考虑共识效率和误判风险的风险-效率混合共识模型

doi:10.16381/j.cnki.issn1003-207x.2021.2645

摘要/Abstract

摘要：

针对群决策问题的现有共识模型无法识别由共识中心不确定性所导致的共识效率变化和共识误判风险的问题，提出三种基于不同共识中心的共识模型。首先，从共识效率角度，定义高效共识中心并提出基于高效共识中心的最小调整共识模型；接着，考虑共识可靠性，定义可靠共识中心并提出基于可靠共识中心的最小调整共识模型；然后，综合考虑共识效率和共识误判风险，提出风险-效率混合共识模型。最后，通过碳捕捉与储存地质点评估的案例演示了所提方法的适用性，并通过敏感性分析和对比分析验证了所提出的三种共识模型的优点。

关键词: 群决策, 共识模型, 共识中心, 共识效率, 共识误判风险, 共识达成概率

Abstract:

Due to the high complexity of real-life decision-making problems， the knowledge and experience of a single expert are often not sufficient to meet decision-making needs. Therefore， a group of experts is required to participate in the decision-making process. Because of the differences in experts’ fields of expertise and knowledge， the evaluations of experts may differ or even be oppose. In this case， it is necessary to measure the group consensus degree， i.e.， the distance between the consensus center and the evaluations of experts. When the group consensus degree is less than a given consensus threshold， the minimum adjustment consensus model is often used to help decision makers guide experts to revise their opinions so that the group can reach consensus. In the current study， the consensus center used in the minimum adjustment consensus model is the mean of experts’ evaluations derived from aggregation operators. However， influenced by the distribution of information in the group， the consensus center is uncertain. How should a decision maker determine the value of the consensus center？ Is there a risk of misjudgment in using a given value of the consensus center to judge if a group reaches the consensus degree？ Little research has been conducted on these issues.To answer these questions， firstly， in terms of consensus efficiency， the highly efficient consensus center is defined and then a minimum adjustment consensus model is proposed based on the highly efficient consensus center. Next， on account of the consensus misjudgment risk， the reliable consensus center is defined and propose a minimum adjustment consensus model based on the reliable consensus center. Afterwards， a risk-efficiency mixed consensus model is further proposed， which considers both consensus efficiency and consensus misjudgment risk. A numerical example shows that the proposed models have the following advantages： 1） the minimum adjustment consensus model based on the highly efficient consensus center has less expert evaluation adjustment than the classical minimum adjustment consensus model； 2） the minimum adjustment consensus model based on the reliable consensus center has lower group consensus misjudgment risk than the classical minimum adjustment consensus model； 3） the risk-efficiency mixed consensus model can balance the consensus misjudgment risk and efficiency flexibly by a weight.The problem of consensus misjudgment risk and efficiency caused by the uncertainty of consensus center is discussed and three types of models are proposed that can analyze both consensus misjudgment risk and efficiency， which enriches group decision theory.

Key words: group decision making, consensus model, consensus center, consensus efficiency, consensus misjudgment risk, consensus reaching probability

中图分类号:

C934

姜立生,廖虎昌. 考虑共识效率和误判风险的风险-效率混合共识模型[J]. 中国管理科学, 2024, 32(2): 23-30.

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.

图/表 3

表1

专家评价值的调整量、调整方向与群体共识误判风险"

距离	专家1		专家2		专家3		专家4		均衡共识中心	专家评价值调整总量	群体共识误判风险
距离	调整量	调整方向	调整量	调整方向	调整量	调整方向	调整量	调整方向	均衡共识中心	专家评价值调整总量	群体共识误判风险
Euclidean距离	0.08	-	0.4053	+	0.2253	+	0	\	$θ ? =$ 0.57	0.7106	0.9106
Cosine距离	0	\	0.2013	+	0.0231	+	0	\	$θ ? =$ 1	0.2245	0
Dice距离	0	\	0.4314	+	0.2514	+	0	\	$θ ? =$ 0.57	0.6828	0.6405
Jaccard距离	0.2016	-	0.2739	+	0.0937	+	0.1222	-	$θ ? =$ 0.5	0.6914	0.823

表1

图1

表2

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项目	模型	基于Euclidean距离共识程度	基于Cosine距离共识程度	基于Dice距离共识程度	基于Jaccard距离共识程度
调整总量	模型1	0.6	0.2245	0.2303	0.3716
	模型2	0.6	0.2245	0.2245	0.3619
	模型3	2.24	0.2245	\	\
	模型4	0.7106	0.2245	0.6828	0.6914
共识误判风险	模型1	1	0	0.9529	0.9746
	模型2	1	0	1	1
	模型3	0.9	0	\	\
	模型4	0.9106	0	0.6405	0.823

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