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 , Yuwei Song , Jian Wu , Changyong Liang , 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 , Yuwei Song , Jian Wu , Changyong Liang , 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}
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.
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.