Abstract
dc:description.abstractThis paper is about how agents learn. There is a picture of learning that is very influential in epistemology; I call it 'the Classical Picture'. As influential as it is, it is a flawed picture of learning, and epistemology is distorted by it. In this paper, I offer an alternative: the Calibration Picture. It is based on an extended analogy between agents and measuring devices. Epistemology looks very different from the Calibration point of view. Distinctions that are absolute, given the Classical Picture, are relative, given the Calibration Picture. These include the distinction between enabling and justifying roles of experience, the distinction between a priori and a posteriori knowledge, and the distinction between irrationality and ignorance. The beautiful thing about the Calibration Picture is that it gives you a precise way to characterise what is absolute, and a precise way to recover Classical distinctions from that absolute thing, relative to a context. In this way, the Calibration Picture enables you to recover much of the power of the Classical Picture, while offering a new way to understand its significance.
Degree
thesis:*- Department dc:contributor.department
- Massachusetts Institute of Technology. Department of Linguistics and Philosophy.
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2013
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Rochford, Damien (Damien Joseph)
- Advisor dc:contributor.advisor
-
- Robert Stalnaker.
Subjects
dc:subject × 1Rights
dc:rights- Statement dc:rights
-
- M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
- Licence dc:rights.uri
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/1721.1/84421
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/84421