modelleringafhankelijkheid

Modelleringafhankelijkheid refers to the dependence of conclusions on the modeling process. It describes how outputs such as estimates, forecasts, or decisions can change when different plausible modeling choices are made, including data selection and preprocessing, model structure and parametrization, and underlying assumptions. In many domains, multiple reasonable models exist, and their agreement or disagreement reflects this dependence rather than a single truth.

Causes include specification choices (which variables to include, functional form, and simplifications), data limitations and quality, measurement error, missing values, and the use of priors or regularization in probabilistic or machine learning models. The effect can be explicit, shown by comparing alternative models, or implicit, embedded in the modeling workflow and software.

Mitigating Modelleringafhankelijkheid involves several practices: sensitivity analysis to identify influential assumptions; uncertainty quantification and ensemble methods to capture a range of outcomes; validation against independent data; and transparent documentation of data sources, preprocessing steps, model structure, and code. In regulated or high-stakes contexts, model risk management and governance frameworks formalize these activities to support reproducibility and informed decision making.