Sensitivitätsabschätzung

Sensitivitätsabschätzung, often translated as sensitivity analysis, is a method used to determine how the uncertainty in the output of a mathematical model or system can be attributed to different sources of uncertainty in its inputs. It involves systematically varying the input parameters of a model and observing the resulting changes in the model's output. The goal is to identify which input parameters have the most significant impact on the model's predictions.

There are various techniques for conducting a sensitivity analysis. One common approach is one-at-a-time (OAT) analysis, where each input parameter is varied individually while all other parameters are held at their base values. More sophisticated methods include global sensitivity analysis, which considers the interactions between multiple input parameters simultaneously. Examples of global methods include variance-based approaches like the Sobol indices, which decompose the total variance of the model output into contributions from individual inputs and their interactions.

The results of a sensitivity analysis are crucial for various fields, including engineering, environmental science, economics, and risk assessment. By understanding the sensitivity of a model, researchers and decision-makers can focus their efforts on obtaining more accurate data for the most influential parameters, simplifying the model by excluding less important ones, or identifying areas where further research is needed. It helps in understanding model robustness and in making informed decisions when dealing with complex systems where complete certainty is unattainable.