Making Hard Decisions with Decision Tools Suite

Used this book in a "Making Decisions under Uncertainty" graduate class at the Business School of the University of São Paulo. This book brings all the basics for decision making theory, specifically quantitative aspects of it and comes along with the Decision Tools software that has some bugs but works ok. Using the book together with the software you will get a very good basic idea of what decision making is all about. If decision theory is important for your studies buy this book!!! I also recommend Max Bazerman's "Judgment in managerial decision making" as a complement to it because this one focuses more on the subjective piece of decision making (decision biases, uncertainty, etc, etc) which is not really covered well by Clemen & Reilly. With both, you have a very good toolset for starting to study Decision Making Theory.

This exceptional book is worth reading regardless of readers' specialization. A big surprise for those who don't like probability and statistics! This book could be a good introduction to Probability and Statistics with Bayesian approach. Although I cannot point out bad sections, here are some of the topics that could be improved. Influence Diagram examples are much fewer than Decision Tree diagrams. I'd love to see more complex and elaborate decision model examples for practitioners implementing their own models.In probability chapter, the authors implicitly assume that Pr(A,B) means Pr(AB) or Pr(A and B) where Pr() is "probability of" and A and B are some events. This is never mentioned in the book (as far as I recall), so it should be fixed for clarity. Also in the same chapter, chain rules and marginalization are not covered explicitly, yet a reader needs to use these general rules in exercises and in some of main contents. These rules are very important in manipulating probability and conditional probability terms.Although there are few selected answers for exercises, most of the answers are not too hard and they are self-explanatory. So self-study readers shouldn't worry too much.A curious person who might wonder what decision science and analysis is about, I recommend reading "Smart Choice" by John Hammond, et al. Less math and more compact exposition.For further study in Influence Diagrams and Bayesian Belief Nets (BBN), a good introduction is "Bayesian Networks and Decision Graphs" by Finn Jensen, et al. There are numerous good sites for BBN on web so search them online. For probability and statistics, "Probability and Statistics" by Degroot, et al. or Feller's Volume 1 are good, which are mentioned in the textbook.Note: I recommend this blue cover version rather than green one as the former comes with Palisade Decision Tools Suite and is published later.

Understanding Hard DecisionsSo that you know where I am coming from: I found this book while selecting a new text for my senior "Decision & Risk Analysis" course. There are numerous books on this subject, but none are perfect: either the presentation of the subject is hard, talking way above the heads of the audience, or too theoretical, or requiring a high level of math skills, or the presentation is at a too low level, or too narrow in focus, or not covering the whole spectrum of Decision Analysis. I chose this book for various advantages it has. The technical level of math that it requires is low, yet the analysis is high. It is written in a very easy way to read, follow and understand manner. It can be used both as a textbook for a course as well as a reference. It makes use of the most common generic tool for calculating and solving problems, the Excel spreadsheet for the suite of software that comes with the book (Palisades Decision tools) are integrated with it. The book offers a rather complete presentation of decision analysis concepts and techniques: there is a whole chapter on the important topic of sensitivity analysis, another chapter on simulation; and risk and forecasting are also presented.I find the reference section in each chapter excellent, for it helps the reader tie the literature to the concepts presented. The last chapter which gives an annotated decision analysis reading list is also very helpful. All textbooks need to have these two ingredients. It is true that numerous of the references given in the book are fairly old, but the topic is not in the fast changing computer science field, and many of the original articles and books in the decision analysis area are classics and need to be looked at carefully. (Maybe there is a lesson to learn here for those who give a minus when "old" references are included in a scholarly work.)The book is very modern for the concepts it presents (influence diagrams - a relatively newcomer in decision analysis, issues of recent interest, such as assessment, ecology and AIDS) and because it also ties with the web. The reader is directed to the web page of the Decision Analysis Society of INFORMS and this gives an important tool for keeping the knowledge presented in the book constantly updateable and broadening the spectrum of the interested reader. Another advantage: the book emphasizes structuring decision problems rather the more traditional approach to stress modeling of uncertainty and preference.I like both the structure of the book (that follows the decision analysis process) and that of each chapter which consist of an introduction, presentation of concepts and techniques, a summary section, challenging case studies accompanied by questions referring to the concepts just presented, references, and an epilogue.Best qualities of the book: clarity, good structure, interesting real or realistic fictitious case studies - extremely important to keep the student interested in a topic w