The Effect Of Warning Message Length on Language Model Simulated Response in Emergency Situations

The following study introduces a new agentic AI social simulation program (the Hurricane Evacuation Response Optimizer [HERO] program) designed to simulate the responses of advantaged and disadvantaged populations to hurricane relevant alerts and warnings used by the United States National Weather Service (NWS) and the Federal Emergency Management Agency (FEMA). In this project, we evaluate the responses of simulated public civilians to Flash Flood warnings and WEA alerts, assessing if there is a statistical difference in simulation response to longer and shorter emergency messages. We also assess how agent responses to LLM generated, real-world, and nonsensical alerts and warnings compare to one another through cosine similarity and summary statistics. The study concludes that our social simulation responds to prompts with varying character lengths in a statistically different manner, with higher metrics of understanding and greater variance in responses towards WEA alerts when compared to Flash Flood warnings. We also find that LLM-generated alerts were more similar through use of cosine similarity to real-world alerts than nonsensical ones.