følsomhedsundersøgelser

Følsomhedsundersøgelser, or sensitivity studies, are investigations designed to determine how the uncertainty in the output of a model or system can be attributed to different sources of uncertainty in its input. In essence, they aim to understand which input parameters have the most significant impact on the model's results. This is crucial for building robust models and making informed decisions, as it highlights areas where data collection or parameter refinement might be most beneficial.

The process typically involves systematically varying input parameters within their plausible ranges and observing the effect on the model's output. Various techniques exist for conducting sensitivity studies, ranging from simple one-at-a-time (OAT) variations to more sophisticated global sensitivity analysis methods like variance-based methods (e.g., Sobol indices) or regression-based methods. The choice of method often depends on the complexity of the model, the number of input parameters, and the desired level of detail.

The results of a følsomhedsundersøgelse can guide model development by identifying critical parameters that require more accurate estimation or further investigation. They can also help in simplifying models by suggesting which parameters have negligible influence and can potentially be fixed or removed. Furthermore, sensitivity studies are vital for risk assessment, as they reveal which input uncertainties contribute most to the overall uncertainty in the predicted outcome, thereby enabling targeted risk mitigation strategies. Applications span numerous fields, including environmental modeling, engineering design, financial forecasting, and biological systems.