Decision Synthesis: The Principles and Practice of Decision Analysis

by Stephen R. Watson, Dennis M. Buede

This book provides a synthesis of the theory of decision making and its practical application in decision analysis. Part 1 provides a detailed guide to the principles of decision theory. The authors introduce the literature on key ideas such as value theory, subjective probability theory and utility theory, and assess how these ideas, developed for individual decision makers, may apply to decision-making within organisations. Part 2 deals with the various strategies and techniques employed in decision analysis, and the application of decision theory in particular, documented cases. The authors also appraise the validity and usefulness of these procedures of decision synthesis. The book will be of interest to students and teachers of the theory and practice of decision analysis, and to psychologists, economists and operations researchers in universities and business schools; it will also be invaluable as a handbook to these and to professional managers in business and government, and to management consultants.

320 pages, Paperback

First published January 1, 1987

Reviews

[Gerrit G. · Rating 4 out of 5]

I'd say that this book is more like three stars, but due to the time the second part ("Applications") is a great read as it contains OR/decision analysis examples of projects from the 70s/80s (mostly in the industrial setting). For most data scientists this will be new, but this will effectively increase their DPS (data science per second).

The examples are really concise, so one moment you read about deciding between an oil vs a coal plant, then judge merchandizing/marketing projects on several point of sales, among others. However, if it is too fast/quick (as it was for me) you get the sources to check, and also you can obviously reread this chapter.

The first part is a short primer on decision analysis including some math and cognitive psychology as well as stats. Most people in Data Science today will know this and supply an okaish introduction.

A lot of work is done with influence diagrams and modeling is seperated from problem formulation, so you also learn a great deal about modeling (which I did not find in theory books too much).