Terje Aven, University of Stavanger, Norway.

• Preface to the second edition ixPreface to the first edition xi1 Introduction 11.1 The importance of risk and uncertainty assessments 11.2 The need to develop a proper risk analysis framework 4Bibliographic notes 62 Common thinking about risk and risk analysis 72.1 Accident risk 72.1.1 Accident statistics 72.1.2 Risk analysis 112.1.3 Reliability analysis 242.2 Economic risk 282.2.1 General definitions of economic risk in business and project management 282.2.2 A cost risk analysis 302.2.3 Finance and portfolio theory 312.2.4 Treatment of risk in project discounted cash flow analysis 342.3 Discussion and conclusions 362.3.1 The classical approach 362.3.2 The Bayesian paradigm 372.3.3 Economic risk and rational decision-making 392.3.4 Other perspectives and applications 402.3.5 Conclusions 43Bibliographic notes 433 How to think about risk and risk analysis 473.1 Basic ideas and principles 473.1.1 Background knowledge 523.1.2 Models and simplifications in probability considerations 533.1.3 Observable quantities 533.2 Economic risk 543.2.1 A simple cost risk example 543.2.2 Production risk 573.2.3 Business and project management 593.2.4 Investing money in a stock market 603.2.5 Discounted cash flow analysis 613.3 Accident risk 623.4 Discussion 63Bibliographic notes 684 How to assess uncertainties and specify probabilities 714.1 What is a good probability assignment? 724.1.1 Criteria for evaluating probabilities 724.1.2 Heuristics and biases 744.1.3 Evaluation of the assessors 754.1.4 Standardization and consensus 764.2 Modeling 764.2.1 Examples of models 774.2.2 Discussion 784.3 Assessing uncertainty of Y 794.3.1 Assignments based on classical statistical methods 804.3.2 Analyst judgments using all sources of information 814.3.3 Formal expert elicitation 824.3.4 Bayesian analysis 834.4 Uncertainty Assessments of a Vector X 914.4.1 Cost risk 914.4.2 Production risk 934.4.3 Reliability analysis 944.5 Discussion 974.5.1 Risk analysis and science 974.5.2 Probability and utility 984.5.3 Probability and knowledge 994.5.4 Probability models 994.5.5 Firm and vague probabilities 1004.5.6 The need for seeing beyond probabilities 1004.5.7 Interval (imprecise) probabilities 1014.5.8 Example of interval (imprecise) probabilities in a risk analysis setting 1024.5.9 Possibility theory 1034.5.10 Example of interval (imprecise) probabilities in a risk analysis context using possibility theory 1044.5.11 Final comments 106Bibliographic notes 1085 How to use risk analysis to support decision-making 1115.1 What is a good decision? 1125.1.1 Features of a decision-making model 1135.1.2 Decision-support tools 1145.1.3 Discussion 1195.2 Some examples 1225.2.1 Accident risk 1225.2.2 Scrap in place or complete removal of plant 1255.2.3 Production system 1305.2.4 Reliability target 1315.2.5 Health risk 1335.2.6 Warranties 1355.2.7 Offshore development project 1365.2.8 Risk assessment: National sector 1385.2.9 Multi-attribute utility example 1405.3 Risk problem classification schemes 1435.3.1 A scheme based on consequences and uncertainties 1435.3.2 A scheme based on closeness to hazard and level of authority 147Bibliographic notes 1586 Summary and conclusions 161Appendix A: Basic theory of probability and statistics 165A.1 Probability theory 165A.1.1 Types of probabilities 165A.1.2 Probability rules 168A.1.3 Random quantities (random variables) 172A.1.4 Some common discrete probability distributions (models) 176A.1.5 Some common continuous distributions (models) 178A.1.6 Some remarks on probability models and their parameters 182A.1.7 Random processes 183A.2 Classical statistical inference 184A.2.1 Nonparametric estimation 184A.2.2 Estimation of distribution parameters 185A.2.3 Testing hypotheses 187A.2.4 Regression 188A.3 Bayesian inference 189A.3.1 Statistical (Bayesian) decision analysis 191Bibliographic notes 192Appendix B: Terminology 193B.1 Risk management: Relationships between key terms 195References 197Index 207

"The book provides a framework for understanding, conducting and using risk analysis suitable for advanced undergraduates, graduates, analysts and researchers from statistics, engineering, finance, medicine and the physical sciences, as well as for managers facing decision making problems involving risk and uncertainty."  (ZentralblattMATH, 1 December 2012)

Terje Aven, University of Stavanger, Norway.

