sensitivitätsrechnungen

Sensitivitätsrechnungen, also known as sensitivity analysis, are a crucial tool in various fields, including engineering, economics, and environmental science. They involve systematically examining how the uncertainty in the output of a model or system can be attributed to different sources of uncertainty in its inputs. Essentially, sensitivity analysis helps to understand which input parameters have the most significant impact on the model's results.

The primary goal of sensitivitätsrechnungen is to identify the most influential variables, often referred to as "drivers," and to quantify their effect. This is particularly important when dealing with complex models where numerous input parameters might be uncertain. By focusing on the most sensitive parameters, researchers and analysts can prioritize data collection efforts, refine model assumptions, or allocate resources more effectively.

There are various methods for performing sensitivitätsrechnungen. Some common approaches include one-at-a-time (OAT) analysis, where each input parameter is varied individually while keeping others constant. More sophisticated methods like variance-based approaches, such as the Sobol indices, can assess both the individual and interactive effects of input parameters on the model's output variance.

The results of sensitivitätsrechnungen can inform decision-making by highlighting the robustness of model predictions under different scenarios. If a model's output is highly sensitive to a particular input, it suggests that improving the accuracy or understanding of that input is critical. Conversely, if the output is insensitive to variations in an input, that parameter may be less of a concern.