Larski, Stanislav
(2012)
The problem of model selection and scientific realism.
PhD thesis, London School of Economics and Political Science.
Abstract
This thesis has two goals. Firstly, we consider the problem of model selection for the
purposes of prediction. In modern science predictive mathematical models are
ubiquitous and can be found in such diverse fields as weather forecasting,
economics, ecology, mathematical psychology, sociology, etc. It is often the case
that for a given domain of inquiry there are several plausible models, and the issue
then is how to discriminate between them – this is the problem of model selection.
We consider approaches to model selection that are used in classical [also known as
frequentist] statistics, and fashionable in recent years methods of Akaike Information
Criterion [AIC] and Bayes Information Criterion [BIC], the latter being a part of a
broader Bayesian approach. We show the connection between AIC and BIC, and
provide comparison of performance of these methods.
Secondly, we consider some philosophical arguments that arise within the setting of
the model selection approaches investigated in the first part. These arguments aim to
provide counterexamples to the epistemic thesis of scientific realism, viz., that
predictively successful scientific theories are approximately true, and to the idea that
truth and predictive accuracy go together.
We argue for the following claims: 1) that none of the criticisms brought forward in
the philosophical literature against the AIC methodology are devastating, and AIC
remains a viable method of model selection; 2) that the BIC methodology likewise
survives the numerous criticisms; 3) that the counterexamples to scientific realism
that ostensibly arise within the framework of model selection are flawed; 4) that in
general the model selection methods discussed in this thesis are neutral with regards
to the issue of scientific realism; 5) that a plurality of methodologies should be
applied to the problem of model selection with full awareness of the foundational
issues that each of these methodologies has.
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