Weighted Group Role Assignment Based on Three-Way Conflict Analysis With Interval-Valued Intuitionistic Fuzzy Numbers
Why this work is in the frame
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Bibliographic record
Abstract
Role-based collaboration (RBC) has become a crucial computational approach for task allocation and team coordination, yet three critical research gaps remain unresolved. First, while existing methods treat role importance uniformly, real-world scenarios require differentiated prioritization of roles, which is a gap addressed through role weight vectors that dynamically adjust task significance. Second, current qualification matrices that directly specify agent capabilities lack mechanisms to handle assessment uncertainties, leading this article to propose a novel determination method using intuitionistic fuzzy numbers for robust capability modeling. Third, the absence of systematic conflict categorization frameworks motivates our three-way conflict analysis (TWCA) method that classifies conflicts through hierarchical comparisons of agent competency. Drawing from these considerations, the article presents the weighted group role assignment (GRA) with conflicting constraints problem, aiming to overcome the identified challenges through environments-classes, agents, roles, groups, and objects (E-CARGO) framework. The proposed approach is tested and validated through a series of experiments and comparative analyses to demonstrate its efficacy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it