Crowdmags: Multi-Agent Geo-Simulation of Crowd and Control Forces Interactions
Bibliographic record
Abstract
In this chapter we proposed new agent and group models that explicitly take into account the social dimension that is used for the management of collective actions in groups of agents that we call spatial-temporal groups (STG). Our models apply to the simulation of both crowd members and control forces’ officers, as well as to their collective behaviours in groups and their interactions with groups. These new models push further currently existing approaches for crowd simulation, while explicitly introducing a social dimension in relation to the management of groups of agents. These generic models have been adjusted in the context of the CrowdMAGS Project while using the PLAMAGS platform. We used PLAMAGS as a development environment and a language to create multi-agent geo-simulations and we extended its capabilities in order to create the proposed models. Hence, we discussed in details the architecture of our CrowdMAGS system and presented details of the system’s practical use (scenario-based development, user interface, data collection and analysis). We developed an Information Collection Model which is composed of the various structures that are used to collect and organize data obtained during the simulation. This data can be used for analysis purposes. In conclusion, we must mention that this project has been fairly effective in opening new grounds for the development of crowd simulations with agent models in which the social
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.008 | 0.001 |
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".