A Multiagent Approach to Ambulance Allocation Based on Social Welfare and Local Search
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
During a mass casualty incident, there will be many victims who need to be driven in an ambulance to a hospital. Reasoning about which patients to assign to which hospitals can be viewed as a multiagent resource allocation issue. The approach taken in this paper is to view this as a constraint satisfaction problem that should also be sensitive to a chosen social welfare metric. Our proposed algorithm employing local search is presented and then implemented in a series of simulations which experiment with different social welfare functions. The initial state used in the search is identified as a factor in the results. Moreover, a global view of the scenario helps to decide the appropriate strategies. We conclude with a discussion of next steps for multiagent resource allocation problems during mass casualty incidents. In short, we offer a more reasoned approach for ambulance allocation that may provide guidance for effective healthcare delivery.
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 | 0.000 |
| 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