Goals and the Informativeness of Prior Probabilities

Citation data:

Erkenntnis, ISSN: 0165-0106, Page: 1-24

Publication Year:
Usage 29
Downloads 29
Captures 2
Readers 2
Social Media 55
Shares, Likes & Comments 53
Tweets 2
Repository URL:
Olav Benjamin Vassend
Springer Nature
Arts and Humanities, Mathematics
Most Recent Tweet View All Tweets
article description
I argue that information is a goal-relative concept for Bayesians. More precisely, I argue that how much information (or confirmation) is provided by a piece of evidence depends on whether the goal is to learn the truth or to rank actions by their expected utility, and that different confirmation measures should therefore be used in different contexts. I then show how information measures may reasonably be derived from confirmation measures, and I show how to derive goal-relative non-informative and informative priors given background information. Finally, I argue that my arguments have important implications for both objective and subjective Bayesianism. In particular, the Uniqueness Thesis is either false or must be modified. Moreover, objective Bayesians must concede that pragmatic factors systematically influence which priors are rational, and subjective Bayesians must concede that pragmatic factors sometimes partly determine which prior distribution most accurately represents an agent’s epistemic state.

This article has 0 Wikipedia mention.