• Preface to the second edition ixPreface to the first edition xi1 Introduction 11.1 The importance of risk and uncertainty assessments 11.2 The need to develop a proper risk analysis framework 4Bibliographic notes 62 Common thinking about risk and risk analysis 72.1 Accident risk 72.1.1 Accident statistics 72.1.2 Risk analysis 112.1.3 Reliability analysis 242.2 Economic risk 282.2.1 General definitions of economic risk in business and project management 282.2.2 A cost risk analysis 302.2.3 Finance and portfolio theory 312.2.4 Treatment of risk in project discounted cash flow analysis 342.3 Discussion and conclusions 362.3.1 The classical approach 362.3.2 The Bayesian paradigm 372.3.3 Economic risk and rational decision-making 392.3.4 Other perspectives and applications 402.3.5 Conclusions 43Bibliographic notes 433 How to think about risk and risk analysis 473.1 Basic ideas and principles 473.1.1 Background knowledge 523.1.2 Models and simplifications in probability considerations 533.1.3 Observable quantities 533.2 Economic risk 543.2.1 A simple cost risk example 543.2.2 Production risk 573.2.3 Business and project management 593.2.4 Investing money in a stock market 603.2.5 Discounted cash flow analysis 613.3 Accident risk 623.4 Discussion 63Bibliographic notes 684 How to assess uncertainties and specify probabilities 714.1 What is a good probability assignment? 724.1.1 Criteria for evaluating probabilities 724.1.2 Heuristics and biases 744.1.3 Evaluation of the assessors 754.1.4 Standardization and consensus 764.2 Modeling 764.2.1 Examples of models 774.2.2 Discussion 784.3 Assessing uncertainty of Y 794.3.1 Assignments based on classical statistical methods 804.3.2 Analyst judgments using all sources of information 814.3.3 Formal expert elicitation 824.3.4 Bayesian analysis 834.4 Uncertainty Assessments of a Vector X 914.4.1 Cost risk 914.4.2 Production risk 934.4.3 Reliability analysis 944.5 Discussion 974.5.1 Risk analysis and science 974.5.2 Probability and utility 984.5.3 Probability and knowledge 994.5.4 Probability models 994.5.5 Firm and vague probabilities 1004.5.6 The need for seeing beyond probabilities 1004.5.7 Interval (imprecise) probabilities 1014.5.8 Example of interval (imprecise) probabilities in a risk analysis setting 1024.5.9 Possibility theory 1034.5.10 Example of interval (imprecise) probabilities in a risk analysis context using possibility theory 1044.5.11 Final comments 106Bibliographic notes 1085 How to use risk analysis to support decision-making 1115.1 What is a good decision? 1125.1.1 Features of a decision-making model 1135.1.2 Decision-support tools 1145.1.3 Discussion 1195.2 Some examples 1225.2.1 Accident risk 1225.2.2 Scrap in place or complete removal of plant 1255.2.3 Production system 1305.2.4 Reliability target 1315.2.5 Health risk 1335.2.6 Warranties 1355.2.7 Offshore development project 1365.2.8 Risk assessment: National sector 1385.2.9 Multi-attribute utility example 1405.3 Risk problem classification schemes 1435.3.1 A scheme based on consequences and uncertainties 1435.3.2 A scheme based on closeness to hazard and level of authority 147Bibliographic notes 1586 Summary and conclusions 161Appendix A: Basic theory of probability and statistics 165A.1 Probability theory 165A.1.1 Types of probabilities 165A.1.2 Probability rules 168A.1.3 Random quantities (random variables) 172A.1.4 Some common discrete probability distributions (models) 176A.1.5 Some common continuous distributions (models) 178A.1.6 Some remarks on probability models and their parameters 182A.1.7 Random processes 183A.2 Classical statistical inference 184A.2.1 Nonparametric estimation 184A.2.2 Estimation of distribution parameters 185A.2.3 Testing hypotheses 187A.2.4 Regression 188A.3 Bayesian inference 189A.3.1 Statistical (Bayesian) decision analysis 191Bibliographic notes 192Appendix B: Terminology 193B.1 Risk management: Relationships between key terms 195References 197Index 207

"The book provides a framework for understanding, conducting and using risk analysis suitable for advanced undergraduates, graduates, analysts and researchers from statistics, engineering, finance, medicine and the physical sciences, as well as for managers facing decision making problems involving risk and uncertainty."  (ZentralblattMATH, 1 December 2012)

Terje Aven, University of Stavanger, Norway.

