Stress and productivity performance in the workforce modelled with binary decision automata
Bibliographic record
Abstract
This study is the third in a series developing an agent based ecological model of the workplace focused on the impact of stress. Stress and stress-related health problems are a serious matter but, prior to this series of studies, quantitative modeling of stress has been substantially neglected. This model builds on earlier work, incorporating a more realistic model of the stress relief caused by time off on weekends. The model also examines drug use as something that can be learned spontaneously or learned from a mentor rather than being present in an endemic, fixed fraction of the population, as it was in earlier studies. In this study a parameter exploration is performed on the agent representation, binary decision automata. It is found that the BDA representation is highly adaptive, responding robustly to parameter changes. Parameters investigated include number internal states in agents, accuracy of imitation of mentors, work requirements, and probabilities of learned and spontaneous drug use. Parameter values are taken beyond reasonable ranges to examine the model's failure modes. This study demonstrates that the model behaves in a reasonable fashion, determines its limits, and established a baseline for further investigation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".