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数据分析与知识发现  2022, Vol. 6 Issue (2/3): 348-363     https://doi.org/10.11925/infotech.2096-3467.2021.0941
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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*
徐选华,黄丽()
中南大学商学院 长沙 410083
Trust Information Fusion and Expert Opinion for Large Group Emergency Decision-Making Based on Complex Network
Xu Xuanhua,Huang Li()
School of Business, Central South University, Changsha 410083, China
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摘要 

【目的】 提出一种信息融合方法以更全面地描述决策专家之间的复杂关系,并将其有效应用于大群体应急决策中。【方法】 结合信息融合、复杂网络分析等方法,综合决策意见与信任信息捕获专家群体的复杂关系并构建关联网络,基于该网络实现群体聚类、专家权重求解和个性化共识达成。【结果】 实现了复杂关系的可视化及其在大群体应急决策中的融合运用。案例分析表明,本方法在考虑专家意愿的同时,使得共识调整成本较传统方法降低约47%,共识效率提升40%。【局限】 除信任及专家意见外,专家复杂关系还可从其他维度获取;信任需要专家额外提供。【结论】 本研究丰富了群体关系的内涵,为社会网络环境下利用复杂关系支撑大群体决策提供了创新思路。

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徐选华
黄丽
关键词 大群体应急决策复杂网络信任关系信息融合    
Abstract

[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.

Key wordsLarge Group Emergency Decision-Making    Complex Network    Trust Relationship    Information Fusion
收稿日期: 2021-08-31      出版日期: 2022-04-14
ZTFLH:  TP273  
基金资助:*国家自然科学基金项目(71971217);国家自然科学基金重点项目的研究成果之一(72091515);国家自然科学基金重点项目的研究成果之一(71790615)
通讯作者: 黄丽,ORCID:0000-0001-9966-3725     E-mail: yb961224@csu.edu.cn
引用本文:   
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.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.0941      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I2/3/348
Fig.1  方法整体框架
Fig.2  大群体的关联网络表示
Fig.3  专家大群体的信任关系网络
C 1 C 2 C 3 C 4 C 1 C 2 C 3 C 4
e 1 X 1 [ S 0 , S 4 ] [ S 1 , S 1 ] [ S 2 , S 2 ] [ S 3 , S 4 ] e 2 X 1 [ S - 4 , S 3 ] [ S - 1 , S 1 ] [ S 3 , S 4 ] [ S 1 , S 3 ]
X 2 [ S - 1 , S 0 ] [ S 0 , S 1 ] [ S 2 , S 2 ] [ S 3 , S 4 ] X 2 [ S - 1 , S 0 ] [ S 3 , S 3 ] [ S 0 , S 2 ] [ S 3 , S 4 ]
X 3 [ S 0 , S 0 ] [ S - 4 , S - 3 ] [ S - 1 , S - 1 ] [ S - 1 , S 1 ] X 3 [ S 2 , S 2 ] [ S - 4 , S - 2 ] [ S 3 , S 3 ] [ S - 1 , S 0 ]
X 4 [ S 2 , S 3 ] [ S - 3 , S - 2 ] [ S - 2 , S - 2 ] [ S 2 , S 2 ] X 4 [ S 0 , S 0 ] [ S 3 , S 4 ] [ S 0 , S 2 ] [ S 1 , S 1 ]
X 5 [ S - 4 , S - 3 ] [ S 1 , S 1 ] [ S 0 , S 0 ] [ S 3 , S 3 ] X 5 [ S 2 , S 3 ] [ S 1 , S 1 ] [ S 4 , S 4 ] [ S 0 , S 1 ]
e 19 X 1 [ S 0 , S 4 ] [ S 1 , S 1 ] [ S 2 , S 2 ] [ S 3 , S 4 ] e 20 X 1 [ S 0 , S 3 ] [ S - 4 , S - 2 ] [ S 0 , S 1 ] [ S 0 , S 3 ]
X 2 [ S 1 , S 1 ] [ S 0 , S 2 ] [ S 2 , S 2 ] [ S 2 , S 3 ] X 2 [ S 0 , S 1 ] [ S 0 , S 1 ] [ S 0 , S 1 ] [ S 0 , S 1 ]
X 3 [ S 2 , S 2 ] [ S - 4 , S - 2 ] [ S 0 , S 0 ] [ S 4 , S 4 ] X 3 [ S 0 , S 1 ] [ S - 2 , S 0 ] [ S 4 , S 4 ] [ S - 3 , S 2 ]
