Authors
Borut Trpin
Ludwig Maximilians Universität, München
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.
Keywords Inference to the Best Explanation  Abduction  Bayesianism
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Reprint years 2019
DOI 10.1093/bjps/axy027
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References found in this work BETA

The Logic of Decision.Richard C. Jeffrey - 1965 - University of Chicago Press.
Laws and Symmetry.Bas C. van Fraassen - 1989 - Oxford University Press.
Inference to the Best Explanation.Peter Lipton - 1993 - Routledge/Taylor and Francis Group.
Change in View.Gilbert Harman - 1986 - MIT Press.

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Citations of this work BETA

The Ecological Rationality of Explanatory Reasoning.Igor Douven - 2020 - Studies in History and Philosophy of Science Part A 79:1-14.
Jeffrey conditionalization: proceed with caution.Borut Trpin - 2020 - Philosophical Studies 177 (10):2985-3012.

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