基于动态从众行为的社交网络大群体决策方法

doi:10.16381/j.cnki.issn1003-207x.2024.2043

Abstract

Abstract:

In order to reduce divergences among experts and enhance the scientific rigor of decision results， the consensus reaching process（CRP） has become a crucial stage in group decision-making. During the CRP， conformity behavior may exert a significant influence on both the decision process and its outcomes. However， most existing studies assume conformity behavior to be static， which deviates from the complex and ever-changing reality. To address this limitation， a social network large-scale group decision-making method is proposed that incorporates dynamic conformity behavior. Specifically， a K-medoids clustering algorithm is employed to partition the large group into several subgroups. A dynamic conformity degree is then defined by jointly considering individual and group factors， based on which a dynamic ideal adjustment point is introduced to model the dynamic conformity behavior. Furthermore， by dynamically adjusting the limited budget based on subgroup consensus levels， a maximum consensus model under dynamic conformity behavior is constructed. Numerical results demonstrate that the presented method not only substantially reduces decision-making costs but also effectively improves consensus levels. The presented method exhibits strong consensus efficiency and behavioral adaptability， thereby offering a novel theoretical perspective and a practical framework for modeling dynamic conformity behavior and supporting complex group decision-making under evolving environments.

Key words: dynamic conformity behavior, consensus reaching process, consensus model, social network large-scale group decision-making

CLC Number:

C934

Yuan Xu,Haiyan Xu,Zhiwei Xu. A Social Network Large-scale Group Decision-making Method Based on Dynamic Conformity Behavior[J]. Chinese Journal of Management Science, 2026, 34(9): 57-67.

Figures/Tables 11

网络图	代数符号	0-1矩阵
	$e 1 t e 3 e 3 t e 4 e 4 t e 5 e 5 t e 2 e 2 t e 4 e 4 t e 1 e 1 t e 5 e 5 t e 3 e 3 t e 2 e 2 t e 1$	$O = 0010110010010101000101100$

$s g k$	$e u k$	$w u k$	$w k$
sg¹	{e₁,e₃,e₄,e₈,e₁₂,e₁₄,e₁₉}	{1/7, 1/7, 1/7, 1/7, 1/7, 1/7, 1/7}	0.35
sg²	{e₂,e₅,e₆,e₉,e₁₈}	{1/5, 1/5, 1/5, 1/5, 1/5}	0.25
sg³	{e₇,e₁₁,e₁₅,e₁₇,e₂₀}	{1/5, 1/5, 1/5, 1/5, 1/5}	0.25
sg⁴	{e₁₀,e₁₃,e₁₆}	{1/3, 1/3, 1/3}	0.15

t	$B t$	$c d k, t$				$G C L t$
t	$B t$	sg¹	sg²	sg³	sg⁴	$G C L t$
t=0	—	—	—	—	—	0.7589
t=1	1	0.3631	0.3832	0.3865	0.4123	0.8936
t=2	1	0.3916	0.4178	0.4151	0.4421	0.9610
t=3	0.75	0.4072	0.4344	0.4296	0.4565	0.9885

