The Large-Small Group-Based Evolutionary Game on Knowledge Sharing in Uncertain Environment under the Background of Telemedicine Service." Complexity 2020 (October 8, 2020): 1–13. http://dx.doi.org/10.1155/2020/9818417.

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Group decision-making is an effective method to deal with complex unstructured problem in uncertain environment, and it has been widely used in many fields such as medical decision-making. This is a novel study that considers the decision-makers as different groups in the group decision-making problems in uncertain environment. This paper aims to present a novel method combined with evolutionary game for decision-making problem of knowledge sharing in uncertain environment between the large and the small groups in Telemedicine service. For this purpose, the evolutionary game model is construct

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27

Liao, Quan Mi, Chao Wang, Yu Wang, and Sheng Huang. "Application of Targeted Consistency-Adjusting Method in Group Decision-Making." Applied Mechanics and Materials 590 (June 2014): 773–77. http://dx.doi.org/10.4028/www.scientific.net/amm.590.773.

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For multiple attribute group decision-making problems in intuitionistic fuzzy theory, a new targeted consistency-adjusting method is proposed to insure the efficiency and consistency of the decision process as well as preserving the original information of the experts. To reach this goal, the new method modifies the evaluation information of the expert who has large difference with the group evaluation information in the level of attribute which differs from the traditional modification without target. By applying the new method in longitudinal position selection model of engine room in naval

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28

Wang, Lei, and Huifeng Xue. "Group Decision-Making Method Based on Expert Classification Consensus Information Integration." Symmetry 12, no. 7 (2020): 1180. http://dx.doi.org/10.3390/sym12071180.

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Existing decision-making methods are mostly a simple aggregation of expert decision information when solving large group decision-making problems. In these methods, priority should be given to expert weight information; however, it is difficult to avoid the loss of expert decision information in the decision-making process. Therefore, a new idea to solve the problem of large group decision-making by combining the expert group clustering algorithm and the group consensus model is proposed in this paper in order to avoid the disadvantages of subjectively assigning expert weights. First, expert g

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29

Cronin, Adam L., and Martin C. Stumpe. "Ants work harder during consensus decision-making in small groups." Journal of The Royal Society Interface 11, no. 98 (2014): 20140641. http://dx.doi.org/10.1098/rsif.2014.0641.

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Individuals derive many benefits from being social, one of which is improved accuracy of decision-making, the so-called ‘wisdom of the crowds’ effect. This advantage arises because larger groups can pool information from more individuals. At present, limited empirical data indicate that larger groups outperform smaller ones during consensus decision-making in human and non-human animals. Inaccurate decisions can lead to significant costs, and we might therefore expect individuals in small groups to employ mechanisms to compensate for the lack of numbers. Small groups may be able to maintain de

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30

Khalvati, Koosha, Seongmin A. Park, Saghar Mirbagheri, et al. "Modeling other minds: Bayesian inference explains human choices in group decision-making." Science Advances 5, no. 11 (2019): eaax8783. http://dx.doi.org/10.1126/sciadv.aax8783.

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To make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as “theory of mind.” Such a model becomes especially complex when the number of people one simultaneously interacts with is large and actions are anonymous. Here, we present results from a group decision-making task known as the volunteer’s dilemma and demonstrate that a Bayesian model based on partially observable Markov decision processes outperforms existing models in quantitatively predicting human behavior and outcomes of group int

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31

Marentakis, Charis A., Panagiotis Panagos, and Afroditi Riga. "NaviGaTor: group decision-making methodology for the design of training programs." Industrial and Commercial Training 48, no. 6 (2016): 284–93. http://dx.doi.org/10.1108/ict-01-2016-0002.

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Purpose – The purpose of this paper is to present a systematic methodology (NaviGaTor) combining a structured method for the evolutionary design of training programs with Nominal Group Technique (NGT) setting supporting the involvement of a diverse set of participants and experts. Design/methodology/approach – The paper describes the necessity and efficiency of group decision making in the design of training programs, focussing on how participants can convey experience and knowledge and how NGT can enhance idea generation and support convergence of opinions. The methodology evolved from a pape

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32

Bindler, Anna, and Randi Hjalmarsson. "Path Dependency in Jury Decision Making." Journal of the European Economic Association 17, no. 6 (2018): 1971–2017. http://dx.doi.org/10.1093/jeea/jvy046.

