David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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OUP Oxford (2004)
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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Joshua Shepherd & James Justus (2015). X-Phi and Carnapian Explication. Erkenntnis 80 (2):381-402.
Mike Oaksford & Nick Chater (2009). Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Behavioral and Brain Sciences 32 (1):69-84.
Arif Ahmed (2015). Hume and the Independent Witnesses. Mind 124 (496):1013-1044.
Adam J. L. Harris, Ulrike Hahn, Jens K. Madsen & Anne S. Hsu (2015). The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach. Cognitive Science 39 (8):n/a-n/a.
Stefan Schubert (2012). Coherence Reasoning and Reliability: A Defense of the Shogenji Measure. Synthese 187 (2):305-319.
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