X 4 [ S 1 , S 1 ] [ S 2 , S 4 ] [ S 2 , S 2 ] [ S 1 , S 1 ] X 4 [ S - 4 , S - 2 ] [ S - 2 , S 0 ] [ S 4 , S 4 ] [ S 0 , S 0 ]
X 5 [ S 4 , S 4 ] [ S 4 , S 4 ] [ S 3 , S 3 ] [ S 3 , S 4 ] X 5 [ S - 2 , S - 1 ] [ S 3 , S 3 ] [ S - 4 , S 1 ] [ S 3 , S 4 ]
Table 1  专家的犹豫模糊决策矩阵(T=0)
e 1 e 2 e 3 e 4 e 17 e 18 e 19 e 20
e 1 0.748 1 0.320 0 0.323 2 0.189 4 0.000 0 0.275 4 0.336 0
e 2 0.642 1 0.337 2 0.260 0 0.533 5 0.000 0 0.500 0 0.732 8
e 3 0.277 7 0.330 7 0.500 0 0.129 7 0.181 8 0.685 0 0.274 1
e 4 0.280 9 0.636 5 0.445 2 0.323 8 0.284 5 0.331 7 0.302 2
e 17 0.655 0 0.549 6 0.688 2 0.406 8 0.235 1 0.420 8 0.500 0
e 18 0.090 6 0.107 4 0.234 2 0.496 1 0.157 5 0.109 6 0.250 7
e 19 0.285 3 0.485 2 0.345 1 0.692 6 0.079 4 0.115 5 0.253 4
e 20 0.239 6 0.417 0 0.228 6 0.364 4 0.362 9 0.000 0 0.182 9
Table 2  专家群体的综合影响力矩阵 CIMT=0)
Fig.4  专家大群体的关联网络
Fig.5  模拟聚类(模块度)
Fig.6  模拟聚类(聚集数量)
聚集 C k 聚集成员及权重 聚集权重 μ k
C 1 e 1(0.205 6)、 e 3(0.196 9)、 e 7(0.232 0)、 e 17(0.156 2)、 e 19(0.209 3) 0.259 9
C 2 e 2(0.255 1)、 e 5(0.243 3)、 e 16(0.282 0)、 e 20(0.219 6) 0.222 1
C 3 e 4(0.523 8)、 e 15(0.476 2) 0.102 9
C 4 e 6(0.207 5)、 e 8(0.269 6)、 e 10(0.261 5)、 e 12(0.261 4) 0.198 5
C 5 e 9(0.193 7)、 e 11(0.185 2)、 e 13(0.213 4)、 e 14(0.221 6)、 e 18(0.186 1) 0.216 6
Table 3  群体成员聚类结果
聚集 聚集(大群体)决策矩阵(T=0)
C 1 [ 0.6319,0.4939,0.4696,0.6221,0.6313 Τ ; 0.4399,0.5231,0.2256,0.4342,0.6407 Τ ;
0.5131,0.5934,0.5460,0.4884,0.7645 Τ ; 0.7748,0.6475,0.6797,0.7303,0.6681 Τ ]
C 2 [ 0.3743,0.5470,06697,0.4683,0.7514 Τ ; 0.1990,0.6664,0.5244,0.5743,0.6512 Τ ;
0.6556,0.5760,0.8661,0.4224 ; 0.4973 Τ ; 0.6570,0.6656,0.4309,0.488,0.8502 Τ ]
C 3 [ 0.3829,0.5096,0.5096,0.3009,0.8836 Τ ; 0.5065,0.5096,0.2165,0.1847,0.6944 Τ ;
0.5096,0.5097,0.2159,0.5423,0.4774 Τ ; 0.6429,0.5096,0.5913,0.8750,0.5754 Τ ]
C 4 [ 0.3302,0.8020,0.9346,0.6178,0.745 Τ ; 0.7202,0.6114,0.8379,0.3091,0.3385 Τ ;
0.7127,0.6442,0.7173,0.4802,0.5249 Τ ; 0.4294,0.7099,0.5190,0.5485,0.7355 Τ ]
C 5 [ 0.3597,0.2064,0.2026,0.4641,0.5914 Τ ; 0.6422,0.2014,0.5188,0.5114,0.2784 Τ ;
0.2014,0.2075,0.7090,0.3440,0.4611 Τ ; 0.5859,0.2014,0.6527,0.3217,0.5327 Τ ]
大群体 [ 0.4302,0.5062,0.5527,0.5198,0.6997 Τ ; 0.4927,0.5014,0.4 761,0.4315,0.5101 Τ ;
0.5187,0.5075,0.6524,0.4464,0.5623 Τ ; 0.6266,0.5531,0.5776,0.5803,0.6831 Τ ]
Table 4  初始群体信息聚合结果
聚集 聚集成员(个体共识度) 共识度 累计共识贡献度
C 1 e 1(0.765 7)、 e 3(0.824 2)、 e 7(0.838 9)、 e 17(0.692 7)、 e 19(0.774 2) 0.784 6 0.002 3
C 2 e 2(0.810 4)、 e 5(0.820 1)、 e 16(0.805 1)、 e 20(0.792 0) 0.807 2 0.008 4
C 3 e 4(0.809 3)、 e 15(0.783 4) 0.797 0 0.002 2
C 4 e 6(0.685 7)、 e 8(0.765 2)、 e 10(0.770 8)、 e 12(0.792 3) 0.757 2 -0.005 1
