Interventions via Social Influence for Emergent Suboptimal Restraint Use
Bibliographic record
Abstract
Although restraint use has increased primarily in developed countries, vehicle accident-related injuries and deaths continue to be a problem. Alongside lack of restraint use, studies involving suboptimal restraint use have gained recent popularity. In this study we investigate the use of social influence forinterventions to counter emerging suboptimal restraint use in groups of agents.A multi-agent simulation model is provided where dominant individuals use randomly assigned influence rates to repeatedly alter the knowledge of lessinfluential group members. Cultural influence is implemented via a cultural algorithm and used to simulate individuals affected by beliefs in the community. Objectives include investigating the emergence of patterns of restraint selection and use as well as interventions targeted at more influential agents. Results demonstrate that prominent patterns of behaviour similar to the influentialmembers of the groups do emerge. Furthermore, interventions targeted at influential group members outperform interventions targeted at a percentage of the population at large. Interventions succeed at some level both in the presence and absence of cultural influence.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".