Modellosäkerheter

Modellosäkerheter refers to the uncertainties associated with mathematical or computational models. These uncertainties can arise from various sources. Firstly, there are model uncertainties stemming from the simplification and abstraction of reality that models inherently involve. No model can perfectly capture every aspect of a complex system. Secondly, parameter uncertainties exist when the values of input parameters used in the model are not known precisely and are instead estimated or based on incomplete data. This can include statistical uncertainties from data fitting or subjective choices in parameter assignment. Thirdly, data uncertainties can arise from the measurement errors, limitations, or biases present in the data used to build, calibrate, or validate the model. Finally, scenario uncertainties relate to the uncertainty in the future conditions or external factors that the model is used to predict or analyze. Understanding and quantifying modellosäkerheter is crucial for making informed decisions based on model outputs, as it provides insight into the reliability and limitations of the predictions. Techniques for addressing modellosäkerheter include sensitivity analysis, uncertainty propagation, and the use of ensemble modeling.