A leader-followers game of emergency preparedness for adverse events
Bibliographic record
Abstract
Natural disasters occur across the globe, resulting in billions of dollars of damage each year. Effective preparation before a disaster can help to minimize damages, economic impact, and loss of human life. This paper uses a game theory framework to set up a leader-followers model for resource distribution to several geographic zones before an adverse event. The researchers model population members who may choose to prepare in advance of an event by acquiring supplies, whereas others may wait until the last minute. Failure to prepare in advance could result in a significant loss due to the chance that supplies may no longer be available. Numerical simulations are run to determine how the leader should distribute supplies to maximize the preparedness of the overall population. It was found that population size is a significant factor for supply distribution, but the behaviour of individuals within a zone is also important. Much of the current resource allocation research focuses on the logistics and economics of supply distribution, but this paper demonstrates that social aspects should also be considered.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".