Cox Jr. Risk Analysis of Complex and Uncertain Systems

1. Auflage 2009
ISBN: 978-0-387-89014-2
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 129, 436 Seiten

Reihe: International Series in Operations Research & Management Science

In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems - systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence - and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure.  This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.

Autoren/Hrsg.

Cox Jr., Louis Anthony

Inhaltsverzeichnis

1;Preface;7
1.1;Why This Book?;7
1.2;For Whom Is It Meant?;7
1.3;What’s in It?;8
1.4;Some Specific Risk Models and Applications for Interested Specialists;12
1.5;Why Do These Models and Methods Matter?;14
2;Acknowledgments;15
3;Contents;19
4;Introduction to Risk Analysis;29
4.1;Quantitative Risk Assessment Goals and Challenges;30
4.1.1;The Quantitative Risk Assessment (QRA) Paradigm;30
4.1.2;Against QRA: Toward Concern-Driven Risk Management;34
4.1.3;Toward Less Analytic, More Pluralistic Risk Management;38
4.1.4;Alternatives to QRA in Recent Policy Making: Some Practical Examples;40
4.1.5;Concern-Driven Risk Management;42
4.1.6;How Effective Is Judgment-Based Risk Management?;45
4.1.7;Performance of Individual Judgment vs. Simple Quantitative Models;46
4.1.8;Performance of Consensus Judgments vs. Simple Quantitative Models;53
4.1.9;How Effective Can QRA Be?;58
4.1.10;Summary and Conclusions;59
4.2;Introduction to Engineering Risk Analysis;61
4.2.1;Overview of Risk Analysis for Engineered Systems;61
4.2.2;Using Risk Analysis to Improve Decisions;65
4.2.3;Hazard Identification: What ShouldWe Worry About?;65
4.2.4;Structuring Risk Quantification and Displaying Results: Models for Accident Probabilities and Consequences;67
4.2.5;Quantifying Model Components and Inputs;70
4.2.6;Risk Characterization;84
4.2.7;Methods for Risk Management Decision Making;93
4.2.8;Game-Theory Models for Risk Management Decision Making;96
4.2.9;Conclusions;98
4.3;Introduction to Health Risk Analysis;99
4.3.1;Introduction;99
4.3.2;Quantitative Definition of Health Risk;101
4.3.3;A Bayesian Network Framework for Health Risk Assessment;103
4.3.4;Hazard Identification;106
4.3.5;Exposure Assessment;111
4.3.6;Dose-Response Modeling;115
4.3.7;Risk and Uncertainty Characterization for Risk Management;119
4.3.8;Conclusions;122
5;Avoiding Bad Risk Analysis;124
5.1;Limitations of Risk Assessment Using Risk Matrices;125
5.1.1;Introductory Concepts and Examples;126
5.1.2;A Normative Decision-Analytic Framework;128
5.1.3;Logical Compatibility of Risk Matrices with Quantitative Risks;132
5.1.4;Risk Matrices with Too Many Colors Give Spurious Resolution;138
5.1.5;Risk Ratings Do Not Necessarily Support Good Resource Allocation Decisions;141
5.1.6;Discussion and Conclusions;146
5.1.7;Appendix A: Proof of Theorem 1;147
5.2;Limitations of Quantitative Risk Assessment Using Aggregate Exposure and Risk Models;149
5.2.1;What Is Frequency?;150
5.2.2;Limitations of Aggregate Exposure Metrics;157
5.2.3;Limitations of Aggregate Exposure-Response Models: An Antimicrobial Risk Assessment Case Study;165
5.2.4;Some Limitations of Risk Priority-Scoring Methods;173
5.2.5;Conclusions;184
6;Principles for Doing Better;186
6.1;Identifying Nonlinear Causal Relations in Large Data Sets;187
6.1.1;Nonlinear Exposure-Response Relations;188
6.1.2;Entropy, Mutual Information, and Conditional Independence;190
6.1.3;Classification Trees and Causal Graphs via Information Theory;192
6.1.4;Illustration for the Campylobacteriosis Case Control Data;195
6.1.5;Conclusions;199
6.2;Overcoming Preconceptions and Confirmation Biases Using Data Mining;201
6.2.1;Confirmation Bias in Causal Inferences;202
6.2.2;Appendix A: Computing Adjusted Ratios of Medians and Their Confidence Limits;223
6.3;Estimating the Fraction of Disease Caused by One Component of a Complex Mixture: Bounds for Lung Cancer;225
6.3.1;Motivation: Estimating Fractions of Illnesses Preventable by Removing Specific Exposures;225
