Iterative Role Negotiation via the Bilevel GRA++ With Decision Tolerance
Why this work is in the frame
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Bibliographic record
Abstract
Role negotiation (RN) is situated at the initial stage of the role-based collaboration (RBC) methodology and is independent of the subsequent agent evaluation and role assignment (RA) processes. RN is to determine the roles and the resource requirements for each role. In existing RBC-related research, RN is assumed to be static. This means that the roles and the resource requirements for each role are predetermined by decision-makers. However, the resources allocated to each role can vary. At this time, iterative RN outcomes will have different RA results. There may not be a direct dominant relationship between different RA outcomes, especially when solving group role assignment (GRA) with multiple objectives (GRA++) problems, which makes it even more complex. To address these concerns, we introduce the original bilevel GRA++ (BGRA++) model. Specifically, at the lower level of BGRA++, a strategy is designed for quantifying iterative RNs. For the upper level, we introduce the novel GRA-NSGA-II algorithm for the RA process. Finally, we introduce the concept of decision tolerance to assist decision-makers in selecting the optimal solution from the multiple RNs. Last, simulation experiments are conducted to verify the robustness and practicability of the proposed method. Comparisons and discussions show that the proposed solution is highly competitive for solving the GRA++ problem with iterative RN.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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