Inference to the Best Explanation in Uncertain Evidential Situations
Bibliographic Data
| ID | 8396232 |
|---|---|
| Authors | Borut Trpin (0000-0002-1121-9388, University of Ljubljana, corresponding author), Max Pellert (0000-0002-6557-7607, Complexity Science Hub Vienna, corresponding author) |
| Year | 2019 |
| Volume | 70 |
| Issue | 4 |
| Pages | 977-1001 |
| Publication date | 2019-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The British Journal for the Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0007-0882 • E-ISSN: 1464-3537 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/bjps/axy027 |
| OpenAlex | W2778157824 |
| Language | EN |
| Citations received | 5 |
| References cited | 19 |
It has recently been argued that a non-Bayesian probabilistic version of inference to the best explanation (IBE*) has a number of advantages over Bayesian conditionalization (Douven [2013]; Douven and Wenmackers [2017]). We investigate how IBE* could be generalized to uncertain evidential situations and formulate a novel updating rule IBE**. We then inspect how it performs in comparison to its Bayesian counterpart, Jeffrey conditionalization (JC), in a number of simulations where two agents, each updating by IBE** and JC, respectively, try to detect the bias of a coin while they are only partially certain what side the coin landed on. We show that IBE** more often prescribes high probability to the actual bias than JC. We also show that this happens considerably faster, that IBE** passes higher thresholds for high probability, and that it in general leads to more accurate probability distributions than JC. 1. Introduction2. Generalizing Inference to the Best Explanation to Uncertain Evidential Situations3. Detecting the Bias of a Coin4. Overall Performance of IBE** versus Jeffrey Conditionalization5. Speed of Convergence6. The Threshold for High Subjective Probability7. Epistemic Inaccuracy8. Conclusions
Economics · Epistemology · Inference · Management science · Computer Science · Epistemology, Ethics, and Metaphysics · Logic, Reasoning, and Knowledge · Philosophy · Philosophy and History of Science
Conditionalization and observation
A Nonpragmatic Vindication of Probabilism
An Objective Justification of Bayesianism I
Leitgeb and Pettigrew on Accuracy and Updating
An Objective Justification of Bayesianism II
Dynamic Coherence and Probability Kinematics
Inference to the Best Explanation Made Coherent
Is There a Dutch Book Argument for Probability Kinematics
The Logic of Explanatory Power
On Assertion and Indicative Conditionals
The Picture Theory of Mental Images
Assertion, Knowledge, and Rational Credibility
Inference to the Best Explanation versus Bayes’s Rule in a Social Setting
Inference to the Best Explanation, Dutch Books, and Inaccuracy Minimisation
| Citations per year | 0.83 |
|---|---|
| Citation span | 2020 - 2025 (6) |
| Highly cited | No |