Functionspaces, simplicity and curve fitting

Bonk, Thomas (2022): Functionspaces, simplicity and curve fitting. In: Synthese, Bd. 201, Nr. 2

Abstract

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

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Philosophie, Wissenschaftstheorie und Religionswissenschaft
• Themengebiete: 100 Philosophie und Psychologie > 100 Philosophie
• ISSN: 0039-7857
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892