An Objective Justification of Bayesianism II: The Consequences of Minimizing Inaccuracy*
By Hannes Leitgeb, Richard Pettigrew. Philosophia Scientiæ, 2010
210 citations recorded by the source corpus.
Subfields: Artificial Intelligence, History and Philosophy of Science, Philosophy
View Paper: https://www.semanticscholar.org/paper/6376d67cc5f3f3aa6f3563aa9552340649fd4580
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In History and Philosophy of Science
- Adam J. L. Harris
- Juergen Landes
- Jürgen Landes
- Ulrike Hahn
- Richard Pettigrew
- Hannes Leitgeb
- Benjamin Levinstein
In Philosophy
- Adam J. L. Harris
- Juergen Landes
- Jürgen Landes
- Ulrike Hahn
- Richard Pettigrew
- Hannes Leitgeb
- Benjamin Levinstein
Citations in the corpus
- Probabilism, entropies and strictly proper scoring rules (2015, background): “12 Recent epistemic justifications of probabilism are accuracy-based argu13 ments [24, 25, 30, 31, 49], which all build on [11].”
- An Objective Justification of Bayesianism I: Measuring Inaccuracy* (2010, background): “In Leitgeb and Pettigrew (2010), we investigate the consequences of these norms.”
- Strictly Proper Scoring Rules (2014, background): “Recently, quadratic inaccuracy measures, such as S Brier, have been advocated in [30, 31] on the grounds that they are the only class of measures which keep an agent out of certain epistemic dilemmas.”
- What Does It Mean to be Biased: Motivated Reasoning and Rationality (2014, background): “Leitgeb and Pettigrew (2010b) demonstrate that for a suitable measure of accuracy (justified in Leitgeb & Pettigrew, 2010a; but see Levinstein, 2012), Bayesianism follows from the simple premise that an agent ought to approximate the truth, and hence seek to minimize inaccuracy.”