Instrumentalism, Parsimony, and the Akaike Framework
By Elliott Sober. Philosophia Scientiæ, 2002
129 citations recorded by the source corpus.
Subfields: Ecological Modeling, History and Philosophy of Science, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/8305f9a1c423a4144d662011d04dba315f1a7207
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecological Modeling
- Łukasz Waszak
- David Posada
- Tomasz Górecki
- W. Wołyński
- Kristen D. Ritchey
- Jeffrey R. Harring
- R. Silverman
- Miroslaw Krzysko
In History and Philosophy of Science
- Łukasz Waszak
- David Posada
- Tomasz Górecki
- W. Wołyński
- Kristen D. Ritchey
- Jeffrey R. Harring
- R. Silverman
- Miroslaw Krzysko
In Molecular Biology
- T. Buckley
- Łukasz Waszak
- David Posada
- Tomasz Górecki
- W. Wołyński
- Kristen D. Ritchey
- Jeffrey R. Harring
- R. Silverman
Citations in the corpus
- Consistent Model Selection by an Automatic "Gets" Approach (2003, background): “Nevertheless, Sober (2003) argues that there are good philosophical grounds for its adoption in an instrumentalist approach, where prediction is pre-eminent.”
- Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. (2004, methodology): “The AIC is designed to estimate the predictive accuracy of competing hypotheses (Forster, 2002; Sober, 2002b), which is the expected performance of a model when predicting new data.”
- Fluency Has a Role in the Simple View of Reading (2013, methodology): “Accordingly, because the multiplicative model fit no better than the additive model in our study and because the additive model is more parsimonious (Sober, 2000), we used the additive model to evaluate the role of fluency in SVR.”
- Selected statistical methods of data analysis for multivariate functional data (2018, methodology): “Wedecided to use such criterion becauseAkaike Information Criterion (AIC) better measures predictive accuracy while BIC better measures goodness of fit (Berk 2008; Shmueli 2010; Sober 2002).”
- Predictive Analytics in Information Systems Research (2011, unknown): “In the philosophy of science literature, there has been much debate over the difference between explaining and predicting (e.g., Dowe et al. 2007; Forster 2002; Forster and Sober 1994; Hitchcock and Sober 2004; Sober 2002).”