A Computational Model of Hurricane Evacuation Decision

Hurricanes are devastating natural disasters. In deciding how to respond to a hurricane, in particular whether and when to evacuate, a decision-maker must weigh often highly uncertain and contradictory information about the future path and intensity of the storm. To effectively plan to help people during a hurricane, it is crucial to be able to predict and understand this evacuation decision. To this end, we propose a computational model of human sequential decision-making in response to a hurricane based on a Partial Observable Markov Decision Process (POMDP) that models concerns, uncertain beliefs about the hurricane, and future information. We evaluate the model in two ways. First, hurricane data from 2018 was used to evaluate the model's predictive ability on real data. Second, a simulation study was conducted to qualitatively evaluate the sequential aspect of the model to illustrate the role that the acquisition of future, more accurate information can play on current decision-making. The evaluation with 2018 hurricane season data shows that our proposed features are significant predictors and the model can predict the data well, within and across distinct hurricane datasets. The simulation results show that, across different setups, our model generates predictions on the sequential decisions making aspect that align with expectations qualitatively and suggests the importance of modeling information.