Foundations of Risk Analysis: A Knowledge and Decision-Oriented Perspective
1.1 The Importance of Risk and Uncertainty Assessments.
1.2 The Need to Develop a Proper Risk Analysis Framework.
2 Common Thinking about Risk and Risk Analysis.
2.1 Accident Risk.
2.1.1 Accident Statistics.
2.1.2 Risk Analysis.
2.1.3 Reliability Analysis.
2.2 Economic Risk.
2.2.1 General Definitions of Economic Risk in Business and Project Management.
2.2.2 A Cost Risk Analysis.
2.2.3 Finance and Portfolio Theory.
2.2.4 Treatment of Risk in Project Discounted Cash Flow Analysis.
2.3 Discussion and Conclusions.
2.3.1 The Classical Approach.
2.3.2 The Bayesian Paradigm.
2.3.3 Economic Risk and Rational Decision-Making.
2.3.4 Other Perspectives and Applications.
3 How to Think about Risk and Risk Analysis.
3.1 Basic Ideas and Principles.
3.1.1 Background Information.
3.1.2 Models and Simplifications in Probability Considerations.
3.1.3 Observable Quantities.
3.2 Economic Risk.
3.2.1 A Simple Cost Risk Example.
3.2.2 Production Risk.
3.2.3 Business and Project Management.
3.2.4 Investing Money in a Stock Market.
3.2.5 Discounted Cash Flow Analysis.
3.3 Accident Risk.
4 How to Assess Uncertainties and Specify Probabilities.
4.1 What Is a Good Probability Assignment?
4.1.1 Criteria for Evaluating Probabilities.
4.1.2 Heuristics and Biases.
4.1.3 Evaluation of the Assessors.
4.1.4 Standardization and Consensus.
4.2.1 Examples of Models.
4.3 Assessing Uncertainty of Y.
4.3.1 Assignments Based on Classical Statistical Methods.
4.3.2 Analyst Judgements Using All Sources of Information.
4.3.3 Formal Expert Elicitation.
4.3.4 Bayesian Analysis.
4.4 Uncertainty Assessments of a Vector X.
4.4.1 Cost Risk.
4.4.2 Production Risk.
4.4.3 Reliability Analysis.
4.5 Discussion and Conclusions.
5 How to Use Risk Analysis to Support Decision-Making.
5.1 What Is a Good Decision?
5.1.1 Features of a Decision-Making Model.
5.1.2 Decision-Support Tools.
5.2 Some Examples.
5.2.1 Accident Risk.
5.2.2 Scrap in Place or Complete Removal of Plant.
5.2.3 Production System.
5.2.4 Reliability Target.
5.2.5 Health Risk.
5.2.7 Offshore Development Project.
5.2.8 Risk Assessment: National Sector.
5.2.9 Multi-Attribute Utility Example.
5.3 Risk Problem Classification Schemes.
5.3.1 A Scheme Based on Potential Consequences and Uncertainties.
5.3.2 A Scheme Based on Closeness to Hazard and Level of Authority.
6 Summary and Conclusions.
Appendix A: Basic Theory of Probability and Statistics.
A.1 Probability Theory.
A.1.1 Types of Probabilities.
A.1.2 Probability Rules.
A.1.3 Random Quantities (Random Variables).
A.1.4 Some Common Discrete Probability Distributions (Models).
A.1.5 Some Common Continuous Distributions (Models).
A.1.6 Some Remarks on Probability Models and Their Parameters.
A.1.7 Random Processes.
A.2 Classical Statistical Inference.
A.2.1 Non-Parametric Estimation.
A.2.2 Estimation of Distribution Parameters.
A.2.3 Testing Hypotheses.
A.3 Bayesian Inference.
A.3.1 Statistical (Bayesian) Decision Analysis.
Appendix B: Terminology.
"...refreshing, very well written, useful for its intended audience...in line with the way most statisticians would approach the problem..." (Short Book Reviews, 2004)
- Extremely topical as there is enormous public concern about risk
- Presents a framework that allows the reader to deal with risk and uncertainty for many application areas
- Highlights modelling as a tool for reducing uncertainties
- Offers a new perspective on the use of parametric probability models valuable reading for statisticians, engineers and risk analysts