Designing a Multi-Agent Occupant Simulation System to Support Facility Planning and Analysis for COVID-19
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
The COVID-19 pandemic changed our lives, forcing us to reconsider our built environment, architectural designs, and even behaviours. Multiple stakeholders, including designers, building facility managers, and policy makers, are making decisions to reduce SARS-CoV-2 virus transmission and make our environment safer; however, systems to effectively and interactively evaluate virus transmission in physical spaces are lacking. To help fill this gap, we propose OccSim, a system that automatically generates occupancy behaviours in a 3D model of a building and helps users analyze the potential effect of virus transmission from a large-scale and longitudinal perspective. Our participatory evaluation with four groups of stakeholders revealed that OccSim could enhance their decision making processes by identifying specific risks of virus transmission in advance, and illuminating how each risk relates to complex human-building interactions. We reflect on our design and discuss OccSim’s potential implications in the domains of ‘design evaluation,’ ‘generative design,’ and ‘digital twins.’
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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