Understanding the Data Needs for Developing a Computational Model of Team Dynamics
Bibliographic record
Abstract
Understanding team effectiveness is crucial to improving performance in many workplace and educational contexts. Many theories have postulated a variety of factors that influence team success. However, they are limited to a descriptive framework or involve small empirical studies. In contexts involving many teams, we would ideally like to monitor ongoing team behaviors to alert problematic behaviors and reward positive actions. To accomplish this goal, we propose to develop a computational model of team concepts to facilitate the detection, prediction, and proper management of team behaviors. In this work, we synthesize the literature on team models and present six overarching team concepts. We select two specific concepts and model them as a dynamic Bayesian network (DBN). We demonstrate the utility of the DBN models in simulation and discuss the gap between the behaviors prescribed by team theories and the data needs in computational models. Lastly, we discuss possible data sources that serve as starting points for developing empirically accurate models.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".