Addressing uncertainty in models for improved decision making

In science and engineering, models are used for making predictions. These predictions are associated with uncertainties, mainly due to limitations in the models and data availability. While these uncertainties might be reduced with further analysis and data collection, that is often not an option because of constrained resources. Whenever the resulting predictions serve as a basis for decision making, it is important to appraise the uncertainty, so that decision makers can understand how much weight to give to the predictions. In addition, performing uncertainty and sensitivity analysis at intermediate stages of a study can help to better focus the model building process on those elements that contribute most to the uncertainty. Decision sensitivity metrics, which are based on the concept of value of information, enable to identify which uncertainties most affect the conclusions drawn from the model outcomes. We have found that such decision sensitivity metrics can be a powerful tool to understand and communicate an acceptable level of uncertainty associated with model predictions.

In this contribution, we will discuss the general principles of decision-oriented sensitivity measures for dealing with uncertainty and will demonstrate them on two real-life cases: (1) the use of geological models for the choice of the nuclear waste deposit site in Switzerland, and (2) the use of flood risk models for decisions on flood protection along the Danube river.