Decision Theory

Preface

Goals

The goal of this book is to give an overview of fundamental ideas and results about rational decision making under uncertainty, highlighting the implications of these results for the philosophy and practice of statistics. The book grew from lecture notes from graduate courses taught at the Institute of Statistics and Decision Sciences at Duke University, at the Johns Hopkins University, and at the University of Washington. It is designed primarily for graduate students in statistics and biostatistics, both at the Masters and PhD level. However, the interdisciplinary nature of the material should make it interesting to students and researchers in economics (choice theory, econometrics), engineering (signal processing, risk analysis), computer science (pattern recognition, artificial intelligence), and scientists who are interested in the general principles of experimental design and analysis.

Rational decision making has been a chief area of investigation in a number of disciplines, in some cases for centuries. Several of the contributions and viewpoints are relevant to both the education of a well-rounded statistician and to the development of sound statistical practices. Because of the wealth of important ideas, and the pressure from competing needs in current statistical curricula, our first course in decision theory aims for breadth rather than depth. We paid special attention to two aspects: bridging the gaps among the different fields that have contributed to ...