Learn the science of collecting information to make effective decisions
Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business.
This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication:
Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems
Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems
Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements
Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.
Most Helpful Customer Reviews
Traditionally, optimization problems are divided into two categories: deterministic, where the costs are fixed, and stochastic, where the costs are a probability distribution.
In this groundbreaking book, Powell and Ryzhov lucidly explore the situations where the costs are not fully known, but the cost of collecting that information is non-trivial, so the important optimization problem includes how much to invest in getting more data vs. simply using what we already know - the "exploration vs. exploitation" tradeoff.
This book presents excellent terminology and insights into these problems, and should be a good addition to anyone interested in applied optimization. I highly recommend it.
Disclaimer: I have worked with Warren Powell professionally and am also on the advisory board of Princeton's Department of Operations Research and Financial Engineering, where he is a tenured professor.
Interesting book to digest,
Including an exciting topics.
This book is well organized and provides ton of online resources for further research
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