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The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability, and Chance

Mark Kaplan, +1 more
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TLDR
The Enterprise of Knowledge as discussed by the authors is a major conceptual and speculative philosophic investigation of knowledge, belief, and decision, which offers a distinctive approach to the improvement of knowledge where knowledge is construed as a resource for deliberation and inquiry.
Abstract
This book presents a major conceptual and speculative philosophic investigation of knowledge, belief, and decision. It offers a distinctive approach to the improvement of knowledge where knowledge is construed as a resource for deliberation and inquiry.The first three chapters of the book address the question of the revision of knowledge from a highly original point of view, one that takes issue with the fallibilist doctrines of Peirce and Popper, and with the views of Dewey, Quine, and Kuhn as well.The next ten chapters are more technical in nature but require relatively little background in mathematical technique. Among the topics discussed are inductive logic and inductive probability, utility theory, rational decision making, value conflict, chance (statistical probability), direct inference, and inverse inference.Chapters 14-17 review alternative approaches to the topic of inverse statistical inference. Much of the discussion focuses on contrasting Bayesian and anti-Bayesian reactions to R. A. Fisher's fiducial argument. This section of the book concludes with a discussion of the Neyman-Pearson-Wald approach to the foundations of statistical inference.The final chapter returns to the epistemological themes with which the book opened, emphasizing the question of the objectivity of human inquiry. An appendix provides a real-world application of Levi's theories of knowledge and probability, offering a critique of some of the methodological procedures employed in the Rasmussen Report to assess risks of major accidents in nuclear power plants. There are also references and an index."The Enterprise of Knowledge" will interest professionals and students in epistemology, philosophy of science, decision theory, probability theory, and statistical inference.

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Knightian decision theory. Part I

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Belief is credence one (in context)

Roger Clarke
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Decision making under uncertainty using imprecise probabilities

TL;DR: A well-known sufficient condition for existence of optimal decisions is generalised, and an efficient approach to calculate optimal decisions under these decision criteria is suggested.
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Severe Testing as a Basic Concept in a Neyman-Pearson Philosophy of Induction

TL;DR: In this article, the relevance of error probabilities is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data, which is a meta-statistical principle for evaluating proposed statistical inferences.
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The Stability Theory of Belief

TL;DR: The authors developed a joint theory of rational belief and degrees of belief based on three assumptions: the logical closure of rational beliefs, the axioms of probability for rational degrees of beliefs, and the so-called Lockean thesis.