David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Dissertation, University of Massachusetts, Amherst (2010)
Bayesian Epistemology is a general framework for thinking about agents who have beliefs that come in degrees. Theories in this framework give accounts of rational belief and rational belief change, which share two key features: (i) rational belief states are represented with probability functions, and (ii) rational belief change results from the acquisition of evidence. This dissertation focuses specifically on the second feature. I pose the Evidence Question: What is it to have evidence? Before addressing this question we must have an understanding of Bayesian Epistemology. The first chapter argues that we should understand Bayesian Epistemology as giving us theories that are evaluative and not action-guiding. I reach this verdict after considering the popular ‘ought’-implies-‘can’ objection to Bayesian Epistemology. The second chapter argues that it is important for theories in Bayesian Epistemology to answer the Evidence Question, and distinguishes between internalist and externalist answers. The third and fourth chapters present and defend a specific answer to the Evidence Question. The account is inspired by reliabilist accounts of justification, and attempts to understand what it is to have evidence by appealing solely to considerations of reliability. Chapter 3 explains how to understand reliability, and how the account fits with Bayesian Epistemology, in particular, the requirement that an agent’s evidence receive probability 1. Chapter 4 responds to objections, which maintain that the account gives the wrong verdict in a variety of situations including skeptical scenarios, lottery cases, scientific cases, and cases involving inference. After slight modifications, I argue that my account has the resources to answer the objections. The fifth chapter considers the possibility of losing evidence. I show how my account can model these cases. To do so, however, we require a modification to Conditionalization, the orthodox principle governing belief change. I present such a modification. The sixth and seventh chapters propose a new understanding of Dutch Book Arguments, historically important arguments for Bayesian principles. The proposal shows that the Dutch Book Arguments for implausible principles are defective, while the ones for plausible principles are not. The final chapter is a conclusion
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Franz Dietrich & Christian List (2013). Reasons for (Prior) Belief in Bayesian Epistemology. Synthese 190 (5):781-786.
Stephan Hartmann & Jan Sprenger (forthcoming). Bayesian Epistemology. In Duncan Pritchard & Sven Bernecker (eds.), Routledge Companion to Epistemology. Routledge
John Shoemaker (2003). Epistemological Naturalism and Mark Kaplan's Decision Theory. Philo 6 (2):249-262.
Patrick Maher (2010). Bayesian Probability. Synthese 172 (1):119 - 127.
Frank Döring (1999). Why Bayesian Psychology is Incomplete. Philosophy of Science 66 (3):389.
Gregory Wheeler (2012). Objective Bayesian Calibration and the Problem of Non-Convex Evidence. British Journal for the Philosophy of Science 63 (4):841-850.
Geoffrey Hellman (1997). Bayes and Beyond. Philosophy of Science 64 (2):191-221.
James Hawthorne (1993). Bayesian Induction IS Eliminative Induction. Philosophical Topics 21 (1):99-138.
Alan Hájek & Stephan Hartmann (2010). Bayesian Epistemology. In J. Dancy et al (ed.), A Companion to Epistemology. Blackwell
Richard Otte (1994). A Solution to a Problem for Bayesian Confirmation Theory. British Journal for the Philosophy of Science 45 (2):764-769.
Added to index2012-04-27
Total downloads25 ( #150,152 of 1,792,914 )
Recent downloads (6 months)3 ( #282,298 of 1,792,914 )
How can I increase my downloads?