Risk Analysis Foundations, Models, and Methods / Edition 1by Louis Anthony Cox Jr.
Pub. Date: 01/01/2002
Publisher: Springer US
Risk Analysis: Foundations, Models, and Methods goal is to produce inf ormation to improve decisions. It does this by relating alternative de cisions to their probable consequences and by identifying those decisi ons that make preferred outcomes more likely. Health risk assessment d raws on explicit engineering, biomathematical, and statistical consequ ence… See more details below
Risk Analysis: Foundations, Models, and Methods goal is to produce inf ormation to improve decisions. It does this by relating alternative de cisions to their probable consequences and by identifying those decisi ons that make preferred outcomes more likely. Health risk assessment d raws on explicit engineering, biomathematical, and statistical consequ ence models to describe or simulate the causal relations between actio ns and their probable effects on health. Risk communication characteri zes and presents information about health risks and uncertainties to d ecision-makers and stakeholders. Risk management applies principles fo r choosing among alternative decision alternatives or actions that aff ect exposure, health risks, or their consequences.
- Springer US
- Publication date:
- International Series in Operations Research & Management Science, #45
- Edition description:
- Product dimensions:
- 6.10(w) x 9.25(h) x 0.24(d)
Table of Contents
1: Introduction and Basic Risk Models. 1: Introduction. 1.1. Distinguishing Characteristics Of Risk Analysis. 1.2. The Traditional Health Risk Analysis Framework. 1.3. Defining Risks: Source, Target, Effect, Mechanism. 2: Basic Quantitative Risk Models. 2.1. Risk as Probability of a Binary Event. 2.2. A Binary Event with Time: Hazard Rate Models. 2.3. Calculating and Interpreting Hazard Functions. 2.4. Hazard Models for Binary Events. 2.5. Probabilities of Causation for a Binary Event. 2.6. Risk Models with Non-Binary Consequences. 3: Health Risks from Human Activities. 3.1. Risk Management Decision Support Sub-Models. 2: Risk Assessment Modeling. 1: Introduction. 1.1. Approaches to QRA: Probability, Statistical, Engineering. 2: Conditional Probability Framework for Risk Calculations. 2.1. Calculating Average Individual Risks when Individuals Respond. 2.2. Population Risks Modeled by Conditional Probabilities. 2.3. Trees, Risks and Martingales. 2.4. Value of Information in Risk Management Decisions. 3: Basic Engineering Modeling Techniques. 3.1. Compartmental Flow Simulation Models. 3.2. Applications to Pharmacokinetic Models. 3.3. Monte Carlo Uncertainty Analysis. 3.4. Applied Probability and Shastic Transition Models. 4: Introduction to Exposure Assessment. 5: A Case Study: Simulating Food Safety. 5.1. Background: The Potential Human Health Hazard. 5.2. Risk Management Setting: Many Decisions Affect Risk. 5.3. Methods and Data: Overview of Simulation Model. 5.4. Results: Baseline and Sensitivity Analysis of Options. 5.5. Uncertainty Analysis and Discussion. 5.6: Conclusions. 3: Statistical Risk Modeling. 1: Introduction. 2: Statistical Dose-Response Modeling. 2.1. Define Exposure and Response Variables, Collect Data. 2.2. Select a Model Form for the Dose-Response Relation. 2.3. Estimate Risk, Confidence Limits, and Model Fit. 2.4. Interpret Results. 3: Progress in Statistical Risk Modeling. 3.1. Dealing with Model Uncertainty and Variable Selection. 3.2. Dealing with Missing Data: New Algorithms and Ideas. 3.3. Mixture Distribution Models for Unobserved Variables. 3.4. Summary of Advances in Statistical Risk Modeling. 4: A Statistical Case Study: Soil Sampling. 4: Causality. 1: Introduction. 2: Statistical vs. Causal Risk Modeling. 3: Criteria for Causation. 3.1. Traditional Epidemiological Criteria for Causation. 