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The British Journal for the Philosophy of Science Advance Access originally published online on September 10, 2007
The British Journal for the Philosophy of Science 2007 58(4):689-708; doi:10.1093/bjps/axm032
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Copyright © The Author 2007. Published by Oxford University Press on behalf of British Society for the Philosophy of Science.

Inductive Influence

Jon Williamson

Philosophy, SECL, University of Kent, Canterbury CT2 7NF, UK

j.williamson{at}kent.ac.uk


  Abstract

Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief Formula to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to objective Bayesian nets, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an inductive influence on the next observation. I show how this approach can be used to capture the Johnson–Carnap continuum of inductive methods, as well as the Nix–Paris continuum, and show how inductive influence can be measured.

1 Introduction
2 The Problem
3 Diagnosis
4 Objective Bayesian Nets
5 Resolution
6 The Johnson–Carnap Continuum
7 The Nix–Paris Continuum
8 Linguistic Slack
9 Frequencies and Degrees of Belief
10 Conclusion


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This article has been cited by other articles:


Home page
 J Logic ComputationHome page
J. Williamson
Aggregating Judgements by Merging Evidence
J Logic Computation, June 1, 2009; 19(3): 461 - 473.
[Abstract] [PDF]


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