Empirical content as a criterion for evaluating models.
Empirical content
Falsification
Model evaluation
Precision
Theory of science
Universality
Journal
Cognitive processing
ISSN: 1612-4790
Titre abrégé: Cogn Process
Pays: Germany
ID NLM: 101177984
Informations de publication
Date de publication:
05 2019
05 2019
Historique:
received:
22
02
2019
accepted:
11
03
2019
pubmed:
22
3
2019
medline:
10
7
2019
entrez:
22
3
2019
Statut:
ppublish
Résumé
Hypotheses derived from models can be tested in an empirical study: If the model reliably fails to predict behavior, it can be dismissed or modified. Models can also be evaluated before data are collected: More useful models have a high level of empirical content (Popper in Logik der Forschung, Mohr Siebeck, Tübingen, 1934), i.e., they make precise predictions (degree of precision) for many events (level of universality). I apply these criteria to reflect on some critical aspects of Kirsch's (Cognit Process, 2019. https://doi.org/10.1007/s10339-019-00904-3 ) unifying computational model of decision making.
Identifiants
pubmed: 30895421
doi: 10.1007/s10339-019-00913-2
pii: 10.1007/s10339-019-00913-2
doi:
Types de publication
Journal Article
Comment
Langues
eng
Sous-ensembles de citation
IM
Pagination
273-275Commentaires et corrections
Type : CommentOn
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