Group Multirole Assignment With General Conflict
Bibliographic record
Abstract
Role-based collaboration (RBC) is a novel problem-solving paradigm to facilitate collaboration. Group multirole assignment (GMRA), an extension of group role assignment (GRA), is a critical step in the RBC process, enabling the formation of efficient collaborative teams by reasonably assigning roles to agents. Recognized as a significant determinant impacting assignment and collaboration, conflict has been delineated and incorporated into GMRA. Nevertheless, the specified conflict is characterized as an oppositional conflict (OC), signifying that conflicting agents are engaged in conflict across the entirety of the role set. Rather than completely OC, which is highly specific, a more prevalent relationship involves conflict in certain aspects while remaining conflict-free in others. Therefore, we propose the concept of general conflict (GC) to describe the more common and realistic conflict relationship, offering a broader and novel perspective to depict conflict relationships. Then, we formalize these two problems by considering GC avoidance in GRA and GMRA, called GRA with GC (GRAGC) and GMRA with GC (GMRAGC), respectively. Furthermore, we establish the necessary conditions for the GRAGC and GMRAGC problems through a graph-theoretical lens, accompanied by a thorough analysis of their mathematical nature. Additionally, we propose practical solutions and refine methodologies to address both problems. The effectiveness of the improved algorithms utilizing necessary conditions is verified by simulations, which also provides evidence supporting the advantages of conflict avoidance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".