考虑专家信任风险行为挖掘与动态管理的社会网络群体共识决策研究

doi:10.16381/j.cnki.issn1003-207x.2022.2549

Abstract

Abstract:

With the development of social network technology， trust relationships among experts bring new opportunities and challenges to the consensus research of group decision making. Due to the inherent risk of trust， experts may be blindly trusted or lack due trust in decision-making process， which will cause risk issues affecting decision and make it more difficult to reach a scientific and reliable consensus. Therefore， based on experts’ behavior mining， the trust risk in social network group decision making is linked with the scientificity and reliability of the decision result， and a consensus model is proposed for social network group decision making considering experts’ trust risk behaviors mining and dynamic management to reduce the decision risk， improve the credibility of decision result and consensus efficiency. Firstly， based on social network and experts’ credibility， the trust risk behaviors mining， measurement and dynamic management mechanism is designed. Secondly， the information aggregation driven by the comprehensive influence of experts， consensus measure and feedback adjustment mechanism are constructed. Finally， the maximum/minimum thresholds of experts’ risk index and the threshold of group risk level are obtained by Monte Carlo simulation， the rationality and validity of the proposed model are demonstrated， and it is applied to the cooperative research between automobile enterprises and laser display technology companies to assist actual decision-making and enrich the consensus research of social network group decision making.

Key words: social network analysis, group decision making, trust risk behaviors, Monte Carlo simulation

CLC Number:

C934

Mengqi Li, Gaofeng Liu, Yejun Xu, Xia Liu, Huimin Wang. Research on Consensus of Social Network Group Decision Making Considering Experts' Trust Risk Behaviors Mining and Dynamic Management[J]. Chinese Journal of Management Science, 2024, 32(11): 144-156.

Figures/Tables 10

专家	修正后的偏好信息	修正后的TD
e ₁	$0.5 0.5 0.4 0.15 0.5 0.5 0.6 0.45 0.6 0.4 0.5 0.25 0.85 0.55 0.75 0.5$	0.52
e ₂	$0.5 0.35 0.3 0.4 0.65 0.5 0.4 0.25 0.7 0.6 0.5 0.35 0.6 0.75 0.65 0.5$	/
e ₃	$0.5 0.5 0.25 0.25 0.5 0.5 0.6 0.45 0.75 0.4 0.5 0.2 0.75 0.55 0.8 0.5$	/

方法	θ_G	t	GRI	专家最终分类	QGDD值	排序结果
本文方法	0.0846	1	0.5808	第一类：e ₁，e ₂，e ₄ 第二类：e ₃，e ₅	QGDD(x ₁)=0.3228，QGDD(x ₂)=0.5252，QGDD(x ₃)=0.4323，QGDD(x ₄)=0.7378	x ₄ $≻$ x ₂ $≻$ x ₃ $≻$ x ₁
模型2	0.1117	1	0.6722	第一类：e ₂，e ₄ 第二类：e ₅ 第三类：e ₁，e ₃	QGDD(x ₁)=0.3283，QGDD(x ₂)=0.5249，QGDD(x ₃)=0.4357，QGDD(x ₄)=0.7221	x ₄ $≻$ x ₂ $≻$ x ₃ $≻$ x ₁
模型3	0.1116	3	0.6763	第一类：e ₄ 第二类：e ₂，e ₅ 第三类：e ₁，e ₃	QGDD(x ₁)=0.4214，QGDD(x ₂)=0.5340，QGDD(x ₃)=0.4565，QGDD(x ₄)=0.5816	x ₄ $≻$ x ₂ $≻$ x ₃ $≻$ x ₁
文献[12]方法	0.1095	4	0.6763	第一类：e ₄ 第二类：e ₂，e ₅ 第三类：e ₁，e ₃	QGDD(x ₁)=0.4309，QGDD(x ₂)=0.5459，QGDD(x ₃)=0.4498，QGDD(x ₄)=0.5674	x ₄ $≻$ x ₂ $≻$ x ₃ $≻$ x ₁

References 25

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m n	3	4	5	6	7	8	9
2	0.6211	0.6616	0.6977	0.7083	0.7188	0.7084	0.7095
3	0.6020	0.6606	0.6868	0.6913	0.6956	0.6982	0.6937
4	0.5840	0.6418	0.6788	0.6819	0.6867	0.6898	0.6916
5	0.5738	0.6314	0.6687	0.6779	0.6802	0.6798	0.6811
6	0.5647	0.6164	0.6590	0.6717	0.6774	0.6764	0.6802
7	0.5590	0.6144	0.6530	0.6678	0.6753	0.6729	0.6763
8	0.5506	0.6060	0.6450	0.6666	0.6675	0.6703	0.6711
9	0.5466	0.6075	0.6451	0.6619	0.6696	0.6660	0.6682
10	0.5371	0.5966	0.6339	0.6599	0.6622	0.6662	0.6661
11	0.5362	0.5929	0.6338	0.6545	0.6640	0.6639	0.6660
12	0.5357	0.5865	0.6282	0.6534	0.6595	0.6655	0.6619
13	0.5254	0.5841	0.6256	0.6487	0.6578	0.6620	0.6611
14	0.5287	0.5823	0.6275	0.6493	0.6553	0.6581	0.6590
15	0.5209	0.5792	0.6266	0.6476	0.6548	0.6581	0.6565
16	0.5200	0.5725	0.6250	0.6464	0.6517	0.6524	0.6568
17	0.5109	0.5730	0.6222	0.6441	0.6488	0.6539	0.6559
18	0.5097	0.5709	0.6175	0.6441	0.6489	0.6511	0.6577
19	0.5044	0.5630	0.6179	0.6397	0.6471	0.6501	0.6496

