[Objective] This paper proposes an information fusion approach to describe the complex relationship among decision-making experts and improve large group emergency response. [Methods] First, we identified and constructed a network for the relationship of the expert groups with information fusion, complex network analysis, experts’ opinion and trust information. Then, we clustered the group members, calculated expert weights, and reached personalized consensus. [Results] The proposed model visualized relationship among experts, which could be used in large group emergency decision-making. Compared to the traditional approaches, the proposed method reduced the cost of consensus adjustment by about 47% and improved consensus reaching efficiency by 40% while considering experts’ willingness. [Limitations] Experts’ complex relationships can be obtained from other dimensions. Trust needs to be additionally provided by experts. [Conclusions] This study enriches the group relationship analysis and provides innovative ideas for using complex relationships to support large group decision-making in the social network environment.
徐选华, 黄丽. 基于复杂网络的大群体应急决策专家意见与信任信息融合方法及应用*[J]. 数据分析与知识发现, 2022, 6(2/3): 348-363.
Xu Xuanhua, Huang Li. Trust Information Fusion and Expert Opinion for Large Group Emergency Decision-Making Based on Complex Network. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 348-363.
Ding R X, Palomares I, Wang X Q, et al. Large-Scale Decision-Making: Characterization, Taxonomy, Challenges and Future Directions from an Artificial Intelligence and Applications Perspective[J]. Information Fusion, 2020, 59:84-102.
doi: 10.1016/j.inffus.2020.01.006
[2]
Tan X, Zhu J J, Cabrerizo F J, et al. A Cyclic Dynamic Trust-Based Consensus Model for Large-Scale Group Decision Making with Probabilistic Linguistic Information[J]. Applied Soft Computing, 2021, 100:106937.
doi: 10.1016/j.asoc.2020.106937
[3]
Aghdam N H, Ashtiani M, Azgomi M A. An Uncertainty-Aware Computational Trust Model Considering the Co-Existence of Trust and Distrust in Social Networks[J]. Information Sciences, 2020, 513:465-503.
doi: 10.1016/j.ins.2019.10.067
[4]
Yu S M, Du Z J, Wang J Q, et al. Trust and Behavior Analysis-Based Fusion Method for Heterogeneous Multiple Attribute Group Decision-Making[J]. Computers & Industrial Engineering, 2021, 152:106992.
doi: 10.1016/j.cie.2020.106992
[5]
Du Z J, Luo H Y, Lin X D, et al. A Trust-Similarity Analysis-Based Clustering Method for Large-Scale Group Decision-Making Under a Social Network[J]. Information Fusion, 2020, 63:13-29.
doi: 10.1016/j.inffus.2020.05.004
[6]
Wu T, Liu X W, Qin J D, et al. Balance Dynamic Clustering Analysis and Consensus Reaching Process with Consensus Evolution Networks in Large-Scale Group Decision Making[J]. IEEE Transactions on Fuzzy Systems, 2021, 29(2):357-371.
doi: 10.1109/TFUZZ.2019.2953602
[7]
Kamis N H, Chiclana F, Levesley J. Preference Similarity Network Structural Equivalence Clustering Based Consensus Group Decision Making Model[J]. Applied Soft Computing, 2018, 67:706-720.
doi: 10.1016/j.asoc.2017.11.022
( Xu Xuanhua, Wang Bing, Zhou Yanju. Method for Large Group Decision Making with Incomplete Decision Preference Information Based on Trust Mechanism[J]. Control and Decision, 2016, 31(4):577-585.)
[9]
Tian Z P, Nie R X, Wang J Q. Social Network Analysis-Based Consensus-Supporting Framework for Large-Scale Group Decision-Making with Incomplete Interval Type-2 Fuzzy Information[J]. Information Sciences, 2019, 502:446-471.
doi: 10.1016/j.ins.2019.06.053
( Xu Xuanhua, Zhang Qianhui. Management of Non-Cooperative Behavior in Consensus-Based Large Group Emergency Decision-Making in Social Network Environment[J]. Control and Decision, 2020, 35(10):2497-2506.)
[11]
Cheng D, Cheng F X, Zhou Z L, et al. Reaching a Minimum Adjustment Consensus in Social Network Group Decision-Making[J]. Information Fusion, 2020, 59:30-43.
doi: 10.1016/j.inffus.2020.01.004
[12]
Li S L, Wei C P. A Two-Stage Dynamic Influence Model-Achieving Decision-Making Consensus within Large Scale Groups Operating with Incomplete Information[J]. Knowledge-Based Systems, 2020, 189:105132.
doi: 10.1016/j.knosys.2019.105132
[13]
Wu T, Liu X W, Liu F. The Solution for Fuzzy Large-Scale Group Decision Making Problems Combining Internal Preference Information and External Social Network Structures[J]. Soft Computing, 2019, 23(18):9025-9043.
doi: 10.1007/s00500-018-3512-3
[14]
Wu T, Zhang K, Liu X W, et al. A Two-Stage Social Trust Network Partition Model for Large-Scale Group Decision-Making Problems[J]. Knowledge-Based Systems, 2019, 163:632-643.
doi: 10.1016/j.knosys.2018.09.024
[15]
Ureña R, Kou G, Dong Y C, et al. A Review on Trust Propagation and Opinion Dynamics in Social Networks and Group Decision Making Frameworks[J]. Information Sciences, 2019, 478:461-475.
doi: 10.1016/j.ins.2018.11.037
[16]
Kamis N H, Chiclana F, Levesley J. An Influence-Driven Feedback System for Preference Similarity Network Clustering Based Consensus Group Decision Making Model[J]. Information Fusion, 2019, 52:257-267.
