Devs Simulation of Belbin's Team Roles for Collaborative Team Dynamics
Bibliographic record
Abstract
Belbin's team role theory identifies nine behavioral roles that, when combined, support effective collaboration. Configuring teams based on these roles is often manual, costly, and inflexible. This article presents an individual-oriented simulation model using the DEVS formalism to emulate group interactions shaped by Belbin roles. Each team member is modeled as an atomic entity with behavior defined by a combination of two roles. This enables controlled experimentation with different team compositions, interaction timings, and communication sequences. Simulations were conducted using synthetic data, defined under plausible assumptions based on Belbin's framework. The model enables exploration of how different configurations affect communication flow and task distribution, supporting the identification of team structures that promote balance and efficiency. Results demonstrate the potential of integrating behavioral theories with formal modeling approaches to improve team design. This work offers a flexible and extensible simulation-based method for analyzing and optimizing team dynamics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".