Risk Analysis of Complex and Uncertain Systems / Edition 1by Louis Anthony Cox Jr.
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… See more details below
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.
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, the book emphasizes methods and strategies for modeling causal relations in complex and uncertain systems to the point at which effective risk management decisions can be made. Individual sections of the book introduce QRA, show how to avoid bad risk analysis, illustrate the principles for doing better analysis, and then show specific applications and extensions.
- Springer US
- Publication date:
- International Series in Operations Research & Management Science, #129
- Edition description:
- Softcover reprint of hardcover 1st ed. 2009
- Product dimensions:
- 6.14(w) x 9.21(h) x 0.94(d)
Table of Contents
1 INTRODUCTION TO RISK ANALYSIS.- Quantitative Risk Assessment Goals and Challenges.- to Engineering Risk Analysis.- to Health Risk Analysis.- 2 AVOIDING BAD RISK ANALYSIS.- Limitations of Risk Assessment Using Risk Matrices.- Limitations of Quantitative Risk Assessment Using Aggregate Exposure and Risk Models.- 3 PRINCIPLES FOR DOING BETTER.- Identifying Nonlinear Causal Relations in Large Data Sets.- Overcoming Preconceptions and Confirmation Biases Using Data Mining.- Estimating the Fraction of Disease Caused by One Component of a Complex Mixture: Bounds for Lung Cancer.- Bounding Resistance Risks for Penicillin.- Confronting Uncertain Causal Mechanisms – Portfolios of Possibilities.- Determining What Can Be Predicted: Identifiability.- 4 APPLICATIONS AND EXTENSIONS.- Predicting the Effects of Changes: Could Removing Arsenic from Tobacco Smoke Significantly Reduce Smoker Risks of Lung Cancer?.- Simplifying Complex Dynamic Networks: A Model of Protease Imbalance and COPD Dynamic Dose-Response.- Value of Information (VOI) in Risk Management Policies for Tracking and Testing Imported Cattle for BSE.- Improving Antiterrorism Risk Analysis.- Designing Resilient Telecommunications Networks.
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