The impact of culture on crowd dynamics: an empirical approach
Bibliographic record
Abstract
In agent-based social simulation, crowd models are used to generate agent behaviors that should correspond closely to human crowds. Despite significant progress in this area, many existing crowd models do not yet account for important cultural factors in crowd behavior, and even more so, for mixed-culture crowds. Moreover, evaluation of crowd models accounting for culture is particularly difficult, e.g., as controlled experiments are more difficult to set up, due to lack of subjects from different cultures. In this paper we examine the impact of cultural differences on crowd dynamics in pedestrian and evacuation domains. We account for micro-level cultural attributes: personal spaces, speed, pedestrian avoidance side and group formations. We then quantitatively validate the macro-level predictions of an agent-based simulation utilizing these against data from web-cam movies of human pedestrian crowds recorded in five different countries: Iraq, Israel, England, Canada and France. Using the validated simulations, we investigate the impact of each micro-level attribute on the resulting macro level behavior. We also examine the impact of mixed cultures on macro-level behavior. In the evacuation domain, we use an established simulation system to investigate cultural differences reported in the literature, and additionally explore the resulting macro level behavior.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".