Bayesian Epistemology

Oxford: Oxford University Press (2003)
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Abstract

Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information sources. Measurement of the coherence of information is a controversial matter: arguably, the more coherent a set of information is, the more confident we may be that its content is true, other things being equal. The authors offer a new treatment of coherence which respects this claim and shows its relevance to scientific theory choice. Bovens and Hartmann apply this methodology to a wide range of much discussed issues regarding evidence, testimony, scientific theories, and voting. Bayesian Epistemology is an essential tool for anyone working on probabilistic methods in philosophy, and has broad implications for many other disciplines.

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Epilogue

Presents some general reflections on the role and the challenges of probabilistic modelling in philosophy.

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Luc Bovens
University of North Carolina, Chapel Hill

Citations of this work

Conspiracy Theories and Evidential Self-Insulation.M. Giulia Napolitano - 2021 - In Sven Bernecker, Amy K. Flowerree & Thomas Grundmann (eds.), The Epistemology of Fake News. Oxford University Press. pp. 82-105.
The Structure of Epistemic Probabilities.Nevin Climenhaga - 2020 - Philosophical Studies 177 (11):3213-3242.
Imprecise Probabilities.Seamus Bradley - 2019 - Stanford Encyclopedia of Philosophy.
Inference to the Best Explanation.Peter Lipton - 2004 - In Martin Curd & Stathis Psillos (eds.), The Routledge Companion to Philosophy of Science. Routledge. pp. 193.

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