{"title":"Consistency and Consensus of Distributed Preference Relations Based on Stochastic Optimal Allocation in GDM Problems","authors":"Xianchao Dai, Hao Li, Ligang Zhou","doi":"10.1007/s10726-023-09867-5","DOIUrl":null,"url":null,"abstract":"<p>Consistency and consensus are two key challenges under uncertain circumstances in pairwise comparison-based group decision-making (GDM), especially in a distributed preference relation (DPR) environment. In this paper, a comprehensive framework designed to tackle GDM problems evaluated by DPR is developed. First, after discussing two types of inconsistency in a complete DPR matrix: uncertainty-caused inconsistency and preference-caused inconsistency, two targeted optimization models to generate a consistent certain DPR matrix are proposed based on the definitions of stochastic additive strong/weak consistency and the similarity measure between DPRs. These models can effectively derive a DPR matrix closest to the original certain judgment by stochastic optimal allocation of uncertainties. Second, a new group consensus degree is introduced to measure the consensus in the group. Then a consensus improving model is given to reach an acceptable consensus by adjusting the DPR matrix of the decision maker (DM) with the least consensus degree. Third, the DMs’ weights are determined based on the expected consistency of the original DPR matrix by stochastic simulation instead of subjective judgment, and then the aggregated DPR matrix is obtained to derive a final solution using the weighted averaging operator. Finally, an automobile selection example is given to verify the validity and rationality of the proposed models.</p>","PeriodicalId":47553,"journal":{"name":"Group Decision and Negotiation","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Group Decision and Negotiation","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s10726-023-09867-5","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
Consistency and consensus are two key challenges under uncertain circumstances in pairwise comparison-based group decision-making (GDM), especially in a distributed preference relation (DPR) environment. In this paper, a comprehensive framework designed to tackle GDM problems evaluated by DPR is developed. First, after discussing two types of inconsistency in a complete DPR matrix: uncertainty-caused inconsistency and preference-caused inconsistency, two targeted optimization models to generate a consistent certain DPR matrix are proposed based on the definitions of stochastic additive strong/weak consistency and the similarity measure between DPRs. These models can effectively derive a DPR matrix closest to the original certain judgment by stochastic optimal allocation of uncertainties. Second, a new group consensus degree is introduced to measure the consensus in the group. Then a consensus improving model is given to reach an acceptable consensus by adjusting the DPR matrix of the decision maker (DM) with the least consensus degree. Third, the DMs’ weights are determined based on the expected consistency of the original DPR matrix by stochastic simulation instead of subjective judgment, and then the aggregated DPR matrix is obtained to derive a final solution using the weighted averaging operator. Finally, an automobile selection example is given to verify the validity and rationality of the proposed models.
期刊介绍:
The idea underlying the journal, Group Decision and Negotiation, emerges from evolving, unifying approaches to group decision and negotiation processes. These processes are complex and self-organizing involving multiplayer, multicriteria, ill-structured, evolving, dynamic problems. Approaches include (1) computer group decision and negotiation support systems (GDNSS), (2) artificial intelligence and management science, (3) applied game theory, experiment and social choice, and (4) cognitive/behavioral sciences in group decision and negotiation. A number of research studies combine two or more of these fields. The journal provides a publication vehicle for theoretical and empirical research, and real-world applications and case studies. In defining the domain of group decision and negotiation, the term `group'' is interpreted to comprise all multiplayer contexts. Thus, organizational decision support systems providing organization-wide support are included. Group decision and negotiation refers to the whole process or flow of activities relevant to group decision and negotiation, not only to the final choice itself, e.g. scanning, communication and information sharing, problem definition (representation) and evolution, alternative generation and social-emotional interaction. Descriptive, normative and design viewpoints are of interest. Thus, Group Decision and Negotiation deals broadly with relation and coordination in group processes. Areas of application include intraorganizational coordination (as in operations management and integrated design, production, finance, marketing and distribution, e.g. as in new products and global coordination), computer supported collaborative work, labor-management negotiations, interorganizational negotiations, (business, government and nonprofits -- e.g. joint ventures), international (intercultural) negotiations, environmental negotiations, etc. The journal also covers developments of software f or group decision and negotiation.