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Folk psychology and the interpretation of decision theory

Thoma, Johanna (2020) Folk psychology and the interpretation of decision theory. Ergo. ISSN 2330-4014 (In Press)

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Abstract

Most philosophical decision theorists and philosophers of the social sciences believe that decision theory is and should be in the business of providing folk psychological explanations of choice behaviour, and that it can only do so if we understand the preferences, utilities and probabilities that feature in decision-theoretic models as as- criptions of mental states not reducible to choice. The behavioural interpretation of preference and related concepts, still common in economics, is consequently cast as misguided. This paper argues that even those who strive to provide folk psychological explanations should side with the economists, and adopt a behavioural interpretation of the preferences featuring in decision-theoretic models. Under a mentalistic inter- pretation of preference, decision-theoretic models do not straightforwardly provide ordinary folk psychological explanations. Instead, they involve controversial enough commitments about the mental causes of choice to not only fail to adequately capture much unreflective decision-making, but also many intentional, reason-based and in- strumentally rational choices. Satisfactory folk psychological explanation in fact only comes indirectly from inferring more fundamental conative attitudes from a pattern of decision-theoretic preferences. And the behavioural interpretation does a better job at facilitating such inferences. My argument extends to the related concepts of utility and probability.

Item Type: Article
Official URL: https://www.ergophiljournal.org/
Additional Information: © 2020 The Author
Divisions: Philosophy, Logic and Scientific Method
Subjects: B Philosophy. Psychology. Religion > B Philosophy (General)
B Philosophy. Psychology. Religion > BF Psychology
Date Deposited: 29 Apr 2020 14:24
Last Modified: 09 Sep 2020 23:40
URI: http://eprints.lse.ac.uk/id/eprint/104181

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