Tracking Confirmation
Bibliographic Data
| ID | 10706683 |
|---|---|
| Authors | Igor Douven (0000-0003-3413-080X, Institut d'Histoire et de Philosophie des Sciences et des Techniques, corresponding author) |
| Year | 2021 |
| Volume | 88 |
| Issue | 3 |
| Pages | 398-414 |
| Publication date | 2021-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0031-8248 • E-ISSN: 1539-767X |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1086/712792 |
| OpenAlex | W4230506225 |
| Language | EN |
| Citations received | 1 |
| References cited | 20 |
Confirmation is a graded notion: evidence can confirm a hypothesis to a greater or lesser degree. There has been debate about how to measure degree of confirmation. Starting from the observation that we would like evidence to be a discriminating indicator of truth, we conduct computer simulations to determine how well the various known measures of confirmation predict the extent to which a given piece of evidence fulfills that role, given a hypothesis of interest. The outcomes show that some measures are markedly better indicators of truth than others
Data mining · Degree (music) · Econometrics · Epistemology · Measure (data warehouse) · Physics · Tracking (education) · Computer Science · Explainable Artificial Intelligence (XAI · Mathematics · Misinformation and Its Impacts · Philosophy · Psychology · Topic Modeling
Tracking Truth
An introduction to ROC analysis
From covariation to causation
Bayesian t tests for accepting and rejecting the null hypothesis
Confirming Power of Observations Metricized for Decisions among Hypotheses
The Plurality of Bayesian Measures of Confirmation and the Problem of Measure Sensitivity
Logical Foundations of Evidential Support
On Bayesian Measures of Evidential Support
Degree of Factual Support
Philosophical Explanations
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |