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Info-Gap Decision Theory : Decisions Under Severe Uncertainty - Yakov Ben-Haim

Info-Gap Decision Theory

Decisions Under Severe Uncertainty


Published: 11th October 2006
Format: PDF
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  • Hardcover View Product Published: 1st October 2006

Everyone makes decisions, but not everyone is a decision analyst. A decision analyst uses quantitative models and computational methods to formulate decision algorithms, assess decision performance, identify and evaluate options, determine trade-offs and risks, evaluate strategies for investigation, and so on. This book is written for decision analysts.
The term "decision analyst" covers an extremely broad range of practitioners. Virtually all engineers involved in design (of buildings, machines, processes, etc.) or analysis (of safety, reliability, feasibility, etc.) are decision analysts, usually without calling themselves by this name. In addition to engineers, decision analysts work in planning offices for public agencies, in project management consultancies, they are engaged in manufacturing process planning and control, in financial planning and economic analysis, in decision support for medical or technological diagnosis, and so on and on. Decision analysts provide quantitative support for the decision-making process in all areas where systematic decisions are made.
This second edition entails changes of several sorts. First, info-gap theory has found application in several new areas - especially biological conservation, economic policy formulation, preparedness against terrorism, and medical decision-making. Pertinent new examples have been included. Second, the combination of info-gap analysis with probabilistic decision algorithms has found wide application. Consequently "hybrid" models of uncertainty, which were treated exclusively in a separate chapter in the previous edition, now appear throughout the book as well as in a separate chapter. Finally, info-gap explanations of robust-satisficing behavior, and especially the Ellsberg and Allais "paradoxes", are discussed in a new chapter together with a theorem indicating when robust-satisficing will have greater probability of success than direct optimizing with uncertain models.

* New theory developed systematically.
* Many examples from diverse disciplines.
* Realistic representation of severe uncertainty.
* Multi-faceted approach to risk.
* Quantitative model-based decision theory.

Preface to the 1st edition
Preface to the 2nd edition
1. Overview
2. Uncertainty
3. Robustness and Opportuneness
4. Value Judgments
5. Antagonistic and Sympathetic Immunities
6. Gambling and Risk Sensitivity
7. Value of Information
8. Learning
9. Coherent Uncertainties and Consensus
10. Hybrid Uncertainties
11. Robust-Satisficing Behavior
12. Retrospective Essay: Risk Assessment in Project Management
13. Implications of Info-Gap Uncertainty

ISBN: 9780080465708
ISBN-10: 0080465706
Format: PDF
Language: English
Published: 11th October 2006
Publisher: Elsevier Science
Edition Number: 2