Extending Group Role Assignment With Cooperation and Conflict Factors via KD45 Logic
Bibliographic record
Abstract
Group role assignment with cooperation and conflict factors (GRACCFs) is a creative social computing method for team establishment. It can maximize the new team’s performance through role assignment considering potential cooperation or conflict factors among agents. However, this method has two bottlenecks in practical applications. First, in the scenario of establishing a new team from several existing teams, collecting the pertinent cooperation or conflict information encounters challenges. Second, GRACCF merely takes the CCFs as a part of the objective function for team performance, but this will underestimate the CCFs’ impacts on the sustainable development of the team. This article tackles these issues by extending GRACCF from a new viewpoint. It first designs a KD45 logic algorithm based on the KD45 logic system, which can discover the implicit cognitive CCFs through logical inferences with closure calculations. Then, it proposes an original team evaluation method that can help decision-makers determine the weights of team performance and CCFs’ impacts based on their demands. Large-scale simulation experiments indicate that the proposed solution is practicable and robust. The proposed method provides a solid decision-making reference for administrators when establishing a sustainable team.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".