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Cited by 49Epistemology, Ethics, and MetaphysicsLogic, Reasoning, and KnowledgeBayesian Modeling and Causal Inference

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Quitting Certainties: A Bayesian Framework Modeling Degrees of Belief

Titelbaum, Michael G.·2012
DOI 10.1093/acprof:oso/9780199658305.001.0001
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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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Metadata

Authors
Titelbaum, M. G.
Affiliations
University of Wisconsin–Madison (US)
Corresponding author
Titelbaum, Michael G.
Publisher
Oxford University PressOxford
Published
2012-12-20
DOI
10.1093/acprof:oso/9780199658305.001.0001
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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