Testing Various Riot Control Police Formations through Agent-Based Modeling and Simulation
Bibliographic record
Abstract
A crowd may gather for various reasons such as to protest in front of government buildings or to cheer on their teams at sporting events. This crowd, influenced by a number of conditions can become aggravated and then violent, has the potential to change dynamics and create public disorder with possible outcomes of people being hurt or damage to public or private properties. Managing and controlling such a disruptive crowd is an important responsibility for police to maintain and restore the public order and safety. Effective crowd management and control strategies need to be considered and planned in advance to mitigate potential disruption and damages caused by a riotous crowd. One of the basic strategies is to utilise police tactics and to strategically deploy police teams in formations. The correct application of police formations can positively influence and be effective in dispersing an agitated crowd. This paper presents the effectiveness of various police formations in a riotous situation using agent-based modeling and simulation. The crowd management/control system that we have developed previously has the built-in crowd models and basic features to conduct simulation experiments. Using this system, the loose cordon and three-sided box formations are simulated to expulse a riotous crowd from either the four-way interaction or “T” intersection, and their results are compared and analyzed. The simulation system can be used to further research on the effect of other police formations in various situations, which potentially helps police prepare better crowd management/control strategies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".