Buch, Englisch, Band 129, 436 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 703 g
Reihe: International Series in Operations Research & Management Science
Buch, Englisch, Band 129, 436 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 703 g
Reihe: International Series in Operations Research & Management Science
ISBN: 978-1-4419-4703-1
Verlag: Springer US
In 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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Betriebswirtschaft Management Risikomanagement
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Entscheidungstheorie, Sozialwahltheorie
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Risikobewertung, Risikotheorie
- Mathematik | Informatik Mathematik Operations Research
Weitere Infos & Material
Preface.- Goals and challenges for quantitative risk assessment.- Introduction to engineering risk analysis.- Introduction to health risk analysis.- Limitations of risk assessment using risk matrices.- Limitations of quantitative risk assessment using aggregate exposure and risk models.- 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.- Predicting effects of changes: could removing arsenic from tobacco smoke significantly reduce smoker risks of lung cancer.- Simplifying complex dynamic networks: a mathematical 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 anti-terrorist risk analysis.- Designing resilient telecommunications networks.- References.- Index.
