基于最大满意度的群体决策非合作行为管理反馈机制及宜人性特征检测

IF 17.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Information Fusion Pub Date : 2025-06-01 Epub Date: 2025-02-01 DOI:10.1016/j.inffus.2025.102959
Yujia Liu , Yuwei Song , Jian Wu , Changyong Liang , Francisco Javier Cabrerizo
{"title":"基于最大满意度的群体决策非合作行为管理反馈机制及宜人性特征检测","authors":"Yujia Liu ,&nbsp;Yuwei Song ,&nbsp;Jian Wu ,&nbsp;Changyong Liang ,&nbsp;Francisco Javier Cabrerizo","doi":"10.1016/j.inffus.2025.102959","DOIUrl":null,"url":null,"abstract":"<div><div>Non-cooperative behaviors will lead to consensus failure in group decision making problems. As a result, managing non-cooperative behavior is a significant challenge in group consensus reaching processes, which involves two main research questions:(1) How to define non-cooperative behavior? (2) How to design an appropriate model to manage non-cooperative behavior? Existing studies often overlook the psychological motivations behind non-cooperative behavior and achieve group consensus potentially at the expense of decision-makers’ satisfaction. To address these issues, this study proposes a novel maximum satisfaction-based feedback mechanism for managing non-cooperative behavior with personality traits prediction. To address the first research question, a novel approach for identifying non-cooperative behavior is proposed by comparing the solution of the Minimum Adjustment Consensus Model (MACM) to the maximum acceptable adjustment. The latter is defined by the decision maker’s Agreeableness trait within the Big Five personality traits framework, which is predicted by a CNN-BiLSTM model using the decision maker’s online reviews. For addressing the second research question, a novel two-phases feedback mechanism is introduced to manage non-cooperative behaviors based on the satisfaction principle in decision-making. The first phase involves implementing adjustment rule for non-cooperative decision-makers. The second phase involves applying adjustment rule for cooperative decision-makers. Finally, this study presents a case study focusing on the selection of a new energy vehicle enterprise supplier to illustrate the effectiveness of the proposed model in real-world applications. Furthermore, sensitivity analysis and comparative assessments are conducted to demonstrate advantages over traditional methods. Results indicate that the proposed method enhances both satisfaction and consensus levels compared to conventional non-cooperative consensus-reaching mechanisms.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"118 ","pages":"Article 102959"},"PeriodicalIF":17.4000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A maximum satisfaction-based feedback mechanism for non-cooperative behavior management with agreeableness personality traits detection in group decision making\",\"authors\":\"Yujia Liu ,&nbsp;Yuwei Song ,&nbsp;Jian Wu ,&nbsp;Changyong Liang ,&nbsp;Francisco Javier Cabrerizo\",\"doi\":\"10.1016/j.inffus.2025.102959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Non-cooperative behaviors will lead to consensus failure in group decision making problems. As a result, managing non-cooperative behavior is a significant challenge in group consensus reaching processes, which involves two main research questions:(1) How to define non-cooperative behavior? (2) How to design an appropriate model to manage non-cooperative behavior? Existing studies often overlook the psychological motivations behind non-cooperative behavior and achieve group consensus potentially at the expense of decision-makers’ satisfaction. To address these issues, this study proposes a novel maximum satisfaction-based feedback mechanism for managing non-cooperative behavior with personality traits prediction. To address the first research question, a novel approach for identifying non-cooperative behavior is proposed by comparing the solution of the Minimum Adjustment Consensus Model (MACM) to the maximum acceptable adjustment. The latter is defined by the decision maker’s Agreeableness trait within the Big Five personality traits framework, which is predicted by a CNN-BiLSTM model using the decision maker’s online reviews. For addressing the second research question, a novel two-phases feedback mechanism is introduced to manage non-cooperative behaviors based on the satisfaction principle in decision-making. The first phase involves implementing adjustment rule for non-cooperative decision-makers. The second phase involves applying adjustment rule for cooperative decision-makers. Finally, this study presents a case study focusing on the selection of a new energy vehicle enterprise supplier to illustrate the effectiveness of the proposed model in real-world applications. Furthermore, sensitivity analysis and comparative assessments are conducted to demonstrate advantages over traditional methods. Results indicate that the proposed method enhances both satisfaction and consensus levels compared to conventional non-cooperative consensus-reaching mechanisms.