A system dynamics view of stress: Towards human-factor modeling with computer agents
Bibliographic record
Abstract
Human-factor models are important for computer systems to i) make such systems more human aware (ie. better estimators of human behavior) and ii) make such systems demonstrate more realistic human behaviors (ie. display more human-like AI). These models expand the research horizons in domains such as multi-agent organizational simulation, as the impact of various human-factors can be investigated. This is particularly important in safety and security as organizational conflicts are largely impacted by human failures. Human-factor calculations, however, are difficult to quantify, validate, and encode because human behavior is both fuzzy and complex. This paper applies system dynamics, a modeling technique for understanding complex systems, to human-factors (stress in particular). It is a way that is computationally useful, and may be validated by experts in human studies. We first model stress as a causal loop diagram to discuss relationships between key components, and then produce a stocks and flows diagram to simulate behavior which, in future work, would be used for “mental models” in computer programs and agent simulations.
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".