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Bayesian Argumentation and the Value of Logical Validity

Eva, Benjamin and Hartmann, Stephan (2018) Bayesian Argumentation and the Value of Logical Validity. [Preprint]

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Abstract

According to the Bayesian paradigm in the psychology of reasoning, the norms by which everyday human cognition is best evaluated are probabilistic rather than logical in character. Recently, the Bayesian paradigm has been applied to the domain of argumentation, where the fundamental norms are traditionally assumed to be logical. Here, we present a major generalisation of extant Bayesian approaches to argumentation that (i)utilizes a new class of Bayesian learning methods that are better suited to modelling dynamic and conditional inferences than standard Bayesian conditionalization, (ii) is able to characterise the special value of logically valid argument schemes in uncertain reasoning contexts, (iii) greatly extends the range of inferences and argumentative phenomena that can be adequately described in a Bayesian framework, and (iv) undermines some influential theoretical motivations for dual function models of human cognition. We conclude that the probabilistic norms given by the Bayesian approach to rationality are not necessarily at odds with the norms given by classical logic. Rather, the Bayesian theory of argumentation can be seen as justifying and enriching the argumentative norms of classical logic.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Eva, Benjaminbenedgareva@icloud.com
Hartmann, Stephans.hartmann@lmu.de
Keywords: Bayesian Epistemology, Psychology of Reasoning, Probability Logic, Conditionals
Subjects: Specific Sciences > Mathematics > Epistemology
Specific Sciences > Mathematics > Logic
Specific Sciences > Cognitive Science
General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Depositing User: Dr Benjamin Eva
Date Deposited: 24 Mar 2018 15:38
Last Modified: 24 Mar 2018 15:38
Item ID: 14491
Subjects: Specific Sciences > Mathematics > Epistemology
Specific Sciences > Mathematics > Logic
Specific Sciences > Cognitive Science
General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Date: 23 March 2018
URI: http://philsci-archive.pitt.edu/id/eprint/14491

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