http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Risk Analysis of Complex and Uncertain Systems
"Tony Cox is among the most active and creative architects and users of quantitative risk analysis. This book is full of interesting equations, conceptual designs and conundrums that characterize QRA and its applications to risk management. Informed by trenchant thinking and perceptive writing, this is a timely resource for the risk analysis community." Michael R. Greenberg, Professor and Faculty Dean, Edward J. Bloustein School of Planning and Public Policy, Rutgers University "Tony Cox's impressive book applies both simple and cutting-edge risk models to a variety of fields. It demonstrates creative but tractable ways to model real-world problems of great practical importance regarding biological, engineering, and social risks. This book deserves to become influential and widely read by those seeking practical state-of-the-art methods for risk analysis." Vicki Bier, Professor, Engineering Physics and Industrial Engineering & Director - Center for Human Performance and Risk Analysis, University of Wisconsin-Madison "Risk analysis is as old as the first cave dweller mulling how to survive winter or woolly mammoths. It remains essential today in our world of accidents, disease, climate change and terrorism. But some say that quantitative risk analysis is too complicated or time-consuming, and that qualitative intuition is a better way to make decisions. In this ambitious and meticulous book, Tony Cox makes the case that quantitative risk analysis is actually the antidote to uncertainty and complexity, superior to other modes of thinking, both practical and effective. Cox raises the bar on a vital debate." Jonathan B. Wiener, Duke University, and President, Society for Risk Analysis (2008) "This is an excellent, approachable read for any risk manager. Simple but devastating examples show how not to do risk analysis. Insightful case studies apply quantitative risk assessment to natural, random threats, and to terrorist threats posed by an intelligent adversary. This is a timely and much-needed resource for skeptical consumers of risk analysis advice and products, and for those who need to improve upon current, dangerously simplistic, guidance on threat assessment and risk management." Gerald G. Brown, Distinguished Professor, Operations Research Department, Naval Postgraduate School, Monterey, CA "This book provides a refreshing new perspective on quantitative risk assessment, and should be of interest to a wide range of practitioners. Drawing substantially on his own work, Tony Cox demonstrates that QRA can be applied even to challenging and complex systems." Suresh Moolgavkar, Vice President and Principal Scientist, Exponent Scientific Consulting; Professor of Epidemiology, University of Washington .
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.
Probability Theory and Stochastic Processes
International Series in Operations Research & Management Science, 129