David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Proceedings of the Aristotelian Society 107 (1pt2):169 - 185 (2007)
There is a lot of philosophically interesting work being done in the borderlands between traditional and formal epistemology. It is easy to think that this would all be one-way traffic. When we try to formalise a traditional theory, we see that its hidden assumptions are inconsistent or otherwise untenable. Or we see that the proponents of the theory had been conflating two concepts that careful formal work lets us distinguish. Either way, the formalist teaches the traditionalist a lesson about what the live epistemological options are. I want to argue, more or less by example, that the traffic here should be twoway. By thinking carefully about considerations that move traditional epistemologists, we can find grounds for questioning some presuppositions that many formal epistemologists make. To make this more concrete, I’m going to be looking at a Bayesian objection to a certain kind of dogmatism about justification. Several writers have urged that the incompatibility of dogmatism with a kind of Bayesianism is a reason to reject dogmatism.1 I rather think that it is reason to question the Bayesianism. To put the point slightly more carefully, there is a simple proof that dogmatism (of the kind I envisage) can’t be modelled using standard Bayesian modelling tools. Rather than conclude that dogmatism is therefore flawed, I conclude that we need better modelling tools. I’ll spend a fair bit of this paper on outlining a kind of model that (a) allows us to model dogmatic reasoning, (b) is motivated by the epistemological considerations that motivate dogmatism, and (c) helps with some familiar problems besetting the Bayesian. I’m going to work up to that problem somewhat indirectly. I’ll start with looking at the kind of sceptical argument that motivates dogmatism. I’ll then briefly rehearse the argument that shows dogmatism and Bayesianism are incompatible. Then in the bulk of the paper I’ll suggest a way of making Bayesian models more flexible so they are no longer incompatible with dogmatism..
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References found in this work BETA
Timothy Williamson (2000). Knowledge and its Limits. Oxford University Press.
Isaac Levi (1980). The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability, and Chance. The MIT Press.
Alan Hájek (2003). What Conditional Probability Could Not Be. Synthese 137 (3):273--323.
Fred Dretske (1971). Conclusive Reasons. Australasian Journal of Philosophy 49 (1):1 – 22.
John Maynard Keynes (1921). A Treatise on Probability. Dover Publications.
Citations of this work BETA
Susanna Rinard (2014). The Principle of Indifference and Imprecise Probability. Thought: A Journal of Philosophy 3 (2):110-114.
Luca Moretti (2015). In Defence of Dogmatism. Philosophical Studies 172 (1):261-282.
Susanna Rinard (2013). Against Radical Credal Imprecision. Thought: A Journal of Philosophy 2 (1):157-165.
Luca Moretti (2014). Global Scepticism, Underdetermination and Metaphysical Possibility. Erkenntnis 79 (2):381-403.
Martin Smith (2015). Evidential Incomparability and the Principle of Indifference. Erkenntnis 80 (3):605-616.
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