3.2. Proposed Criteria for Inferring Probable Causation. 3.3. Bayesian Evidential Reasoning and Refutationism. 4: Testing Causal Graph Models with Data. 4.1. Causal Graph Models and Knowledge Representation. 4.2. Meaning of Causal Graphs. 4.3. Testing Hypothesized Causal Graph Structures. 4.4. Creating Causal Graph Structures from Data. 4.5. Search, Optimization, and Model-Averaging Heuristics. 5: Using Causal Graphs in Risk Analysis. 5.1. Drawing Probabilistic Inferences in DAG Models. 5.2. Applications of DAG Inferences in Risk Assessment. 5.3. Using DAG Models to Make Predictions. 5.4. Decision-Making and Optimization. 6: Attributable Risks in Causal Graphs. 6.1. Why is Risk Attribution Hard? 6.2. Principles for Risk Attribution. 7: Conclusions. 5: Individual Risk Management Decisions. 1: Introduction. 2: Value Functions and Risk Profiles. 3: Rational Individual Risk-Management via Expected Utility (EU). 3.1. EU Decision-Modeling Basics. 3.2. Decision-Making Algorithms and Technologies. 3.3. Optimization Modeling for Risk Management Decisions. 3.4. Axioms for EU Theories. 4: EU Theory Challenges and Alternatives to EU Theory. 4.1. Cognitive Heuristics and Biases Violate Reduction. 4.2. Other Violations of EU Axioms. 5: Subjective Probability and Subjective Expected Utility (SEU). 6: Beyond SEU: Adaptive Decision-Making with Unknown Model. 7: Conclusions. 6: Choosing Among Risk Profiles. 1: Introduction. 2: Basic EU Theory for Single-Attribute Consequences. 3: Certainty Equivalents. 3.1. Risk Attitudes, Risk Aversion, and Prospect Theory. 4. Intrinsic Value and Exponential Utility. 5: Non-Exponential SAUT Utility Functions. 6: Objective Comparisons of Risk Profiles. 6.1. First-Order Shastic Dominance (FSD). 6.2. Extensions of FSD. 7: Higher-Order Shastic Dominance and Risk Definitions. 7.1. Extensions of SSD. 8: Conclusions. 7: Multi-Attribute, Multi-Person, and Multi-Period Risks. 1: Introduction. 2: Multiattribute Utility Theory (MAUT). 2.1. Basics of Multiattribute Value and Utility Theory. 2.2. Some Practical Aspects of MAUT. 3: Applications of MAUT to Health Risks. 3.1. Using MAUT to Develop Health Status Indicators. 3.2. Independence Conditions and QALYs. 3.3. Money Values for Reductions in Risks to Life. 3.4. Perceived Risk of Risk Profiles. 4: Risks to Multiple People: Risk Equity. 5: Beyond MAUT: MCDM Approaches. 6: Choosing Among Temporal Prospects. 6.1. Discounting of Delayed and Gradual Consequences. 6.2. Sequential Choices and Effects of Event Sequencing. 6.3. Repeated Choices and Iterated Prospects. 6.4. Preferences for Timing of Uncertainty Resolution. 6.5. Changeable and Uncertain Preferences. 6.6. Choosing Among Shastic Processes for Health States. 7: Conclusions. 8: Multi-Party Risk Management Decision Processess. 1: Introduction: Risk Management Decision Processes. 2: Social Utility: Modern Utilitarianism. 3: Game Theory: Basic Ideas. 3.1. Evolutionary Game Theory and Learning. 3.2. Mechanism Design. 4: Two-Person Games of Risk Management. 4.1. Prisoner's Dilemma: Individual vs. Group Rationality. 4.2. Moral Hazard in Insurance. 4.3. Inefficiencies Due to Asymmetric Private Information. 4.4. Designing Product Liability Standards. 4.5. Principal-Agent (PA) Models. 4.6. Bargaining and Contracts for Allocating Liability. 4.7. Litigation and Bargaining Under Arbitration. 4.8. Potential Roles for a Social Decision-Maker (SDM). 5: Property Rights and Risk Externalities. 6: Agreeing on Rules: Social Contracts. 6.1. Bargaining from Behind a "Veil of Ignorance". 6.2. Collective Choice and Social Choice Functions (SCFs). 6.3. Fair Allocation, Fair Division, and Fair Auctions. 7: Introduction to Risk Communication. 7.1: Toward More Effective Risk Presentations. 7.2: Toward Designs for Better Risk Management Processes. 8: Conclusion. References. Index.
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