doi: 10.1016/j.inffus.2019.03.004
[17]
Zhang Z X, Xu Y W, Hao W N, et al. A Signed Network Analysis-Based Consensus Reaching Process in Group Decision Making[J]. Applied Soft Computing, 2021, 100:106926.
doi: 10.1016/j.asoc.2020.106926
[18]
Wu J, Wang S, Chiclana F, et al. Two-Fold Personalized Feedback Mechanism for Social Network Consensus by Uninorm Interval Trust Propagation[J]. IEEE Transactions on Cybernetics, 2021: 1-12
[19]
Tang M, Liao H C, Mi X M, et al. Dynamic Subgroup-Quality-Based Consensus in Managing Consistency, Nearness, and Evenness Quality Indices for Large-Scale Group Decision Making Under Hesitant Environment[J]. Journal of the Operational Research Society, 2021, 72(4):865-878.
doi: 10.1080/01605682.2019.1708823
[20]
Rodriguez R M, Martinez L, Herrera F. Hesitant Fuzzy Linguistic Term Sets for Decision Making[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(1):109-119.
doi: 10.1109/TFUZZ.2011.2170076
[21]
Liao H C, Qin R, Gao C Y, et al. Score-HeDLiSF: A Score Function of Hesitant Fuzzy Linguistic Term Set Based on Hesitant Degrees and Linguistic Scale Functions: An Application to Unbalanced Hesitant Fuzzy Linguistic MULTIMOORA[J]. Information Fusion, 2019, 48:39-54.
doi: 10.1016/j.inffus.2018.08.006
[22]
Blondel V D, Guillaume J L, Lambiotte R, et al. Fast Unfolding of Communities in Large Networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
[23]
Latané B. The Psychology of Social Impact[J]. American Psychologist, 1981, 36(4):343-356.
doi: 10.1037/0003-066X.36.4.343
[24]
Crandall D, Cosley D, Huttenlocher D, et al. Feedback Effects Between Similarity and Social Influence in Online Communities[C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008: 160-168.
[25]
Wang J B, Yang N D. Dynamics of Collaboration Network Community and Exploratory Innovation: The Moderation of Knowledge Networks[J]. Scientometrics, 2019, 121(2):1067-1084.
doi: 10.1007/s11192-019-03235-4
[26]
Han J, Teng X Y, Tang X, et al. Discovering Knowledge Combinations in Multidimensional Collaboration Network: A Method Based on Trust Link Prediction and Knowledge Similarity[J]. Knowledge-Based Systems, 2020, 195:105701.
doi: 10.1016/j.knosys.2020.105701
[27]
Liao H C, Tan R Z, Tang M. An Overlap Graph Model for Large-Scale Group Decision Making with Social Trust Information Considering the Multiple Roles of Experts[J]. Expert Systems, 2021, 38(3):e12659.
[28]
Blanchard M D, Jackson S A, Kleitman S. Collective Decision Making Reduces Metacognitive Control and Increases Error Rates, Particularly for Overconfident Individuals[J]. Journal of Behavioral Decision Making, 2020, 33(3):348-375.
doi: 10.1002/bdm.2156
[29]
Zhang C X, Zhao M, Zhao L C, et al. A Consensus Model for Large-Scale Group Decision-Making Based on the Trust Relationship Considering Leadership Behaviors and Non-Cooperative Behaviors[J]. Group Decision and Negotiation, 2021, 30(3):553-586.
doi: 10.1007/s10726-021-09723-4
( Zhang Shitao, Liu Xiaodi, Zhu Jianjun, et al. Adaptive Consensus Model with Hesitant Fuzzy Linguistic Information Considering Individual Cumulative Consensus Contribution[J]. Control and Decision, 2021, 36(1):187-195.)
[31]
Wu J, Zhao Z W, Sun Q, et al. A Maximum Self-Esteem Degree Based Feedback Mechanism for Group Consensus Reaching with the Distributed Linguistic Trust Propagation in Social Network[J]. Information Fusion, 2021, 67:80-93.
doi: 10.1016/j.inffus.2020.10.010
[32]
Wu J, Chiclana F, Fujita H, et al. A Visual Interaction Consensus Model for Social Network Group Decision Making with Trust Propagation[J]. Knowledge-Based Systems, 2017, 122:39-50.
doi: 10.1016/j.knosys.2017.01.031
[33]
Su W, Wang X Q, Chen G, et al. Noise-Based Synchronization of Bounded Confidence Opinion Dynamics in Heterogeneous Time-Varying Communication Networks[J]. Information Sciences, 2020, 528:219-230.
doi: 10.1016/j.ins.2020.04.018
[34]
Li K, Liang H M, Kou G, et al. Opinion Dynamics Model Based on the Cognitive Dissonance: An Agent-Based Simulation[J]. Information Fusion, 2020, 56:1-14.
doi: 10.1016/j.inffus.2019.09.006
[35]
Tang M, Liao H C, Xu J P, et al. Adaptive Consensus Reaching Process with Hybrid Strategies for Large-Scale Group Decision Making[J]. European Journal of Operational Research, 2020, 282(3):957-971.
doi: 10.1016/j.ejor.2019.10.006
[36]
Newman M E J, Girvan M. Finding and Evaluating Community Structure in Networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2004, 69(2):026113.
doi: 10.1103/PhysRevE.69.026113
[37]
Xu X H, Yin X P, Chen X H. A Large-Group Emergency Risk Decision Method Based on Data Mining of Public Attribute Preferences[J]. Knowledge-Based Systems, 2019, 163:495-509.
doi: 10.1016/j.knosys.2018.09.010
[38]
Yuan Y, Cheng D, Zhou Z. A Minimum Adjustment Consensus Framework with Compromise Limits for Social Network Group Decision Making Under Incomplete Information[J]. Information Sciences, 2021, 549:249-268.
doi: 10.1016/j.ins.2020.11.014