A Novel Noncooperative Behavior Management Method for Multiattribute Large Group Decision-Making

Xiaoqin Dong,Bo Shao,Xianbin Sun,Ying Yang,Huihua Chen

doi:10.1155/2022/6978771

Abstract

In multiattribute large-group decision-making (MALGDM), the ideal state indicates a high degree of consensus for decision-makers. However, it is difficult to reach a consensus because the conflict between various decision attributes and decision-makers increases. To deal with the problem, a novel consensus model was developed to manage the decision-making in large groups based on noncooperative behavior. The improved clustering method was used to take account of the similarities among different decision-makers, while similar decision-makers will be grouped into the same group. Moreover, the consensus threshold was determined from an objective and subjective aspect to judge whether the consensus reaching process continues. The noncooperative behavior and adjustment amount of decision-makers' opinions were investigated based on the proposed consensus model, and an emergency decision-making problem in flood disaster is applied to manifest the feasibility and distinctive features of the proposed method. The results show the proposed novel consensus model demonstrated strong applicability and reliability to the noncooperative subgroup problem and can be explored to manage multiattribute interactions in LGDM.

Highlights

Decision-making, which aims at identifying an ideal alternative based on the information described by decisionmakers, is widely used in all aspects of modern life [1–3]
The higher degree of clustering represents that the large group has higher consensus level. us, the method used in this article is more effective than the traditional one in dealing with multiattribute large-group decision-making (MALGDM) problem
Conclusion e MALGDM problem becomes more and more significant for participants and stakeholders to make a consensus-based decision. e main contributions of the paper are as follows: (1) A novel clustering method was adopted to divide the large group into several clusters. e similarity is calculated to express the gather degree of decisionmakers, and the decision-maker can be classified by the gathered degree. e value of gathered degree decides the number of subgroups

Summary

Introduction

Decision-making, which aims at identifying an ideal alternative based on the information described by decisionmakers, is widely used in all aspects of modern life [1–3]. With the increase in the complexity of decision-making problems, many attributes relevant to decision-making problems have been explored [4]. Decision-makers need to consider all relevant aspects of the problem [5]. Decision-makers need to know about Business, Management and Accounting, Engineering, Social Sciences, and Computer Science to inform the decision-making process [7]. Group decision-making (GDM) has attracted increasing attention due to its characteristic superiority of gathering knowledge of decisionmakers from various fields [8–10]. Problems always involve many interconnected fields, and the decision-making results are related to the benefits of stakeholders. Us, it becomes uneasy for small-group decision-making to reach the demands of social development [11, 12]. As the number of people in the decision-making group increases, the problem of multiattribute large group decision-making appears [13]

Methods

Findings

Discussion

Conclusion