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.

该论文提出了一种被忽视的应对不确定性的策略类别 ——RDOT（风险降低设计和运营工具包），该工具包可以应用于受到高度不确定性影响的决策问题，使其变得更容易处理。论文总结了 90 多种 RDOT 策略，并提出了一个将其纳入多目标优化的决策模型的框架。