Personalized Feedback Constraint-Driven Minimum-Relative-Cost Consensus in Group Decision-Making
Bibliographic Data
| ID | 22107414 |
|---|---|
| Authors | Sumin Yu (0000-0001-8984-2906, Shenzhen University), Jia Xiao (0000-0001-8351-1524, Shenzhen University), Zhijiao Du (0000-0001-9309-8809, Sun Yat-sen University), Rui Liu (0000-0002-1489-731X, City University of Hong Kong), Jing Wang (0000-0002-7594-8539, Hunan Normal University) |
| Year | 2025 |
| Volume | 12 |
| Issue | 5 |
| Pages | 2411-2422 |
| Publication date | 2025-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3522955 |
| OpenAlex | W4406321403 |
| Language | EN |
| References cited | 44 |
Minimum-cost consensus (MCC) is an effective technique for reducing differences of opinion in group decision-making that is sensitive to adjustment costs. Classical MCC aims to obtain an optimal solution regarding the consensus reaching process (CRP) with the objective function of minimizing the group adjustment cost. However, the following limitations exist: 1) the absolute adjustment cost is usually employed as the sole criterion, which is susceptible to the uneven distribution of individual opinions; and 2) while some MCC models account for over-adjustment, how to prevent over-adjustment in the CRP with relative adjustment costs remains almost unexplored. To this end, this study proposes minimum-relative-cost consensus (MRCC) models based on personalized feedback constraints. First, the concept of relative adjustment cost is defined. The notion of personalized feedback constraint is introduced to take full account for individual willingness to adjust their respective opinions. According to different types of consensus constraints, three personalized feedback constraint-driven MRCC models are constructed. We then perform the cost analysis, which shows that MRCC changes the acting mechanism of classical MCC on CRP. In addition, we explore the integration of MCC and MRCC to fully utilize the advantages of both. Finally, a case study of online healthcare community knowledge services and the comparative analysis reveals the feasibility and advantages of the models
Mathematical optimization · Cognitive Science and Mapping · Computer Science · Mathematics · Psychology · Social Psychology
| Highly cited | No |
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