• Preface to the second edition ixPreface to the first edition xi1 Introduction 11.1 The importance of risk and uncertainty assessments 11.2 The need to develop a proper risk analysis framework 4Bibliographic notes 62 Common thinking about risk and risk analysis 72.1 Accident risk 72.1.1 Accident statistics 72.1.2 Risk analysis 112.1.3 Reliability analysis 242.2 Economic risk 282.2.1 General definitions of economic risk in business and project management 282.2.2 A cost risk analysis 302.2.3 Finance and portfolio theory 312.2.4 Treatment of risk in project discounted cash flow analysis 342.3 Discussion and conclusions 362.3.1 The classical approach 362.3.2 The Bayesian paradigm 372.3.3 Economic risk and rational decision-making 392.3.4 Other perspectives and applications 402.3.5 Conclusions 43Bibliographic notes 433 How to think about risk and risk analysis 473.1 Basic ideas and principles 473.1.1 Background knowledge 523.1.2 Models and simplifications in probability considerations 533.1.3 Observable quantities 533.2 Economic risk 543.2.1 A simple cost risk example 543.2.2 Production risk 573.2.3 Business and project management 593.2.4 Investing money in a stock market 603.2.5 Discounted cash flow analysis 613.3 Accident risk 623.4 Discussion 63Bibliographic notes 684 How to assess uncertainties and specify probabilities 714.1 What is a good probability assignment? 724.1.1 Criteria for evaluating probabilities 724.1.2 Heuristics and biases 744.1.3 Evaluation of the assessors 754.1.4 Standardization and consensus 764.2 Modeling 764.2.1 Examples of models 774.2.2 Discussion 784.3 Assessing uncertainty of Y 794.3.1 Assignments based on classical statistical methods 804.3.2 Analyst judgments using all sources of information 814.3.3 Formal expert elicitation 824.3.4 Bayesian analysis 834.4 Uncertainty Assessments of a Vector X 914.4.1 Cost risk 914.4.2 Production risk 934.4.3 Reliability analysis 944.5 Discussion 974.5.1 Risk analysis and science 974.5.2 Probability and utility 984.5.3 Probability and knowledge 994.5.4 Probability models 994.5.5 Firm and vague probabilities 1004.5.6 The need for seeing beyond probabilities 1004.5.7 Interval (imprecise) probabilities 1014.5.8 Example of interval (imprecise) probabilities in a risk analysis setting 1024.5.9 Possibility theory 1034.5.10 Example of interval (imprecise) probabilities in a risk analysis context using possibility theory 1044.5.11 Final comments 106Bibliographic notes 1085 How to use risk analysis to support decision-making 1115.1 What is a good decision? 1125.1.1 Features of a decision-making model 1135.1.2 Decision-support tools 1145.1.3 Discussion 1195.2 Some examples 1225.2.1 Accident risk 1225.2.2 Scrap in place or complete removal of plant 1255.2.3 Production system 1305.2.4 Reliability target 1315.2.5 Health risk 1335.2.6 Warranties 1355.2.7 Offshore development project 1365.2.8 Risk assessment: National sector 1385.2.9 Multi-attribute utility example 1405.3 Risk problem classification schemes 1435.3.1 A scheme based on consequences and uncertainties 1435.3.2 A scheme based on closeness to hazard and level of authority 147Bibliographic notes 1586 Summary and conclusions 161Appendix A: Basic theory of probability and statistics 165A.1 Probability theory 165A.1.1 Types of probabilities 165A.1.2 Probability rules 168A.1.3 Random quantities (random variables) 172A.1.4 Some common discrete probability distributions (models) 176A.1.5 Some common continuous distributions (models) 178A.1.6 Some remarks on probability models and their parameters 182A.1.7 Random processes 183A.2 Classical statistical inference 184A.2.1 Nonparametric estimation 184A.2.2 Estimation of distribution parameters 185A.2.3 Testing hypotheses 187A.2.4 Regression 188A.3 Bayesian inference 189A.3.1 Statistical (Bayesian) decision analysis 191Bibliographic notes 192Appendix B: Terminology 193B.1 Risk management: Relationships between key terms 195References 197Index 207

"The book provides a framework for understanding, conducting and using risk analysis suitable for advanced undergraduates, graduates, analysts and researchers from statistics, engineering, finance, medicine and the physical sciences, as well as for managers facing decision making problems involving risk and uncertainty."  (ZentralblattMATH, 1 December 2012)