Crowdmags: Multi-Agent Geo-Simulation of Crowd and Control Forces Interactions
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
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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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 it