Synopses & Reviews
Brian Skyrms presents a set of influential essays on the nature of quantity, probability, coherence, and induction. The first part explores the nature of quantity and includes essays on tractarian nominalism, combinatorial possibility, and coherence. Part Two proceeds to examine coherent updating of degrees of belief in various learning situations. Finally, in Part Three, Skyrms develops an account of aspects of inductive reasoning, which proceeds from specific problems to general considerations. These essays span the breadth of Skyrms's illustrious career and will be essential reading for scholars and advanced students in philosophy of science and formal epistemology.
Review
"I hope that [this book] helps make more philosophers aware of the important work Brian Skyrms has been doing for several decades. The work is well worth studying for anyone dealing with these issues, and the book is a convenient resource for it."--Kenny Easwaran, Notre Dame Philosophical Reviews
About the Author
Brian Skyrms is Distinguished Professor of Logic and Philosophy of Science and Economics at the University of California, Irvine. His interests cover a range of topics, including the evolution of conventions, the social contract, inductive logic, decision theory, rational deliberation, the metaphysics of logical atomism, causality, and truth. He is the author of
Signals: Evolution, Learning, and Information (OUP, 2010).
Table of Contents
Preface
Acknowledgements
I. Zeno and the Metaphysics of Quantity
Introduction
1. Zeno's Paradox of Measure
2. Tractarian Nominalism
3. Logical Atoms and Combinatorial Possibility
4. Strict Coherence, Sigma Coherence, and the Metaphysics of Quantity
II. Coherent Degrees of Belief
Introduction
5. Higher Order Degrees of Belief
6. A Mistake in Dynamic Coherence Arguments?
7. Dynamic Coherence and Probability Kinematics
8. Updating, Supposing, and MAXENT
9. The Structure of Radical Probabilism
10. Diachronic Coherence and Radical Probabilism
III. Induction
Introduction
11. Carnapian Inductive Logic for Markov Chains
12. Carnapian Inductive Logic and Bayesian Statistics
13. Bayesian Projectibility