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AIC model selection using Akaike weights

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Wagenmakers, Eric-Jan, Farrell, Simon·2004·Psychonomic Bulletin & Review

DOI 10.3758/bf03206482

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Abstract

The Akaike information criterion (AIC; Akaike, 1973) is a popular method for comparing the adequacy of multiple, possibly nonnested models. Current practice in cognitive psychology is to accept a single model on the basis of only the "raw" AIC values, making it difficult to unambiguously interpret the observed AIC differences in terms of a continuous measure such as probability. Here we demonstrate that AIC values can be easily transformed to so-called Akaike weights (e.g., Akaike, 1978, 1979; Bozdogan, 1987; Burnham & Anderson, 2002), which can be directly interpreted as conditional probabilities for each model. We show by example how these Akaike weights can greatly facilitate the interpretation of the results of AIC model comparison procedures.

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Authors: Wagenmakers, E.-J., & Farrell, S.
Affiliations: Northwestern University (US)
Corresponding author: Wagenmakers, Eric-Jan
Farrell, Simon
ORCID: Wagenmakers, Eric-Jan
Farrell, Simon
Venue: Psychonomic Bulletin & Review
Publisher: Springer Science and Business Media LLC
Published: 2004-02
Volume: 11
Issue: 1
Pages: 192-196
ISSN: 1069-9384 (ISSN-L)
1531-5320
DOI: 10.3758/bf03206482
OpenAlex: W1987464952
Cited by: 2,684
References: 54
Citations by year: 2026: 125 · 2025: 207 · 2024: 249 · 2023: 229 · 2022: 218 · 2021: 279 · 2020: 242 · 2019: 212 · 2018: 188 · 2017: 146 · 2016: 119 · 2015: 124 · 2014: 99 · 2013: 57 · 2012: 42
FWCI: 10.83
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
Locations: Psychonomic Bulletin & Review (publishedVersion)
PubMed (publishedVersion)
UvA-DARE (University of Amsterdam) (submittedVersion)
Bristol Research (University of Bristol) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: Akaike information criterion, Akaike weights, model selection, model comparison, conditional probability, cognitive psychology, model adequacy
MeSH terms: Bayes Theorem, Choice Behavior, Humans, Models, Psychological

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