Michael G. Titelbaum presents a new Bayesian framework for modeling rational degrees of belief¿the first of its kind to represent rational requirements on agents who undergo certainty loss. He compares the framework to alternative solutions, and applies it to cases in epistemology, decision theory, the theory of identity, and quantum mechanics.
Michael G. Titelbaum presents a new Bayesian framework for modeling rational degrees of belief¿the first of its kind to represent rational requirements on agents who undergo certainty loss. He compares the framework to alternative solutions, and applies it to cases in epistemology, decision theory, the theory of identity, and quantum mechanics.
Michael G. Titelbaum grew up in the San Francisco Bay Area. He completed a BA in philosophy at Harvard College, a PhD in philosophy at the University of California, Berkeley, and a postdoc at the Australian National University. He is an Assistant Professor of philosophy at the University of Wisconsin-Madison.
Inhaltsangabe
I: Going Modeling 1: Introduction 2: Models and Norms II Elements of CLF 3: Modeling Framework 4: Applying CLF Models to Stories 5: Three Objections III Memory Loss 6: Generalized Conditionalization 7: Suppositional Consistency IV Context-Sensitivity 8: The Proper Expansion Principle 9: Applying (PEP) 10: Alternative Updating Schemes 11: Indierence and Quantum V Conclusion 12: Loose Ends 13: Modeling Advantages Proofs
I: Going Modeling 1: Introduction 2: Models and Norms II Elements of CLF 3: Modeling Framework 4: Applying CLF Models to Stories 5: Three Objections III Memory Loss 6: Generalized Conditionalization 7: Suppositional Consistency IV Context-Sensitivity 8: The Proper Expansion Principle 9: Applying (PEP) 10: Alternative Updating Schemes 11: Indierence and Quantum V Conclusion 12: Loose Ends 13: Modeling Advantages Proofs
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