Planning for Emergencies Without Good Historical Data: A Human-in-the-Loop LLM Approach
A human-in-the-loop LLM method helps planners build scenario portfolios for emergency stockpiles when historical data is weak and expert time is scarce.
Preparedness planning has a strange problem at its core: the emergencies you most need to plan for are often the ones you know the least about.
We developed and tested a human-in-the-loop planning method that uses a large language model to generate a structured first-pass portfolio of incident scenarios for a specific stockpiled item. Experts then review, challenge, and refine that draft before it enters the planning model. The result is a faster, more systematic way to build planning inputs when historical data is weak, expert time is scarce, and the next disruption may not look like the last one.