Equivocation for the Objective Bayesian
(2015) In Erkenntnis 80(2). p.403-432- Abstract
- According to Williamson (In defense of objective Bayesianism, Oxford University Press, Oxford, 2010), the difference between empirical subjective Bayesians and objective Bayesians is that, while both hold reasonable credence to be calibrated to evidence, the objectivist also takes such credence to be as equivocal as such calibration allows. However, Williamson's prescription for equivocation generates constraints on reasonable credence that are objectionable. Herein Williamson's calibration norm is explicated in a novel way that permits an alternative equivocation norm. On this alternative account, evidence calibrated probability functions are recognised as implications of evidence calibrated density functions defined over chance... (More)
- According to Williamson (In defense of objective Bayesianism, Oxford University Press, Oxford, 2010), the difference between empirical subjective Bayesians and objective Bayesians is that, while both hold reasonable credence to be calibrated to evidence, the objectivist also takes such credence to be as equivocal as such calibration allows. However, Williamson's prescription for equivocation generates constraints on reasonable credence that are objectionable. Herein Williamson's calibration norm is explicated in a novel way that permits an alternative equivocation norm. On this alternative account, evidence calibrated probability functions are recognised as implications of evidence calibrated density functions defined over chance hypotheses. The objective Bayesian equivocates between these calibrated density functions rather than between the calibrated probability functions themselves. The result is an objective Bayesianism that avoids the main problem afflicting Williamson's original proposal. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5277797
- author
- Masterton, George LU
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Equivocation, Calibration, Objective Bayesianism, Entropy maximization, Jon Williamson
- in
- Erkenntnis
- volume
- 80
- issue
- 2
- pages
- 403 - 432
- publisher
- Springer
- external identifiers
-
- wos:000351838800009
- scopus:84925703629
- ISSN
- 1572-8420
- DOI
- 10.1007/s10670-014-9649-2
- language
- English
- LU publication?
- yes
- id
- 29765a31-77fe-40a3-b078-e35f55925323 (old id 5277797)
- date added to LUP
- 2016-04-01 10:47:00
- date last changed
- 2022-01-26 02:26:52
@article{29765a31-77fe-40a3-b078-e35f55925323,
abstract = {{According to Williamson (In defense of objective Bayesianism, Oxford University Press, Oxford, 2010), the difference between empirical subjective Bayesians and objective Bayesians is that, while both hold reasonable credence to be calibrated to evidence, the objectivist also takes such credence to be as equivocal as such calibration allows. However, Williamson's prescription for equivocation generates constraints on reasonable credence that are objectionable. Herein Williamson's calibration norm is explicated in a novel way that permits an alternative equivocation norm. On this alternative account, evidence calibrated probability functions are recognised as implications of evidence calibrated density functions defined over chance hypotheses. The objective Bayesian equivocates between these calibrated density functions rather than between the calibrated probability functions themselves. The result is an objective Bayesianism that avoids the main problem afflicting Williamson's original proposal.}},
author = {{Masterton, George}},
issn = {{1572-8420}},
keywords = {{Equivocation; Calibration; Objective Bayesianism; Entropy maximization; Jon Williamson}},
language = {{eng}},
number = {{2}},
pages = {{403--432}},
publisher = {{Springer}},
series = {{Erkenntnis}},
title = {{Equivocation for the Objective Bayesian}},
url = {{http://dx.doi.org/10.1007/s10670-014-9649-2}},
doi = {{10.1007/s10670-014-9649-2}},
volume = {{80}},
year = {{2015}},
}