A system dynamics view of stress: Towards human-factor modeling with computer agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it