Maximizing Group Utilities While Avoiding Conflicts Through Agent Qualifications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Role-based collaboration (RBC) is a role-centered computational approach designed to solve collaboration problems. Group role assignment is an essential and extensive part of this research. Based on group multirole assignment (GMRA), this article addresses some issues in the current research. First, managers often hope to obtain the highest benefits rather than maximizing the team performance, which is emphasized in the traditional RBC research. This article introduces the use of expected utility theory to assign roles in order to maximize team effectiveness. Second, the existing studies need to provide expressions of agent and role conflicts, which have yet to be reasonably addressed. This article classifies conflicts by employing agent and role capability combined with the three-way conflict analysis theory. Based on these, this article puts forward the utility-based GMRA with conflicting agent and role problems. The validity is verified through several experiments and comparative analysis, which provides more possibilities for future research.
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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 | 0.002 | 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 | 0.002 | 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 | 0.002 |
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