6.3.2;Why Not Use Population Attributable Fractions?;226
6.3.3;Theory: Paths, Event Probabilities, Bounds on Causation;228
6.3.4;The Smoking-PAH-BPDE-p53-Lung Cancer Causal Pathway;232
6.3.5;Applying the Theory: Quantifying the Contribution of the Smoking- PAH- BPDE- p53 Pathway to Lung Cancer Risk;234
6.3.6;Uncertainties and Sensitivities;241
6.3.7;Discussion;242
6.3.8;Conclusions;243
6.4;Bounding Resistance Risks for Penicillin;245
6.4.1;Background, Hazard Identification and Scope: ReducingAmpicillin-Resistant E. faecium (AREF) Infections in ICUPatients;245
6.4.2;Methods and Data: Upper Bounds for Preventable Mortalities;247
6.4.3;Results Summary, Sensitivity, and Uncertainty Analysis;254
6.4.4;Summary and Conclusions;256
6.5;Confronting Uncertain Causal Mechanisms - Portfolios of Possibilities;259
6.5.1;Background: Cadmium and Smoking Risk;260
6.5.2;Previous Cadmium-Lung Cancer Risk Studies;261
6.5.3;Biological Mechanisms of Cadmium Lung Carcinogenesis;264
6.5.4;Quantifying Potential Cadmium Effects on Lung Cancer Risk;273
6.5.5;Discussion and Conclusions;279
6.5.6;Appendix A: Relative Risk Framework;280
6.6;Determining What Can Be Predicted: Identifiability;282
6.6.1;Identifiability;283
6.6.2;Multistage Clonal Expansion (MSCE) Models of Carcinogenesis;287
6.6.3;Nonunique Identifiability of Multistage Models from Input- Output Data;291
6.6.4;Discussion and Conclusions;296
6.6.5;Appendix A: Proof of Theorem 1;298
6.6.6;Appendix B: Listing of ITHINKTM Model Equations for the Example in Figure 11.3;300
7;Applications and Extensions;302
7.1;Predicting the Effects of Changes: Could Removing Arsenic from Tobacco Smoke Significantly Reduce Smoker Risks of Lung Cancer?;303
7.1.1;Biologically Based Risk Assessment Modeling;303
7.1.2;Arsenic as a Potential Human Lung Carcinogen;304
7.1.3;Data, Methods, and Models;307
7.1.4;Results;316
7.1.5;Sensitivities, Uncertainties, Implications, and Conclusions;318
7.1.6;Appendix A: Listing for TSCE Model of Smoking and Lung Cancer;320
7.1.7;Appendix B: Listing for MSCE Lung Cancer Model with Field Carcinogenesis;321
7.2;Simplifying Complex Dynamic Networks: A Model of Protease Imbalance and COPD Dynamic Dose- Response;323
7.2.1;Background on COPD;324
7.2.2;A Flow Process Network Model of Protease-Antiprotease Imbalance in COPD;325
7.2.3;Mathematical Analysis of the Protease-Antiprotease Network;328
7.2.4;Some Possible Implications for Experimental and Clinical COPD;333
7.2.5;Is the Model Consistent with Available Human Data?;334
7.2.6;Summary and Conclusions;336
7.2.7;Appendix A: Equilibrium in Networks of Homeostatic Processes;337
7.3;Value of Information (VOI) in Risk Management Policies for Tracking and Testing Imported Cattle for BSE;344
7.3.1;Testing Canadian Cattle for Bovine Spongiform Encephalitis ( BSE);346
7.3.2;Methods and Data;349
7.3.3;Results;362
7.3.4;Discussion;365
7.3.5;Epilogue and Conclusions;366
7.3.6;Appendix: Market Impact Assumptions and Calculations;368
7.4;Improving Antiterrorism Risk Analysis;370
7.4.1;The Risk = Threat × Vulnerability × Consequence Framework;370
7.4.2;RAMCAPTM Qualitative Risk Assessment;372
7.4.3;Limitations of RAMCAPTM for Quantitative Risk Assessment;373
7.4.4;Risk Rankings Are Not Adequate for Resource Allocation;376
7.4.5;Some Fundamental Limitations of Risk = Threat × Vulnerability × Consequence;377
7.4.6;Discussion and Conclusions;386
7.5;Designing Resilient Telecommunications Networks;389
7.5.1;Introduction: Designing Telecommunications Infrastructure Networks to Survive Intelligent Attacks;390
7.5.2;Background: Diverse Routing, Protection Paths, and Protection Switching;390
7.5.3;A Simple Two-Stage Attacker-Defender Model;394
7.5.4;Results for Networks with Dedicated Routes ("Circuit-Switched" Networks);395
7.5.5;Statistical Risk Models and Results for Scale-Free Packet Networks;399
7.5.6;Real-World Implementation Challenges: Incentives to Invest in Protection;402
7.5.7;Summary;406
7.5.8;Epilogue;407
7.6;References;409
7.7;Index;441