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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
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A Novel Ordinal Consensus Model for Multiple Attribute Group Decision Making With Incomplete Social Network Trust
IEEE Transactions on Cybernetics
|July 24, 2025
Summary
This study introduces a new ordinal consensus framework for multiple attribute group decision making (MAGDM) using incomplete social network trust preference relations (TPRs). It enhances decision quality by improving consensus-building efficiency.
Area of Science:
- Decision Sciences
- Operations Research
- Artificial Intelligence
Background:
- Multiple Attribute Group Decision Making (MAGDM) aids groups in selecting optimal alternatives from multiple options.
- Achieving consensus among experts is crucial for improving decision quality in MAGDM.
- Existing consensus models face challenges with incomplete social network trust preference relations (TPRs).
Purpose of the Study:
- To propose a novel ordinal consensus framework for MAGDM incorporating incomplete social network TPRs.
- To enhance the consensus-building process and improve decision outcomes in MAGDM.
- To address opinion differences and achieve consensual decisions efficiently.
Main Methods:
- Utilized incomplete linguistic preference relations to represent experts' social network TPRs.
- Developed a consistency-driven two-stage optimization approach to derive individual trust levels and expert weights.
- Designed a distance-based ordinal consensus measure and an opinion dynamic-based feedback adjustment mechanism.
Main Results:
- The proposed framework effectively handles incomplete social network TPRs in MAGDM.
- The ordinal consensus process enhances the effectiveness of consensual decision outcomes.
- Simulation experiments and case studies demonstrate the framework's usability and efficiency in consensus building.
Conclusions:
- The study offers a new perspective on managing incomplete social network TPRs for MAGDM.
- The novel ordinal consensus process significantly improves consensus reaching in complex decision-making scenarios.
- The findings provide valuable insights for practical social network MAGDM problems.
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