方法	$T B$	$G C L ̑$	$C C R$
M-1	2.7500	0.9885	0.3595
M-3	35.9416	0.9700	0.0270

References 41

[1]	Wang P， Liu P D， Li Y Y， et al. Trust exploration- and leadership incubation- based opinion dynamics model for social network group decision-making： A quantum theory perspective［J］. European Journal of Operational Research， 2024， 317（1）： 156-170.
[2]	Wu T， Liu X W. Large-Scale Group Decision-Making with Uncertain and Behavioral Considerations： Methods and Applications［M］. Singapore： Springer Nature Singapore， 2023.
[3]	Shen Y F， Ma X L， Kou G， et al. Consensus methods with Nash and Kalai-Smorodinsky bargaining game for large-scale group decision-making［J］. European Journal of Operational Research， 2025， 321（3）： 865-883.
[4]	Sun X L， Zhu J J. Large-scale group classification decision making method and its application with trust-interest dual factors in social network［J］. Applied Soft Computing， 2023， 133： 109890.
[5]	Dong YC， Zha Q B， Zhang H J， et al. Consensus reaching in social network group decision making： Research paradigms and challenges［J］. Knowledge-Based Systems， 2018， 162： 3-13.
[6]	Lu Y L， Xu Y J， Huang J， et al. Social network clustering and consensus-based distrust behaviors management for large-scale group decision-making with incomplete hesitant fuzzy preference relations［J］. Applied Soft Computing， 2022， 117： 108373.
[7]	Wang X Y， Wang Y M. Minimum adjustment consensus model for multi-person multi-criteria large scale decision-making with trust consistency propagation and opinion dynamics［J］. Information Fusion， 2025， 117： 102883.
[8]	Zhan J X， Cai M J. A cost-minimized two-stage three-way dynamic consensus mechanism for social network-large scale group decision-making： Utilizing K-nearest neighbors for incomplete fuzzy preference relations［J］. Expert Systems with Applications， 2025， 263： 125705.
[9]	张发明，韩江涛，张淋茜，等. 社会信任网络下基于T2PLD算子的多属性大群体决策方法［J］. 中国管理科学， 2025， 33（9）： 177-188.
	Zhang F M， Han J T， Zhang L Q， et al. Multi-attribute large group decision making method based on T2PLD operator in social trust network［J］. Chinese Journal of Management Science，2025，33（9）： 177-188.
[10]	Ma W X， Lv J， Tian X L， et al. Large-scale consensus in incomplete social network with non-cooperative behaviors and dimension reduction［J］. Information Sciences， 2025， 690： 121563.
[11]	Meng F Y， Zhao D Y， Gong Z W， et al. Consensus adjustment for multi-attribute group decision making based on cross-allocation［J］. European Journal of Operational Research， 2024， 318（1）： 200-216.
[12]	Herrera-Viedma E， Cabrerizo F J， Kacprzyk J， et al. A review of soft consensus models in a fuzzy environment［J］. Information Fusion， 2014， 17： 4-13.
[13]	Pang K， Lu Y， Martínez L， et al. A conceptual clustering method for large-scale group decision-making with linguistic truth-valued lattice implication algebra［J］. Applied Soft Computing， 2024， 155： 111418.
[14]	Zhang G Q， Dong Y C， Xu Y F， et al. Minimum-cost consensus models under aggregation operators［J］. IEEE Transactions on Systems， Man， and Cybernetics -Part A： Systems and Humans， 2011， 41（6）： 1253-1261.
[15]	Zhang C H， Luo D D， Su W H， et al. Optimal ranking model of fuzzy preference relations with self-confidence for addressing self-confidence failure［J］. European Journal of Operational Research， 2025， 322（2）： 615-628.
[16]	Meng F Y， Li H， Li J Y. An interactive iteration consensus based social network large-scale group decision making method and its application in zero-waste city evaluation［J］.Information Fusion， 2025， 115： 102744.
[17]	Shen Y F， Ma X L， Xu Z S， et al. A minimum cost and maximum fairness-driven multi-objective optimization consensus model for large-scale group decision-making［J］.Fuzzy Sets and Systems，2025，500： 109198.
[18]	Xu Y， Liu S F， Cheng T C E， et al. Opinion convergence and management： Opinion dynamics in interactive group decision-making［J］. European Journal of Operational Research， 2025， 323（3）： 938-951.
[19]	Xiao Y B， Zhan J M， Xu Z S， et al. Multiscale group consensus with heterogeneous preference structures based on fuzzy social networks and prospect-regret theory［J］. IEEE Transactions on Fuzzy Systems， 2025， 33（3）： 1073-1087.
[20]	Tang X A， Sun M， Zhang Q， et al. Consensus reaching model with self-confidence-based dynamic weights and personalized adjustment constraints for multi-attribute group decision making［J］. Computers & Industrial Engineering， 2025， 203： 111032.
[21]	Tang M， Liao H C. Group efficiency and individual fairness tradeoff in making wise decisions［J］. Omega， 2024， 124： 103015.
[22]	Graham J R. Herding among investment newsletters： Theory and evidence［J］. The Journal of Finance， 1999， 54（1）： 237-268.
[23]	Banerjee A V. A simple model of herd behavior［J］. The Quarterly Journal of Economics， 1992， 107（3）： 797-817.
[24]	Gong Q B， Diao X D. The impacts of investor network and herd behavior on market stability： Social learning， network structure， and heterogeneity［J］. European Journal of Operational Research， 2023， 306（3）： 1388-1398.
[25]	郝晓玲，陈晓梦. 体验型产品消费行为的羊群效应及机理研究——基于电影行业消费行为的实证解释［J］. 中国管理科学， 2019， 27（11）： 176-188.
	Hao X L， Chen X M. The herding effect of consumption decision of experience products and mechanism explanation： An empirical interpretation of consumer behavior in film industry［J］. Chinese Journal of Management Science， 2019， 27（11）： 176-188.
[26]	徐浩，谭德庆，张敬钦，等. 群体性突发事件非利益相关者羊群行为的演化博弈分析［J］. 管理评论， 2019， 31（5）： 254-266.
	Xu H， Tan D Q， Zhang J Q， et al. Evolutionary game analysison herding behavior of non-direct stakeholders in mass emergencies［J］. Management Review， 2019， 31（5）： 254-266.
[27]	苏加福，王丹，张凤婷，等. 基于从众行为和移情关系的区间直觉模糊多属性群决策［J］. 计算机集成制造系统， 2024， 30（9）： 3288-3298.
	Su J F， Wang D， Zhang F T， et al. Interval intuitionistic fuzzy multi-attribute group decision-making based on herd behavior and empathy relationship［J］. Computer Integrated Manufacturing Systems， 2024， 30（9）： 3288-3298.
[28]	刘伟，王应明. 模糊偏好关系下考虑从众行为的群体共识决策方法［J］系统科学与数学， 2024， 44（4）： 981-998.
	Liu W， Wang Y M. A group consensus decision-making method considering conformity behavior in fuzzy preference relations［J］. Journal of Systems Science and Mathematical Sciences， 2024， 44（4）： 981-998.
[29]	Sun X， Zhu J， Wang J， et al. Consensus-reaching process in multi-stage large-scale group decision-making based on social network analysis： Exploring the implication of herding behavior［J］. Information Fusion， 2024， 104： 102184.
[30]	Pérez L G， Mata F， Chiclana F. Social Network Decision Making with Linguistic Trustworthiness-Based Induced owa Operators： Linguistic trustworthiness-based Iowa operators［J］. International Journal of Intelligent Systems， 2014， 29（12）： 1117-1137.
[31]	Gong Z W， Xu X X， Lu F L， et al. On consensus models with utility preferences and limited budget［J］. Applied Soft Computing， 2015， 35： 840-849.
[32]	Park H S， Jun C H. A simple and fast algorithm for K-medoids clustering［J］. Expert Systems with Applications， 2009， 36（2）： 3336-3341.
[33]	Zhang P， Zhang Z J， Gong D Q. An improved normal wiggly hesitant fuzzy FMEA model and its application to risk assessment of electric bus systems［J］. Applied Intelligence， 2024， 54（8）： 6213-6237.
[34]	Tang M， Liao H， Kou G. Type α and type γ consensus for multi-stage emergency group decision making based on mining consensus sequences［J］. Journal of the Operational Research Society，2022， 73（2）： 365-381.
[35]	Lascu D N， Zinkhan G. Consumer conformity： Review and applications for marketing theory and practice［J］. Journal of Marketing Theory and Practice， 1999， 7（3）： 1-12.
[36]	戴维·迈尔斯. 社会心理学［M］. 北京：人民邮电出版社， 2006.
	Social psychology［M］.Beijing：Posts & Telecom Press， 2006.
[37]	周志家. 环境保护、群体压力还是利益波及厦门居民PX环境运动参与行为的动机分析［J］. 社会， 2011， 31（1）： 1-34.
	Zhou Z J. Environmental protection， group pressure or interests relatedness？［J］.Society， 2011，31（1）： 1-34.
[38]	魏真瑜，赵治瀛，郑涌. 个体的人际信任水平对其从众倾向性的影响［J］. 西南大学学报（自然科学版）， 2017， 39（10）： 139-146.
	Wei Z Y， Zhao Z Y， Zheng Y. The effects of the interpersonal trust of an individual on his conformity tendency［J］. Journal of Southwest University （Natural Science Edition）， 2017， 39（10）： 139-146.
[39]	Hogg M A， Hogg M， Vaughan G M. Essentials of social psychology［M］．Pearson Education， 2009.
[40]	吴文静，罗清玉，贾洪飞. 参照群体对行人从众过街行为决策的影响［J］. 华南理工大学学报（自然科学版）， 2015， 43（4）： 14-19.
	Wu W J， Luo Q Y， Jia H F. Effect of reference group on decision of conformity crossing behaviors of pedestrians［J］. Journal of South China University of Technology （Natural Science Edition）， 2015， 43（4）： 14-19.
[41]	Yu S M， Zhang X T， Du Z J. Enhanced minimum-cost consensus： Focusing on overadjustment and flexible consensus cost［J］. Information Fusion， 2023， 89： 336-354.

