Flermodeller

Flermodeller is a term used in Norwegian-language scientific literature to describe approaches that use multiple models to describe, predict, or analyze a system. The idea contrasts with relying on a single, potentially misspecified model. Flermodeller aim to capture different structural assumptions and sources of uncertainty by combining outputs from several models.

In practice, flermodeller are used across disciplines such as meteorology, hydrology, economics, epidemiology, and artificial intelligence. They include ensembles that mix physical and statistical models, multi-physics simulations, and data-driven predictors. Common methods include ensemble averaging, weighted ensembles, Bayesian model averaging, and stacking, as well as integrated forecasting systems that couple models with data assimilation.

The main benefits are improved predictive skill, more realistic uncertainty estimates, and greater robustness to model misspecification. By incorporating diverse perspectives, flermodeller can reveal where models agree or disagree and guide decision-making under uncertainty. Challenges include calibration of model outputs to a common scale, accounting for correlated errors among models, and high computational costs. Interpretability can also be limited, since it may be difficult to attribute outcomes to specific model components.

See also: ensemble learning, model averaging, multi-model ensemble, uncertainty quantification.