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

摘要/Abstract

摘要：

为降低专家间的分歧并提升决策结果的科学性，共识达成过程已成为群体决策的关键环节。从众行为在此过程中可能对决策过程和结果产生重要影响。然而，现有研究多将其视为静态，这与复杂多变的实际情况相偏离。针对这一问题，本文提出一种基于动态从众行为的社交网络大群体决策方法。首先，采用K中心点聚类方法对大群体进行划分。其次，综合个体因素和群体因素定义动态从众程度，并据此定义动态理想调整点，实现对动态从众行为的数学建模。进一步，结合子组共识水平动态调整有限预算约束，构建基于动态从众行为的最大共识模型。算例结果表明，本文所提方法在显著降低决策成本的同时，有效提高了共识水平，展现出优异的共识效率与行为调控能力，为动态从众行为建模与实际群体决策问题提供了新的理论支撑与实践路径。

关键词: 动态从众行为, 共识达成过程, 共识模型, 社交网络大群体决策

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

中图分类号:

C934

徐远,徐海燕,徐志伟. 基于动态从众行为的社交网络大群体决策方法[J]. 中国管理科学, 2026, 34(9): 57-67.

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.

图/表 11

表1

社交网络表达形式"

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

表1

图1

表2

聚类结果"

$s g k$	$e u k$	$w u k$	$w k$
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

表2

表3

共识迭代过程"

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

表3

表4

图2

图3

图4

表5

与其他共识模型的对比"

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

表5

图5

表6

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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