Uncertainty is a pervasive challenge in decision analysis, and decision theory recognizes two classes of solutions: probabilistic models and cognitive heuristics. However, engineers, public planners and other decision-makers instead use a third class of strategies that could be called RDOT (Risk-reducing Design and Operations Toolkit). These include incorporating robustness into designs, contingency planning, and others that do not fall into the categories of probabilistic models or cognitive heuristics. Moreover, identical strategies appear in several domains and disciplines, pointing to an important shared toolkit. The focus of this paper is to develop a catalog of such strategies and develop a framework for them. The paper finds more than 90 examples of such strategies falling into six broad categories and argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization. Overall, RDOT represents an overlooked class of responses to uncertainty. Because RDOT strategies do not depend on accurate forecasting or estimation, they could be applied fruitfully to certain decision problems affected by high uncertainty and make them much more tractable.

翻译：不确定性是决策分析中普遍存在的挑战，决策理论承认两类解决方案：概率模型和认知启发法。然而，工程师、公共规划者及其他决策者实际采用第三类策略，可称之为"降低风险的设计与运营工具箱"（RDOT）。这类策略包括将鲁棒性融入设计、应急预案等，既不归属于概率模型范畴，也并非认知启发法。值得注意的是，相同策略跨越多领域与学科出现，揭示了一个重要的共享工具箱。本文旨在编纂此类策略目录并建立相应的理论框架。研究发现90余种此类策略，可归为六大类别，并论证其为应对高度不确定性看似棘手的决策问题提供了高效解决方案。随后，本文提出基于多目标优化将此类策略融入决策理论的框架。总体而言，RDOT代表了一种被忽视的不确定性应对方案。由于RDOT策略不依赖精准预测或估计，它们可有效应用于受高度不确定性影响的特定决策问题，并显著降低其处理难度。