Should We Risk It? (eBook)

Exploring Environmental, Health, and Technological Problem Solving

Daniel M. Kammen, David M. Hassenzahl (Autoren)

eBook Download: PDF

2018
424 Seiten
Princeton University Press (Verlag)
978-0-691-18831-7 (ISBN)

Infos

How dangerous is smoking? What are the risks of nuclear power or of climate change? What are the chances of dying on an airplane? More importantly, how do we use this information once we have it? The demand for risk analysts who are able to answer such questions has grown exponentially in recent years. Yet programs to train these analysts have not kept pace. In this book, Daniel Kammen and David Hassenzahl address that problem. They draw together, organize, and seek to unify previously disparate theories and methodologies connected with risk analysis for health, environmental, and technological problems. They also provide a rich variety of case studies and worked problems, meeting the growing need for an up-to-date book suitable for teaching and individual learning. The specific problems addressed in the book include order-of-magnitude estimation, dose-response calculations, exposure assessment, extrapolations and forecasts based on experimental or natural data, modeling and the problems of complexity in models, fault-tree analysis, managing and estimating uncertainty, and social theories of risk and risk communication. The authors cover basic and intermediate statistics, as well as Monte Carlo methods, Bayesian analysis, and various techniques of uncertainty and forecast evaluation. The volume's unique approach will appeal to a wide range of people in environmental science and studies, health care, and engineering, as well as to policy makers confronted by the increasing number of decisions requiring risk and cost/benefit analysis. Should We Risk It? will become a standard text in courses involving risk and decision analysis and in courses of applied statistics with a focus on environmental and technological issues.

Autor

Daniel M. Kammen is Associate Professor of Energy and Society and director of the Renewable and Appropriate Energy Laboratory at the University of California, Berkeley. He has received international awards for his work on renewable energy and development and for his work on risk analysis and forecasting. David M. Hassenzahl is Assistant Professor of Environmental Studies at the University of Nevada, Las Vegas. He has been an environmental risk professional in both the public and private sectors.School of Public and International Affairs, Princeton University, where his research is focused on the role of risk analysis in policy making. He has been an environmental risk professional in both the public and private sectors.

Produkt-Details

• Erscheint lt. Verlag: 5.6.2018
• Verlagsort: Princeton
• Sprache: englisch
• Themenwelt: Naturwissenschaften
• Schlagworte: Abatement Cost • Addition • Adverse effect • Alternatives • Base rate • Bayesian inference • Botulism • Calculation • carcinogen • Carcinogenesis • Confidence interval • confounding • Correlation and dependence • Cost–benefit analysis • cost-effectiveness analysis • decay product • Decision Analysis • Decision-Making • Decision Theory • detection • developmental toxicity • disaster • Disease • Drawback • epidemiology • error • Error bar • Errors and residuals • Error Term • estimation • expected loss • expected value • exposure assessment • Exposure factor • Externality • Extrapolation • failure rate • False positive rate • Fault Tree Analysis • Finding • Food contaminant • Forecasting • genotoxicity • Groupthink • hazardous waste • Ideology • inference • Legislation • Likelihood Function • Measurement • Meta-analysis • Monte Carlo Method • National policy • Observation • Observational error • Parameter • Permissible exposure limit • Pesticide • Point mutation • Policy • posterior probability • precondition • Prediction • Predictive Modelling • Prioritization • Probability • Probability Distribution • Prospect Theory • Qualitative risk analysis • Quantity • Radon mitigation • Ranking (information retrieval) • Remedial action • reproductive toxicity • Requirement • Resources for the Future • result • Risk • risk analysis • risk assessment • Risk Aversion • Risk Management • Risk Perception • Selection bias • Sensitivity Analysis • Significant figures • Specific Risk • standard deviation • Standard Score • Statistic • statistical significance • Statistics • Symptom • threshold model • Toxicology • Toxin • Type I and type II errors • Uncertainty • uncertainty analysis • year
• ISBN-10: 0-691-18831-9 / 0691188319
• ISBN-13: 978-0-691-18831-7 / 9780691188317