The Probabilistic Foundations of Rational Learning
by Simon M. Huttegger
This book develops a general approach to learning within the framework of probability theory, drawing on Bayesian epistemology, and discusses its applications to decision and game theory. It will appeal to philosophers working on epistemology as well as to economists, psychologists and computer scientists.
Paperback
English
Brand New
Publisher Description
According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences.
Table of Contents
1. Consistency and symmetry; 2. Bounded rationality; 3. Pattern learning; 4. Large worlds; 5. Radical probabilism; 6. Reflection; 7. Disagreement; 8. Consensus; Appendices.
Promotional
This book extends Bayesian epistemology to develop new approaches to general rational learning within the framework of probability theory.
Promotional "Headline"
This book extends Bayesian epistemology to develop new approaches to general rational learning within the framework of probability theory.
Description for Bookstore
This book develops a general approach to learning within the framework of probability theory, drawing on Bayesian epistemology, and discusses its applications to decision and game theory. It will appeal to philosophers working on epistemology as well as to economists, psychologists and computer scientists.
Description for Library
This book develops a general approach to learning within the framework of probability theory, drawing on Bayesian epistemology, and discusses its applications to decision and game theory. It will appeal to philosophers working on epistemology as well as to economists, psychologists and computer scientists.
Details
