Känslighetsbestämning

Känslighetsbestämning, also known as sensitivity analysis, is a technique used in various fields, including engineering, economics, and environmental science, to determine how the uncertainty in the output of a model can be attributed to different sources of uncertainty in the model input. It is a crucial tool for understanding the robustness and reliability of models, as well as for identifying the most significant factors influencing the model's predictions.

The process of sensitivity analysis typically involves varying the inputs of a model within their uncertainty ranges and observing the corresponding changes in the outputs. This can be done using different methods, such as one-at-a-time (OAT) variation, where each input is changed individually, or global sensitivity analysis methods, which consider the simultaneous variation of multiple inputs.

The results of a sensitivity analysis can be presented in various ways, including sensitivity indices, which quantify the relative importance of each input factor, and tornado diagrams, which provide a visual representation of the sensitivity of the model output to each input factor. These results can help decision-makers and stakeholders understand the key drivers of uncertainty in a model and prioritize efforts to reduce uncertainty in the most significant factors.

Sensitivity analysis is an essential tool for building confidence in models and improving their predictive power. By identifying the most influential factors and understanding the sources of uncertainty, stakeholders can make more informed decisions and better manage risks associated with model predictions.