modellantaganden

Modellantaganden is a term used to describe the explicit or implicit premises on which a model is built. These assumptions concern how the system is represented, how data are interpreted, and how processes are presumed to operate. They are foundational for the construction, interpretation, and validation of a model and its outputs.

Assumptions can be structural, statistical, temporal, spatial, or theoretical. Structural assumptions specify the form of relationships (for example, which variables influence others and how they interact). Statistical assumptions concern distributions, independence, homoscedasticity, or priors. Temporal and spatial assumptions address how processes change over time or across locations, such as stationarity or homogeneous mixing. Data-related assumptions cover representativeness, measurement error, and sample size. Theoretical assumptions reflect the mechanisms considered essential for the phenomenon under study.

The choice and justification of modellantaganden affect conclusions, uncertainty, and policy implications. If key assumptions are violated, results may be biased or misleading. Transparently documenting these assumptions, along with their reasoning and potential limitations, is essential for scrutiny and reproducibility.

Practices to manage modellantaganden include conducting sensitivity or robustness analyses, presenting alternative scenarios, and testing how results change when assumptions are relaxed. Clear communication of assumptions aids peer review and helps end users understand the scope and limits of the model.

See also: model validation, uncertainty quantification, sensitivity analysis, assumptions in statistical modeling.