</div></div>\",\"PeriodicalId\":50367,\"journal\":{\"name\":\"Information Fusion\",\"volume\":\"118 \",\"pages\":\"Article 102959\"},\"PeriodicalIF\":17.4000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Fusion\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1566253525000326\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/1 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1566253525000326","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/1 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

在群体决策问题中,非合作行为会导致共识失效。因此,管理非合作行为是群体共识达成过程中的一个重大挑战,涉及两个主要的研究问题:(1)如何定义非合作行为?(2)如何设计一个合适的模型来管理非合作行为?现有的研究往往忽视了非合作行为背后的心理动机,并可能以牺牲决策者的满意度为代价来实现群体共识。为了解决这些问题,本研究提出了一种新的基于最大满意度的反馈机制,用于管理具有人格特质预测的非合作行为。为了解决第一个研究问题,提出了一种识别非合作行为的新方法,将最小调整共识模型(MACM)的解与最大可接受调整的解进行比较。后者由大五人格特征框架中的决策者宜人性特征定义,并通过CNN-BiLSTM模型利用决策者的在线评论进行预测。针对第二个研究问题,本文引入了一种新的基于满意度原则的两阶段反馈机制来管理决策中的非合作行为。第一阶段涉及非合作决策者调整规则的实施。第二阶段涉及对合作决策者应用调整规则。最后,本文以一家新能源汽车企业供应商的选择为例,说明了该模型在实际应用中的有效性。此外,还进行了敏感性分析和比较评估,以证明该方法优于传统方法。结果表明,与传统的非合作共识达成机制相比,该方法提高了满意度和共识水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A maximum satisfaction-based feedback mechanism for non-cooperative behavior management with agreeableness personality traits detection in group decision making
Non-cooperative behaviors will lead to consensus failure in group decision making problems. As a result, managing non-cooperative behavior is a significant challenge in group consensus reaching processes, which involves two main research questions:(1) How to define non-cooperative behavior? (2) How to design an appropriate model to manage non-cooperative behavior? Existing studies often overlook the psychological motivations behind non-cooperative behavior and achieve group consensus potentially at the expense of decision-makers’ satisfaction. To address these issues, this study proposes a novel maximum satisfaction-based feedback mechanism for managing non-cooperative behavior with personality traits prediction. To address the first research question, a novel approach for identifying non-cooperative behavior is proposed by comparing the solution of the Minimum Adjustment Consensus Model (MACM) to the maximum acceptable adjustment. The latter is defined by the decision maker’s Agreeableness trait within the Big Five personality traits framework, which is predicted by a CNN-BiLSTM model using the decision maker’s online reviews. For addressing the second research question, a novel two-phases feedback mechanism is introduced to manage non-cooperative behaviors based on the satisfaction principle in decision-making. The first phase involves implementing adjustment rule for non-cooperative decision-makers. The second phase involves applying adjustment rule for cooperative decision-makers. Finally, this study presents a case study focusing on the selection of a new energy vehicle enterprise supplier to illustrate the effectiveness of the proposed model in real-world applications. Furthermore, sensitivity analysis and comparative assessments are conducted to demonstrate advantages over traditional methods. Results indicate that the proposed method enhances both satisfaction and consensus levels compared to conventional non-cooperative consensus-reaching mechanisms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
期刊最新文献
Generalization saturation and reasoning divergence in synthetic persona construction for role-enacted language modeling FDSMM: fusion-driven signals-to-mechanisms modelling with physics-constrained information integration and entropy-based fault evolution CausalCEM: Self-improved causal counterfactual emotion modeling for multimodal physiological signals Federated learning with context-aware client collaboration: Challenges, advances, and open problems Rank-aware routing decomposition for hyperspectral and multispectral image fusion
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1