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Abstract A large behavioral economics literature is concerned with cognitive biases in individual and group decisions, including sequential decisions. These studies often find a negative path-dependency consistent with mechanisms such as the gambler's fallacy or contrast effects. We provide the first test for such biases in group decision making using observational data. Specifically, we study more than 27,000 verdicts adjudicated sequentially by over 900 juries for high-stake criminal cases at London's Old Bailey Criminal Court in the 18th and 19th centuries. Using jury fixed effects to accou

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33

Xu, Xuanhua, Linlin Wang, Xiaohong Chen, and Bingsheng Liu. "Large group emergency decision-making method with linguistic risk appetites based on criteria mining." Knowledge-Based Systems 182 (October 2019): 104849. http://dx.doi.org/10.1016/j.knosys.2019.07.020.

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34

XiongWei, Su Qiuyan, and Li Jinlong. "The group decision-making rules based on rough sets on large scale engineering emergency." Systems Engineering Procedia 4 (2012): 331–37. http://dx.doi.org/10.1016/j.sepro.2011.11.083.

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35

Li, Yupeng, Xiaozhen Lian, Cheng Lu, and Zhaotong Wang. "A Large Group Decision Making Approach Based on TOPSIS Framework with Unknown Weights Information." MATEC Web of Conferences 100 (2017): 02013. http://dx.doi.org/10.1051/matecconf/201710002013.

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36

Palomares, Iván, Luis Martínez, and Francisco Herrera. "MENTOR: A graphical monitoring tool of preferences evolution in large-scale group decision making." Knowledge-Based Systems 58 (March 2014): 66–74. http://dx.doi.org/10.1016/j.knosys.2013.07.003.

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37

Nyerges, Timothy, and Robert W. Aguirre. "Public Participation in Analytic-Deliberative Decision Making: Evaluating a Large-Group Online Field Experiment." Annals of the Association of American Geographers 101, no. 3 (2011): 561–86. http://dx.doi.org/10.1080/00045608.2011.563669.

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38

Xu, Xuanhua, Dong Liang, Xiaohong Chen, and Yanju Zhou. "A Risk Elimination Coordination Method for Large Group Decision-Making in Natural Disaster Emergencies." Human and Ecological Risk Assessment: An International Journal 21, no. 5 (2015): 1314–25. http://dx.doi.org/10.1080/10807039.2014.955394.

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39

Song, Yongming, and Jun Hu. "Large-scale group decision making with multiple stakeholders based on probabilistic linguistic preference relation." Applied Soft Computing 80 (July 2019): 712–22. http://dx.doi.org/10.1016/j.asoc.2019.04.036.

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40

Xiao, Jing, Xiuli Wang, and Hengjie Zhang. "Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making." Information Fusion 53 (January 2020): 20–34. http://dx.doi.org/10.1016/j.inffus.2019.06.003.

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41

Zheng, Yuanhang, Zeshui Xu, Yue He, and Yuhang Tian. "A hesitant fuzzy linguistic bi-objective clustering method for large-scale group decision-making." Expert Systems with Applications 168 (April 2021): 114355. http://dx.doi.org/10.1016/j.eswa.2020.114355.

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42

Wu, Zheng, and Huchang Liao. "A consensus reaching process for large‐scale group decision making with heterogeneous preference information." International Journal of Intelligent Systems 36, no. 9 (2021): 4560–91. http://dx.doi.org/10.1002/int.22469.

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43

Li, Minxuan, Jindong Qin, Tao Jiang, and Witold Pedrycz. "Dynamic Relationship Network Analysis Based on Louvain Algorithm for Large-Scale Group Decision Making." International Journal of Computational Intelligence Systems 14, no. 1 (2021): 1242. http://dx.doi.org/10.2991/ijcis.d.210329.001.

