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Quitting Certainties: A Bayesian Framework Modeling Degrees of Belief
Abstract
Subjective Bayesianism is one of the most popular tools of contemporary epistemology, using probability mathematics to provide comprehensive rational constraints both for an agent’s degrees of belief at a given time and for the evolution of those degrees of belief over time. Yet Conditionalization (the traditional Bayesian updating rule) has trouble modeling cases involving memory loss and context-sensitivity, because these cases involve the loss of certainties over time. This book proposes a new Bayesian modeling framework, the Certainty-Loss Framework (or CLF), that yields correct verdicts about rational requirements in such cases. The framework resolves a variety of outstanding problems for Bayesianism, including the Sleeping Beauty Problem concerning self-locating beliefs and difficulties squaring Bayesianism with Everettian interpretations of quantum mechanics. CLF is developed within a carefully-articulated formal modeling methodology that focuses our attention on the boundaries of our models’ applicability and the precise relation between formal systems and norms. The result is a framework that merges the advantages of formal modeling with the complexities of everyday epistemic life.
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Self-citation
Metadata
- Authors
- Titelbaum, M. G.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Titelbaum, Michael G.
- Publisher
- Oxford University PressOxford
- Published
- 2012-12-20
- OpenAlex
- W4250785122
- Cited by
- 49
- References
- 5
- Citations by year
- 2025: 19 · 2024: 6 · 2023: 2 · 2022: 2 · 2021: 5 · 2020: 1 · 2019: 4 · 2018: 1 · 2017: 4 · 2016: 1 · 2015: 2 · 2014: 2
- FWCI
- 2.10
- Type
- book
- Language
- en
- Open access
- closed
- Topics
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- subjective Bayesianism, conditionalization, memory loss, context sensitivity, Sleeping Beauty problem, self-locating belief, degrees of belief, rationality constraints
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