Simulating use scenarios in hospitals using multi-agent narratives
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
Anticipating building-related complexities ensuing from occupants' behaviour is a major challenge in architectural design. Conventional building performance simulation tools model occupancy in a highly aggregated form, abstracting away the impact of dynamic spatial and social factors on occupant behaviour. To address this issue, we propose a multi-agent system that accounts for these aspects in process-driven facilities, such as hospitals. The approach involves modelling ‘narratives’, rule-based scripts that direct occupants' movement and shared activities. A scheduling mechanism employs Operations Research techniques to dynamically coordinate the narratives' execution. We demonstrate the method by simulating day-to-day operations in a typical hospital setting, involving scheduled procedures and unscheduled adaptations due to dynamic social and environmental conditions. The process involved collecting data using field observations and interviews with experts, modelling narratives, and simulating them to produce use scenarios that can be visualized and analysed by design stakeholders.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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