论文标题
优先考虑紧急撤离在不确定性的复合水平下
Prioritizing emergency evacuations under compounding levels of uncertainty
论文作者
论文摘要
执行良好的紧急撤离可以挽救生命并减少痛苦。但是，鉴于紧急撤离固有的混乱，不确定性和价值判断，决策者努力确定最佳撤离政策。我们建议并分析为准备平民撤离的团队进行危机前培训练习的决策支持工具，并在2021年美国从阿富汗撤离的情况下探索该工具。我们使用不同类别的马尔可夫决策过程（MDP）来捕获（1）在（1）WHO的优先级类别中的不确定性级别出现在疏散之处的下一个，（2）人口级别的优先级类别的分布，以及（3）个人的主张优先类别。我们比较在八个启发式政策下被优先状态撤离的人数。与所有启发式基线相比，优化的MDP政策取得了最佳性能。我们还表明，对模型不确定性的复合水平的核算增加了复杂性，而不会改善策略绩效。可以从优化的政策中提取有用的启发式方法，以告知人类决策者。我们开放所有工具，以鼓励就权衡算法融入高风险人道主义决策的权衡，局限性和潜力进行强有力的对话。
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.