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44

Liu, Yang, Zhi-Ping Fan, and Xiao Zhang. "A method for large group decision-making based on evaluation information provided by participators from multiple groups." Information Fusion 29 (May 2016): 132–41. http://dx.doi.org/10.1016/j.inffus.2015.08.002.

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45

Yao, Shuang, Donghua Yu, Yan Song, Hao Yao, Yuzhen Hu, and Benhai Guo. "Dry Bulk Carrier Investment Selection through a Dual Group Decision Fusing Mechanism in the Green Supply Chain." Sustainability 10, no. 12 (2018): 4528. http://dx.doi.org/10.3390/su10124528.

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Uncertain linguistic variables and scoring evaluations are two important evaluation mechanisms in the decision making field. Sustainability requirements for ship investment lead to the complexity of influence factors and the decision making process. The uncertain linguistic assessment features a large amount of ambiguity and subjectivity, while the scoring evaluation features high precision and distinct gradations. This paper constructs a criteria system in the green supply chain and proposes a dual group decision fusing mechanism for integrating the linguistic variable and scoring evaluation

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46

Liu, Bingsheng, Lishuang Yu, Ru-Xi Ding, Baochen Yang, and Zhi Li. "A decision-making method based on a two-stage regularized generalized canonical correlation analysis for complex multi-attribute large-group decision making problems." Journal of Intelligent & Fuzzy Systems 34, no. 6 (2018): 3941–53. http://dx.doi.org/10.3233/jifs-161845.

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47

Wang, Guan, Lingjiu Wu, Yusheng Liu, and Xiaoping Ye. "A review on fuzzy preference modeling methods for group decision-making." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 10645–60. http://dx.doi.org/10.3233/jifs-201529.

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With the rise of group decision-making and the increasingly complex decision-making environment, preference modeling for decision makers has become more and more important, and many preference modeling methods have emerged. Based on the fuzzy theory, researchers have proposed a large number of preference models to express the subjective uncertainty of decision makers. These methods based on fuzzy theory are collectively referred to as fuzzy preference modeling methods. The fuzzy sets preference model is the first practice of fuzzy theory used in the field of preference modeling, and it is stil

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48

Qingguo, Liu, Liu Xinxue, Wu Jian, and Li Yaxiong. "Multiattribute Group Decision-Making Method Using a Genetic K-Means Clustering Algorithm." Mathematical Problems in Engineering 2020 (May 6, 2020): 1–11. http://dx.doi.org/10.1155/2020/8313892.

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Multiattribute group decision-making (MAGDM) problems are characterized by the large number, uneven levels, and bounded rationality of decision-makers; multiple attributes and fuzziness of decision problems; and complex group behaviours. Considering these characteristics, we propose a MAGDM method using a genetic K-means clustering algorithm. First, we briefly review the traditional multiattribute decision-making method based on prospect theory (PT) and trapezoidal intuitionistic fuzzy numbers (TrIFNs) under the premise of human bounded rationality and uncertain decision environment. Then, the

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49

Haseli, Gholamreza, Reza Sheikh, Jianqiang Wang, Hana Tomaskova, and Erfan Babaee Tirkolaee. "A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM): Application to Supply Chain Management." Mathematics 9, no. 16 (2021): 1881. http://dx.doi.org/10.3390/math9161881.

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Due to the complexity of real-world multi-criteria decision-making (MCDM) issues, analyzing different opinions from a group of decision makers needs to ensure appropriate decision making. The group decision-making methods collect preferences of the decision makers and present the best preferences using mathematical equations. The best–worst method (BWM) is one of the recently introduced MCDM methods that requires fewer pairwise comparisons to obtain the criteria weights than the other MCDM methods. In this research, we develop a novel approach to group decision-making problems based on the BWM

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50

Xu, Xuanhua, Bing Wang, and Yanju Zhou. "A method based on trust model for large group decision-making with incomplete preference information." Journal of Intelligent & Fuzzy Systems 30, no. 6 (2016): 3551–65. http://dx.doi.org/10.3233/ifs-162100.

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