The Science and Management of Uncertainty: Dealing with Doubt in Natural Resource Management

Description

Uncertainty can take many forms, can be represented in many ways, and can have important implications in decision-making and policy development. This book provides a rigorous scientific framework for dealing with uncertainty in real-world situations, and provides a comprehensive study of concepts, measurements, and applications of uncertainty in ecological modeling and natural resource management. The focus of this book is on the kinds and implications of uncertainty in environmental modeling and management, with practical guidelines and examples for successful modeling and risk analysis in the face of uncertain conditions and incomplete information. Provided is a clear classification of uncertainty; methods for measuring, modeling, and communicating uncertainty; practical guidelines for capturing and representing expert knowledge and judgment; explanations of the role of uncertainty in decision-making; a guideline to avoiding logical fallacies when dealing with uncertainty; and several example cases of real-world ecological modeling and risk analysis to illustrate the concepts and approaches. Case topics provide examples of structured decision-making, statistical modeling, and related topics. A summary provides practical next steps that the reader can take in analyzing and interpreting uncertainty in real-world situations. Also provided is a glossary and a suite of references. Cover Title Page Copyright Page Preface Table of Contents 1. What is Uncertainty? Types and Sources of Uncertainty Types of Errors Key Terms An Epistemological Hierarchy References 2. Measuring Uncertainty Measurements and Estimators Uncertainty of Infrequent But Intense Events Quick Estimation Methods Issues Confounding Measures of Uncertainty References 3. Modeling Uncertainty Purpose of Models Kinds of Models and Their Use for Depicting Uncertainty Influence Diagrams, Mind Maps, and Cognitive Maps Causality and Causal Webs Modeling Data Variability and Uncertainty Complexity and Chaos Problems with Modeling Sensitivity Analysis and Influence Analysis Calibration and Validation Next: Conveying Uncertainty References 4. Communicating Uncertainty Information Deficit Communicating Probabilities Communicating the Reliability of Information Sources Syntactic Structures in Language Using Scientific Visualization to Communicate Uncertainty Explaining Counterintuitive Results Tilting the Prediction Table A Revisit to the Johari Window References 5. Capturing Expert Judgment On the Nature of Expert Judgment Objectives for Eliciting Expert Knowledge Methods for Eliciting Expert Knowledge Measuring Reliability of Expert Knowledge Biases in Expert Elicitation Thinking Well and Poorly Local and Traditional Ecological Knowledge When Expertise is Artificial References 6. Decision-Making Under Uncertainty How Classic Risk Analysis can be Wrong Introduction to Structured Decision Making Decision-Making Influences Under Uncertainty Use of Adaptive Management Perceptions of the Future References 7. Fallacies of Logic and Inference On Degrees of Uncertainty Forms of Reasoning and Logic Concepts of Belief Structures A Menagerie of Mislogic Additional Pitfalls in Logic and Inference Caveats on Rationality and Plausibility The Other Side of the Uncertainty Coin References 8. Case Topics The Uncertainty of Perception Fooled by Noise Sometimes It's Better Not to Know. But Not Always The Importance and Shortcomings of Rare Events Bayesian Propagation of Uncertainty Measuring the Range of Natural Variation To the Future References 9. Summary and Conclusions Uncertainty in its Many Forms The Value of Doubt Avoiding the Logical Fallacies Lessons to Learn When Things Go Wrong New Ways of Thinking About Uncertainty The Future of Unknowing What’s Next for You? References Index