Risk Analysis Foundations, Models, and MethodsSpringer Science & Business Media, 6 déc. 2012 - 556 pages Risk Analysis: Foundations, Models, and Methods fully addresses the questions of "What is health risk analysis?" and "How can its potentialities be developed to be most valuable to public health decision-makers and other health risk managers?" Risk analysis provides methods and principles for answering these questions. It is divided into methods for assessing, communicating, and managing health risks. Risk assessment quantitatively estimates the health risks to individuals and to groups from hazardous exposures and from the decisions or activities that create them. It applies specialized models and methods to quantify likely exposures and their resulting health risks. Its goal is to produce information to improve decisions. It does this by relating alternative decisions to their probable consequences and by identifying those decisions that make preferred outcomes more likely. Health risk assessment draws on explicit engineering, biomathematical, and statistical consequence models to describe or simulate the causal relations between actions and their probable effects on health. Risk communication characterizes and presents information about health risks and uncertainties to decision-makers and stakeholders. Risk management applies principles for choosing among alternative decision alternatives or actions that affect exposure, health risks, or their consequences. |
Table des matières
| 1 | |
Health Risks from Human Activities | 34 |
Conditional Probability Framework for Risk Calculations | 53 |
Basic Engineering Modeling Techniques | 71 |
Introduction to Exposure Assessment | 90 |
CHAPTER 3 | 132 |
Progress in Statistical Risk Modeling | 184 |
Soil Sampling | 200 |
Subjective Probability and Subjective Expected Utility SEU | 334 |
Adaptive DecisionMaking with Unknown Models | 344 |
CHAPTER 6 | 350 |
Intrinsic Value and Exponential Utility | 358 |
Objective Comparisons of Risk Profiles | 365 |
HigherOrder Stochastic Dominance and Risk Definitions | 384 |
Conclusions | 390 |
Applications of MAUT to Health Risks | 401 |
CHAPTER 4 | 217 |
Criteria for Causation | 224 |
CHAPTER 5 | 239 |
Testing Causal Graph Models with Data | 240 |
Using Causal Graphs in Risk Analysis | 269 |
Attributable Risks in Causal Graphs | 283 |
Conclusions | 299 |
Individual Risk Management Decisions | 301 |
Rational Individual RiskManagement via Expected Utility EU | 307 |
EU Theory Challenges and Alternatives to EU Theory | 321 |
Risk Equity | 411 |
Choosing Among Temporal Prospects | 419 |
Conclusions | 438 |
Modern Utilitarianism | 453 |
TwoPerson Games of Risk Management | 461 |
Property Rights and Risk Externalities | 488 |
Introduction to Risk Communication | 504 |
Conclusion | 512 |
| 545 | |
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algorithms allocation alternative approach associated assumptions attributable risk average axioms Bayesian biases calculated Campylobacter campylobacteriosis CFUs Chapter chicken choices choose classification tree conditional independence conditional probability consequences contamination costs covariates DAG model data set decision-making defined dose dose-response model estimated example expected utility expected value exponential utility exposure formula FQ-resistant frequency distribution Gibbs sampling hazard function hazard rate health risk implies increase individual risks inference influence diagrams input interpreted lung cancer measured methods microbial load multiple Nash equilibrium observed optimal outcomes parameters payoff players population potential Pr(p Pr(X Pr(Y predictions preferences probability distribution problem prospects quantities random variable reduce regression represented response risk analysis risk assessment risk attributed risk factors risk management risk management decisions risk model risk profiles risk-averse sampling selection simulation smoking specific statistical stochastic stochastic dominance Table techniques theory uncertainty utility function vector
