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Inference to the Best Explanation in Uncertain Evidential Situations

Trpin, Borut and Pellert, Max (2017) Inference to the Best Explanation in Uncertain Evidential Situations. The British Journal for the Philosophy of Science.

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Abstract

It has recently been argued that a non-Bayesian probabilistic version of inference to the best explanation (IBE*) has a number of advantages over Bayesian conditionalization (Douven [2013]; Douven and Wenmackers [2017]). We investigate how IBE* could be generalized to uncertain evidential situations and formulate a novel updating rule IBE**. We then inspect how it performs in comparison to its Bayesian counterpart, Jeffrey conditionalization (JC), in a number of simulations where two agents, each updating by IBE** and JC, respectively, try to detect the bias of a coin while they are only partially certain what side the coin landed on. We show that IBE** more often prescribes high probability to the actual bias than JC. We also show that this happens considerably faster, that IBE** passes higher thresholds for high probability, and that it in general leads to more accurate probability distributions than JC.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Trpin, Borutborut.trpin@ff.uni-lj.si
Pellert, Max
Keywords: inference to the best explanation, belief updating, uncertain evidence
Subjects: General Issues > Confirmation/Induction
General Issues > Explanation
Depositing User: Mr. Borut Trpin
Date Deposited: 22 Dec 2017 18:34
Last Modified: 22 Dec 2017 18:34
Item ID: 14236
Journal or Publication Title: The British Journal for the Philosophy of Science
Subjects: General Issues > Confirmation/Induction
General Issues > Explanation
Date: 21 December 2017
URI: http://philsci-archive.pitt.edu/id/eprint/14236

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