Trust Information Fusion and Expert Opinion for Large Group Emergency Decision-Making Based on Complex Network

doi:10.11925/infotech.2096-3467.2021.0941

Data Analysis and Knowledge Discovery

2022, Vol. 6

Issue (2/3): 348-363 DOI: 10.11925/infotech.2096-3467.2021.0941

Current Issue | Archive | Adv Search

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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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 words： Large Group Emergency Decision-Making Complex Network Trust Relationship Information Fusion

Received: 31 August 2021 Published: 14 April 2022

ZTFLH:

TP273

Fund:National Natural Science Foundation of China(71971217);National Natural Science Foundation of China(72091515);National Natural Science Foundation of China(71790615)

Corresponding Authors: Huang Li,ORCID：0000-0001-9966-3725 E-mail: yb961224@csu.edu.cn

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Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0941 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I2/3/348

Overall Framework of the Proposed Method

Association Network Representation of the Large Group

Trust Relationship Network of the Group

		$C 1$	$C 2$	$C 3$	$C 4$			$C 1$	$C 2$	$C 3$	$C 4$
$e 1$	$X 1$	$[S 0, S 4]$	$[S 1, S 1]$	$[S 2, S 2]$	$[S 3, S 4]$	$e 2$	$X 1$	$[S - 4, S 3]$	$[S - 1, S 1]$	$[S 3, S 4]$	$[S 1, S 3]$
	$X 2$	$[S - 1, S 0]$	$[S 0, S 1]$	$[S 2, S 2]$	$[S 3, S 4]$		$X 2$	$[S - 1, S 0]$	$[S 3, S 3]$	$[S 0, S 2]$	$[S 3, S 4]$
	$X 3$	$[S 0, S 0]$	$[S - 4, S - 3]$	$[S - 1, S - 1]$	$[S - 1, S 1]$		$X 3$	$[S 2, S 2]$	$[S - 4, S - 2]$	$[S 3, S 3]$	$[S - 1, S 0]$
	$X 4$	$[S 2, S 3]$	$[S - 3, S - 2]$	$[S - 2, S - 2]$	$[S 2, S 2]$		$X 4$	$[S 0, S 0]$	$[S 3, S 4]$	$[S 0, S 2]$	$[S 1, S 1]$
	$X 5$	$[S - 4, S - 3]$	$[S 1, S 1]$	$[S 0, S 0]$	$[S 3, S 3]$		$X 5$	$[S 2, S 3]$	$[S 1, S 1]$	$[S 4, S 4]$	$[S 0, S 1]$
…
$e 19$	$X 1$	$[S 0, S 4]$	$[S 1, S 1]$	$[S 2, S 2]$	$[S 3, S 4]$	$e 20$	$X 1$	$[S 0, S 3]$	$[S - 4, S - 2]$	$[S 0, S 1]$	$[S 0, S 3]$
	$X 2$	$[S 1, S 1]$	$[S 0, S 2]$	$[S 2, S 2]$	$[S 2, S 3]$		$X 2$	$[S 0, S 1]$	$[S 0, S 1]$	$[S 0, S 1]$	$[S 0, S 1]$
	$X 3$	$[S 2, S 2]$	$[S - 4, S - 2]$	$[S 0, S 0]$	$[S 4, S 4]$		$X 3$	$[S 0, S 1]$	$[S - 2, S 0]$	$[S 4, S 4]$	$[S - 3, S 2]$
	$X 4$	$[S 1, S 1]$	$[S 2, S 4]$	$[S 2, S 2]$	$[S 1, S 1]$		$X 4$	$[S - 4, S - 2]$	$[S - 2, S 0]$	$[S 4, S 4]$	$[S 0, S 0]$
	$X 5$	$[S 4, S 4]$	$[S 4, S 4]$	$[S 3, S 3]$	$[S 3, S 4]$		$X 5$	$[S - 2, S - 1]$	$[S 3, S 3]$	$[S - 4, S 1]$	$[S 3, S 4]$

Hesitant Fuzzy Language Decision Matrix of Experts （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	—

CIM of the Expert Group （T=0）

Association Network of the Group

Clustering Simulation： Modularity

Clustering Simulation： Number of Clusters

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

Results of Group Member Clustering

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	$[0.4302,0.5062,0.5527,0.5198,0.6997 Τ; 0.4927,0.5014,0.4 761,0.4315,0.5101 Τ;$ $0.5187,0.5075,0.6524,0.4464,0.5623 Τ; 0.6266,0.5531,0.5776,0.5803,0.6831 Τ]$

Original Information Aggregation Results

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	—

Consensus Measurement Results （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 $r lj i$
$C 4$	$e 6$	$e 6, X 2, C 1$	$e 12$	$d 2112$	$C 5$	$e 9$	$e 9, X 2, C 2$	$e 5$	$d 225$
		$e 6, X 2, C 3$	$e 12$	$d 2312$			$e 9, X 2, C 4$	$e 5$	$d 245$
		$e 6, X 2, C 4$	$e 16$	$d 2416$			$e 9, X 3, C 1$	$e 5$	$d 315$
		$e 6, X 3, C 1$	$e 5$	$d 315$		$e 11$	$e 11, X 1, C 1$	$e 9$	$d 119$
		$e 6, X 3, C 2$	$e 8$	$d 328$			$e 11, X 1, C 2$	$e 9$	$d 129$
	$e 8$	$e 8, X 3, C 1$	$e 3$	$d 313$			$e 11, X 5, C 1$	$e 13$	$d 5113$
		$e 8, X 4, C 1$	$e 4$	$d 414$		$e 14$	$e 14, X 2, C 4$	$e 4$	$d 244$
		$e 8, X 4, C 2$	$e 17$	$d 4217$			$e 14, X 3, C 1$	$e 4$	$d 314$
	$e 10$	$e 10, X 2, C 1$	$e 5$	$d 215$			$e 14, X 3, C 2$	$e 18$	$d 3218$
		$e 10, X 2, C 2$	$e 8$	$d 228$			$e 14, X 3, C 3$	$e 18$	$d 3318$
		$e 10, X 2, C 3$	$e 8$	$d 238$		$e 18$	$e 18, X 1, C 2$	$e 9$	$d 129$
		$e 10, X 3, C 2$	$e 8$	$d 328$			$e 18, X 1, C 3$	$e 4$	$d 134$
		$e 10, X 4, C 1$	$e 2$	$d 412$			$e 18, X 2, C 4$	$e 4$	$d 244$
		$e 10, X 4, C 4$	$e 2$	$d 442$

Personalized Recommendations （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

Final Group Decision Information （T= 6）

Information on the Comparative Models

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）	$X 5 ? X 3 ? X 2 ? X 4 ? X 1$

Decision Results by Each Model

Consensus Reaching Process of Each Model

Line Chart of Adjustment Costs（Model 0 and Model 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
调整成本	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
平均调整成本	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
调整成本	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
平均调整成本	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

Details of Adjustment Costs （Model 0 and Model 1）

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