Prioritizing emergency evacuations under compounding levels of uncertainty

TL;DR

This paper develops a decision support tool using Markov decision processes to optimize emergency evacuations under multiple layers of uncertainty, demonstrated through a case study of the 2021 Afghanistan evacuation.

Contribution

It introduces a novel MDP-based framework for modeling complex uncertainties in evacuation decisions and compares heuristic policies with an optimized policy.

Findings

Optimized MDP policy outperforms heuristic baselines.

Accounting for multiple uncertainty levels increases complexity without performance gains.

Heuristics derived from optimized policies aid human decision-making.

Abstract

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.