The Science of Decision Making

The Science of Decision Making : A Problem-Based Approach Using Excel

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Provides the reader with a perspective on the efficient operation of complicated systems. Spreadsheets are used to employ and teach techniques. Includes the facets of probability that relate to decision making.
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Product details

  • Paperback | 720 pages
  • 211 x 279 x 38mm | 1,730g
  • New York, United States
  • English
  • 1. Auflage
  • w. numerous figs.
  • 0471318272
  • 9780471318279

Back cover copy

Learn how to model and solve real decision-making problems using Excel.

This book is a practical, accessible, engaging, and deeply penetrating introduction to the science of decision making. It is a successful fusion of problem-based learning and spreadsheet computation with decision science. It surveys the modfels of operations research and of probability.

As you work through the problems within the chapters, you will acquire a potent set of tools for solving problems, and you'll see how to make practical use of them. You will find examples from engineering, economics, finance, operations management, business, medical decision making, and the sciences. You will see how to use the methodology in a variety of academic disciplines and career paths.


Problem-based learning enhanced by realistic examples drawn from a broad range of disciplines. Depth of understanding, enabled by the marriage of the methods of operations research with spreadsheet computation. Breadth of coverage, welding models of probability to those of decision making. Self-contained as concerns Excel. Useful Add-Ins. A CD-ROM packaged with the text, contains Premium Solver for Education. TreePlan. RiskSim. several useful functions and appropriate data sets.
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Table of contents

Preface. PART A: INTRODUCTION. The Science of Decision Making. Getting Started with Spreadsheets. PART B: USING LINEAR PROGRAMS. Analyzing Solutions of Linear Programs. A Survey of Linear Programs. Networks. Integer Programs. PART C: PROBABILITY FOR DECISION MAKING. Introduction to Probability Models. Discrete Random Variables. Decision Trees and Generalizations. Utility Theory and Decision Analysis. Continuous Random Variables. PART D: STOCHASTIC SYSTEMS. Inventory. Markov Chains. Queueing. Simulation. PART E: GAME THEORY. Game Theory. PART F: SOLVING LINEAR PROGRAMS. Solving Linear Equations. The Simplex Method. Duality. Appendix: Note on Excel. Index.
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About Eric V. Denardo

Educated at Princeton and Northwestern Universities, Eric V. Denardo worked for AT&T and The Rand Corporation before joining the faculty of Yale University. His work is noted for its breadth, innovation, and elegance.
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