m n	3	4	5	6	7	8	9
2	0.6980	0.7282	0.7550	0.7607	0.7698	0.7582	0.7605
3	0.7158	0.7542	0.7720	0.7705	0.7738	0.7743	0.7718
4	0.7238	0.7605	0.7790	0.7780	0.7808	0.7836	0.7835
5	0.7268	0.7611	0.7823	0.7870	0.7851	0.7861	0.7852
6	0.7298	0.7593	0.7830	0.7866	0.7937	0.7891	0.7916
7	0.7330	0.7625	0.7833	0.7918	0.7952	0.7913	0.7939
8	0.7332	0.7635	0.7824	0.7933	0.7916	0.7940	0.7939
9	0.7339	0.7676	0.7854	0.7930	0.7982	0.7951	0.7970
10	0.7319	0.7648	0.7818	0.7955	0.7954	0.7992	0.7977
11	0.7342	0.7639	0.7847	0.7955	0.8003	0.7988	0.8011
12	0.7364	0.7631	0.7836	0.7954	0.7985	0.8023	0.7991
13	0.7331	0.7624	0.7833	0.7950	0.7998	0.8022	0.8015
14	0.7387	0.7650	0.7879	0.7977	0.7995	0.8009	0.8017
15	0.7338	0.7644	0.7875	0.7979	0.8015	0.8026	0.8023
16	0.7351	0.7604	0.7888	0.7989	0.8020	0.8007	0.8035
17	0.7320	0.7630	0.7876	0.7990	0.8011	0.8034	0.8051
18	0.7315	0.7634	0.7857	0.8004	0.8018	0.8025	0.8068
19	0.7303	0.7600	0.7877	0.7982	0.8016	0.8033	0.8033

m n	3	4	5	6	7	8	9
2	0.6595	0.6949	0.7264	0.7345	0.7443	0.7333	0.7350
3	0.6645	0.7121	0.7326	0.7346	0.7373	0.7389	0.7360
4	0.6674	0.7108	0.7365	0.7359	0.7395	0.7417	0.7400
5	0.6658	0.7128	0.7374	0.7417	0.7411	0.7393	0.7385
6	0.6675	0.7111	0.7386	0.7395	0.7435	0.7388	0.7410
7	0.6712	0.7124	0.7355	0.7439	0.7449	0.7377	0.7401
8	0.6708	0.7146	0.7348	0.7444	0.7402	0.7403	0.7375
9	0.6708	0.7168	0.7377	0.7447	0.7420	0.7383	0.7393
10	0.6676	0.7133	0.7332	0.7429	0.7428	0.7400	0.7388
11	0.6708	0.7164	0.7366	0.7419	0.7423	0.7404	0.7387
12	0.6735	0.7128	0.7373	0.7417	0.7428	0.7412	0.7395
13	0.6714	0.7124	0.7352	0.7409	0.7414	0.7429	0.7384
14	0.6747	0.7154	0.7386	0.7429	0.7418	0.7396	0.7378
15	0.6699	0.7167	0.7375	0.7424	0.7416	0.7413	0.7375
16	0.6716	0.7135	0.7397	0.7432	0.7419	0.7393	0.7384
17	0.6701	0.7160	0.7375	0.7446	0.7418	0.7399	0.7385
18	0.6707	0.7154	0.7364	0.7433	0.7411	0.7402	0.7394
19	0.6696	0.7158	0.7379	0.7433	0.7419	0.7399	0.7361

模型	n	m	θ_G 均值	t均值	cs均值	GRI均值
模型1	4	5	0.1476	0.7210	0.9830	0.6422
		11	0.1794	0.8730	0.9930	0.6568
		17	0.1889	1.5250	0.9320	0.6575
	7	5	0.1580	0.6660	0.9790	0.6864
		11	0.1892	1.2350	0.9910	0.6971
		17	0.1969	3.0670	0.6960	0.7015
模型2	4	5	0.1553	0.1450	1	0.7027
		11	0.1843	0.7720	0.9780	0.7043
		17	0.1925	1.8010	0.8520	0.7011
	7	5	0.1666	0.0410	1	0.7395
		11	0.1927	1.2830	0.9760	0.7325
		17	0.1998	3.6520	0.5150	0.7341
模型3	4	5	0.1800	0.9570	0.9980	0.6821
		11	0.1936	2.9340	0.8250	0.6882
		17	0.2009	4.1790	0.4160	0.6912
	7	5	0.1864	0.9010	1	0.7229
		11	0.1946	3.7150	0.7100	0.7347
		17	0.2053	4.8580	0.1030	0.7380
模型4	4	5	0.1898	1.8430	0.6390	0.6725
		11	0.2207	1.9520	0.1290	0.6924
		17	0.2279	2.1310	0.0340	0.6982
	7	5	0.1927	1.9650	0.6500	0.7145
		11	0.2203	2.4010	0.0650	0.7278
		17	0.2291	2.7060	0.001	0.7330

研究类型	考虑信任风险的社会网络群体共识决策研究				基于信任关系的社会网络群体共识决策研究
性能方法	本文方法	[12]	[11]	[10]	[1, 4-6, 18, 19]
信任传递与集结	✕	✕	✕	✕	􀳫
专家决策行为（非合作行为等）	􀳫	􀳫	􀳫	􀳫	􀳫
专家信任风险行为挖掘及管理	􀳫	􀳫	✕	✕	✕
促进共识达成的能力	􀳫	-	􀳫	􀳫	􀳫
可接受的决策成本	􀳫	-	􀳫	􀳫	􀳫
控制群体风险水平的能力	􀳫	-	✕	✕	✕

Research on Consensus of Social Network Group Decision Making Considering Experts' Trust Risk Behaviors Mining and Dynamic Management

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