2D/3D MultiAgent GeoSimulation: The Case of Shopping Behavior in Square One Mall (Toronto)
Bibliographic record
Abstract
Revision with unchanged content. In this book, we propose a generic method to develop 2D and 3D multiagent geosimulation of complex behaviors (human behaviors) in geographic environments. Our work aims at solving some problems in the field of computer simulation in general and the field of multiagent simulation. These problems are are: - The absence of methods to develop 2D-3D multiagent simulation of phenomena in geographic environments. - The absence of gathering and analysis techniques that can be used to collect and analyze spatial and non-spatial data to feed the geosimulation models (input data) and to analyze data generated by geosimulations (output data). The main idea of our work is to create a generic method to develop 2D and 3D multiagent geosimulations of phenomena in geographic environments. The main contributions of this book are: - A new method to develop 2D-3D multiagent geosimulations of complex behaviors (human behaviors) in geographic environments. - An illustration of the method using the shopping behavior in a mall as a case study and the Square One mall in Toronto as a case test. - A new survey-based technique to gather spatial and non-spatial data to feed the geosimulation models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".