Functionspaces, simplicity and curve fitting

Abstract The number of adjustable parameters in a model or hypothesis is often taken as the formal expression its simplicity. I take issue with this `definition´ and argue that comparative simplicity has quasi-empirical measure, reflecting experts’ judgements who track past use model-type across domains. Since models are represented by restricted sets functions suitable space, formally speaking, general `measure simplicity´ may be defined implicitly for elements function space. This paper sketches such framework starting from intuitive constraints. It shown how feed into usual definition can recovered. A theorem H. Akaike theory model-choice recently been used to shine new light on relationship between demand empirical success, even `truth´. approach favored here permits an alternative answer based reliabilist account justification: if successful evidential inductive.