C 5 e 9(0.741 5)、 e 11(0.765 7)、 e 13(0.791 2)、 e 14(0.750 9)、 e 18(0.693 6) 0.749 7 -0.007 8
大群体 0.777 9
Table 5  共识测度结果(T=0)
C low EX P C k 待调整元素 POS 参考专家 e h 个性化建议 r lj i C low EX P C k 待调整元素 POS 参考专家 e h 个性化建议 r lj i
C 4 e 6 e 6 , X 2 , C 1 e 12 d 21 12 C 5 e 9 e 9 , X 2 , C 2 e 5 d 22 5
e 6 , X 2 , C 3 e 12 d 23 12 e 9 , X 2 , C 4 e 5 d 24 5
e 6 , X 2 , C 4 e 16 d 24 16 e 9 , X 3 , C 1 e 5 d 31 5
e 6 , X 3 , C 1 e 5 d 31 5 e 11 e 11 , X 1 , C 1 e 9 d 11 9
e 6 , X 3 , C 2 e 8 d 32 8 e 11 , X 1 , C 2 e 9 d 12 9
e 8 e 8 , X 3 , C 1 e 3 d 31 3 e 11 , X 5 , C 1 e 13 d 51 13
e 8 , X 4 , C 1 e 4 d 41 4 e 14 e 14 , X 2 , C 4 e 4 d 24 4
e 8 , X 4 , C 2 e 17 d 42 17 e 14 , X 3 , C 1 e 4 d 31 4
e 10 e 10 , X 2 , C 1 e 5 d 21 5 e 14 , X 3 , C 2 e 18 d 32 18
e 10 , X 2 , C 2 e 8 d 22 8 e 14 , X 3 , C 3 e 18 d 33 18
e 10 , X 2 , C 3 e 8 d 23 8 e 18 e 18 , X 1 , C 2 e 9 d 12 9
e 10 , X 3 , C 2 e 8 d 32 8 e 18 , X 1 , C 3 e 4 d 13 4
e 10 , X 4 , C 1 e 2 d 41 2 e 18 , X 2 , C 4 e 4 d 24 4
e 10 , X 4 , C 4 e 2 d 44 2
Table 6  个性化建议生成结果(T=0)
C 1 C 2 C 3 C 4 Score 方案排序
X 1 0.456 9 0.413 0 0.511 3 0.674 6 0.494 4 X 5 ? X 3 ? X 2 ? X 1 ? X 4
X 2 0.495 8 0.524 0 0.503 8 0.592 7 0.518 0
X 3 0.522 9 0.422 9 0.643 9 0.551 1 0.537 4
X 4 0.517 9 0.432 3 0.413 4 0.542 8 0.478 4
X 5 0.786 2 0.510 8 0.570 7 0.699 3 0.664 2
Table 7  最终的群体决策信息(T= 6)
模型 模型考虑因素
偏好表达 专家权重 网络划分聚类 CRP
专家的犹豫度 信任关系 意见接近度 信任关系网络 关联网络 权重调整反馈 意见调整反馈
0 HFLTS
1 HFLTS
2 HFLTS
3 乐观偏好
4 悲观偏好
Table 8  对比模型的相关信息
模型 迭代次数 群体共识度 方案得分向量 方案排序
0 6 0.850 9 (0.4944,0.5180,0.5374,0.4784,0.6642) X 5 ? X 3 ? X 2 ? X 1 ? X 4
1 11 0.854 3 (0.4891,0.5310,0.5465,0.4800,0.6445) X 5 ? X 3 ? X 2 ? X 1 ? X 4
2 20 0.854 6 (0.4943,0.5126,0.5660,0.4929,0.6250) X 5 ? X 3 ? X 2 ? X 1 ? X 4
3 6 0.861 0 (0.5277,0.4872,0.5771,0.4825,0.5991) X 5 ? X 3 ? X 1 ? X 2 ? X 4
4 8 0.861 4 (0.4856,0,5251,0.5620,0.4880,0.6105) X 5 ? X 3 ? X 2 ? X 4 ? X 1
Table 9  各模型的决策结果
Fig.7  各模型的共识达成过程
Fig.8  调整成本折线图(模型0与模型1)
成本指标 模型/专家 e 1 e 2 e 3 e 4 e 5 e 6 e 7 e 8 e 9 e 10
调整成本 模型0 2.40 1.01 0.00 0.49 0.00 7.39 0.00 0.58 5.21 2.20
模型1 4.70 1.18 0.00 1.74 0.94 15.75 0.00 1.20 8.31 3.67
平均调整成本 模型0 0.40 0.17 0.00 0.08 0.00 0.50 0.00 0.00 0.05 0.10
模型1 0.47 0.12 0.00 0.17 0.09 0.53 0.00 0.00 0.09 0.20
成本指标 模型/专家 e 11 e 12 e 13 e 14 e 15 e 16 e 17 e 18 e 19 e 20 大群体
调整成本 模型0 1.68 1.43 2.45 5.34 2.67 0.00 0.89 8.32 1.73 0.00 43.78
模型1 0.88 2.92 7.41 8.35 5.16 0.00 6.39 11.73 1.84 0.00 82.16
平均调整成本 模型0 0.28 0.24 0.37 0.44 0.44 0.00 0.00 0.66 0.00 0.00 7.30
模型1 0.09 0.29 0.60 0.34 0.52 0.00 0.20 0.64 0.00 0.00 8.22
Table 10  调整成本明细表(模型0与模型1)
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