  Well-executed emergency evacuations can save lives and reduce suffering.
However, decision makers struggle to determine optimal evacuation policies
given the chaos, uncertainty, and value judgments inherent in emergency
evacuations. We propose and analyze a decision support tool for pre-crisis
training exercises for teams preparing for civilian evacuations and explore the
tool in the case of the 2021 U.S.-led evacuation from Afghanistan. We use
different classes of Markov decision processes (MDPs) to capture compounding
levels of uncertainty in (1) the priority category of who appears next at the
gate for evacuation, (2) the distribution of priority categories at the
population level, and (3) individuals' claimed priority category. We compare
the number of people evacuated by priority status under eight heuristic
policies. The optimized MDP policy achieves the best performance compared to
all heuristic baselines. We also show that accounting for the compounding
levels of model uncertainty incurs added complexity without improvement in
policy performance. Useful heuristics can be extracted from the optimized
policies to inform human decision makers. We open-source all tools to encourage
robust dialogue about the trade-offs, limitations, and potential of integrating
algorithms into high-stakes humanitarian decision-making.
