Decision Making Under Deep Uncertainty often uses prohibitively large scenario ensembles to calculate robustness and rank policies’ performance. This paper contributes a framework using subsampling algorithms and space-filling metrics to determine how smaller ensemble sizes impact the accuracy of robustness rankings. Subsampling methods create smaller scenario ensembles of varying sizes. We evaluate ranking sensitivity to the ensemble size and calculate accuracy relative to a baseline ranking. Then, metrics of scenario set quality predict ranking accuracy. Notably, the metrics and subsampling methods do not require additional model simulations. We demonstrate the framework with a case study of shortage policies for Lake Mead in the Colorado River Basin (CRB). Results suggest that fewer scenarios than previous studies can accurately rank Lake Mead policies, and results depend on the type of objective and robustness metric. Smaller ensembles could reduce the computational burden of robustness analyses in the ongoing CRB policy renegotiation.

深度不确定性下的决策通常使用令人望而却步的大型场景集合来计算鲁棒性并对策略的性能进行排名。本文提供了一个使用子采样算法和空间填充指标的框架，以确定较小的集合大小如何影响鲁棒性排名的准确性。子采样方法创建不同大小的较小场景集合。我们评估排名对整体规模的敏感性，并计算相对于基线排名的准确性。然后，场景集质量指标可以预测排名准确性。值得注意的是，指标和子采样方法不需要额外的模型模拟。我们通过科罗拉多河流域米德湖 (CRB) 短缺政策的案例研究来展示该框架。结果表明，与之前的研究相比，能够准确对米德湖政策进行排名的场景较少，并且结果取决于客观和稳健性指标的类型。较小的集合可以减少正在进行的 CRB 政策重新谈判中鲁棒性分析的计算负担。