参数	d₁	d₂	d₃
a₁	0.4951	0.5159	0.4539
a₂	0.4958	0.4979	0.5175
a₃	0.4662	0.4723	0.3909
a₄	0.4459	0.5277	0.4779

方法	Q1	Q2	Q3	Q4	Q5	Q6	Q7
Sun和Zhu^[4]	否	—	—	大群体	是	否	—
苏加福等^[27]	是	观点形成阶段	静态	小群体	否	是	识别调整
刘伟和王应明^[28]	是	观点形成阶段	静态	小群体	否	是	优化模型
Sun等^[29]	是	共识达成阶段	静态	大群体	是	是	优化模型
本文方法	是	共识达成阶段	动态	大群体	是	是	优化模型

A Social Network Large-scale Group Decision-making Method Based on Dynamic Conformity Behavior

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 41

Related Articles 3

Metrics

Comments

Recommended 0

[1]	Xuanhua Xu,Ting Xiao,Xiaohong Chen. A consensus Model for Large-group Emergency Decision-making Based on Group Wisdomfusion under Social Network Environment [J]. Chinese Journal of Management Science, 2024, 32(2): 285-297.
[2]	Lisheng Jiang,Huchang Liao. Risk-efficiency Mixed Consensus Model Considering Consensus Efficiency and Misjudgment Risk [J]. Chinese Journal of Management Science, 2024, 32(2): 23-30.
[3]	LIU Pei-de, TENG Fei. Multiple Attribute Group Decision-Making Method Based on Consensus Model and ORESTE Method for Extended Probabilistic Linguistic Term Set [J]. Chinese Journal of Management Science, 2021, 29(3): 199-209.