Efficient resource sharing for strategic disaster preparedness
Bibliographic record
Abstract
Disasters, such as the COVID-19 pandemic, cause critical supply shortages and require the immediate injection of additional resources. Governments often set aside capacity for use during crises or opt to rely on third parties, such as the private sector. Both options, however, may not be viable, as the former is expensive and the latter is not reliable. To overcome this dilemma, we introduce a strategic disaster preparedness framework in which the government engages private suppliers by investing in a portion of their resources, in return for access when needed. The resources are maintained and used by suppliers in their routine operations unless required for an emergency. We introduce a Stackelberg game-theoretic model that captures the interaction between the government and private suppliers under a limited budget and characterize key structural properties of optimal disaster preparedness plans. These properties lead to single-level reformulations and efficient exact and approximation algorithms under certain conditions. We test the proposed models on a case study based on major disasters affecting Canada. Findings indicate that incorporating resource sharing into emergency preparedness planning leads to more than a 26% increase in social good or more than a 13% decrease in cost compared to traditional preparedness plans. The study underscores the value of the proposed approach for strategic disaster preparedness and provides important insights into public policy.
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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 | 0.006 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".