This book is intended for use by natural resource managers and scientists, and students in the fields of natural resource management, ecology, and conservation biology, who are confronted with complex and difficult decision making problems. The book takes readers through the process of developing a structured approach to decision making, by firstly deconstructing decisions into component parts, which are each fully analyzed and then reassembled to form a working decision model. The book integrates common-sense ideas about problem definitions, such as the need for decisions to be driven by…mehr
This book is intended for use by natural resource managers and scientists, and students in the fields of natural resource management, ecology, and conservation biology, who are confronted with complex and difficult decision making problems. The book takes readers through the process of developing a structured approach to decision making, by firstly deconstructing decisions into component parts, which are each fully analyzed and then reassembled to form a working decision model. The book integrates common-sense ideas about problem definitions, such as the need for decisions to be driven by explicit objectives, with sophisticated approaches for modeling decision influence and incorporating feedback from monitoring programs into decision making via adaptive management. Numerous worked examples are provided for illustration, along with detailed case studies illustrating the authors' experience in applying structured approaches. There is also a series of detailed technical appendices. An accompanying website provides computer code and data used in the worked examples.
Additional resources for this book can be found at: www.wiley.com/go/conroy/naturalresourcemanagement.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Michael J. Conroy is a Senior Research Scientist in the Warnell School of Forestry and Natural Resources at the University of Georgia. He has over thirty years experience in applications of quantitative approaches to solving problems in natural resource management and is the author of three previous books. He teaches and runs workshops in modeling, statistical estimation, and structured decision making for undergraduate and graduate students and professionals both in the US and internationally. James T. Peterson is the Assistant Unit Leader and Associate Professor for the USGS Oregon Cooperative Fish and Wildlife Research Unit at Oregon State University. He has been developing and teaching courses in applied quantitative decision making to undergraduate and graduate students and professionals in natural resource and related disciplines for more than a decade.
Inhaltsangabe
List of boxes xi Preface xiii Acknowledgements xiv Guide to using this book xv Companion website xvii PART I. INTRODUCTION TO DECISION MAKING 1 1 Introduction: Why a Structured Approach in Natural Resources? 3 The role of decision making in natural resource management 4 Common mistakes in framing decisions 5 What is structured decision making (SDM)? 6 Why should we use a structured approach to decision making? 7 Limitations of the structured approach to decision making 8 Adaptive resource management 9 Summary 10 References 10 2 Elements of Structured Decision Making 13 First steps: defining the decision problem 13 General procedures for structured decision making 15 Predictive modeling: linking decisions to objectives prospectively 17 Uncertainty and how it affects decision making 18 Dealing with uncertainty in decision making 21 Summary 23 References 23 3 Identifying and Quantifying Objectives in Natural Resource Management 24 Identifying objectives 24 Identifying fundamental and means objectives 25 Clarifying objectives 28 Separating objectives from science 29 Barriers to creative decision making 30 Types of fundamental objectives 32 Identifying decision alternatives 34 Quantifying objectives 38 Dealing with multiple objectives 38 Multi-attribute valuation 41 Utility functions 43 Other approaches 50 Additional considerations 52 Decision, objectives, and predictive modeling 55 References 55 4 Working with Stakeholders in Natural Resource Management 57 Stakeholders and natural resource decision making 57 Stakeholder analysis 59 Stakeholder governance 62 Working with stakeholders 68 Characteristics of good facilitators 68 Getting at stakeholder values 71 Stakeholder meetings 72 The first workshop 74 References 76 Additional reading 76 PART II. TOOLS FOR DECISION MAKING AND ANALYSIS 77 5 Statistics and Decision Making 79 Basic statistical ideas and terminology 80 Using data in statistical models for description and prediction 100 Linear models 104 Hierarchical models 116 Bayesian inference 129 Resampling and simulation methods 140 Statistical significance 145 References 146 Additional reading 146 6 Modeling the Influence of Decisions 147 Structuring decisions 147 Influence diagrams 148 Frequent mistakes when structuring decisions 153 Defining node states 157 Decision trees 159 Solving a decision model 160 Conditional independence and modularity 164 Parameterizing decision models 165 Elicitation of expert judgment 179 Quantifying uncertainty in expert judgment 188 Group elicitation 189 The care and handling of experts 190 References 191 Additional reading 191 7 Identifying and Reducing Uncertainty in Decision Making 192 Types of uncertainty 192 Irreducible uncertainty 193 Reducible uncertainty 194 Effects of uncertainty on decision making 197 Sensitivity analysis 203 Value of information 217 Reducing uncertainty 220 References 230 Additional reading 231 8 Methods for Obtaining Optimal Decisions 232 Overview of optimization 233 Factors affecting optimization 234 Multiple attribute objectives and constrained optimization 239 Dynamic decisions 246 Optimization under uncertainty 249 Analysis of the decision problem 253 Suboptimal decisions and "satisficing" 256 Other problems 257 Summary 258 References 258 PART III. APPLICATIONS 261 9 Case Studies 263 Case study 1 Adaptive Harvest Management of American Black Ducks 263 Case study 2 Management of Water Resources in the Southeastern US 276 Case study 3 Regulation of Largemouth Bass Sport Fishery in Georgia 284 Summary 291 References 291 10 Summary, Lessons Learned, and Recommendations 294 Summary 294 Lessons learned 294 Structured decision making for Hector's Dolphin conservation 295 Landowner incentives for conservation of early successional habitats in Georgia 298 Cahaba shiner 299 Other lessons 303 References 304 PART IV. APPENDICES 307 Appendix A Probability and Distributional Relationships 309 Probability axioms 309 Conditional probability 309 Conditional independence 310 Expected value of random variables 311 Law of total probability 311 Bayes' theorem 312 Distribution moments 313 Sample moments 316 Additional reading 316 Appendix B Common Statistical Distributions 317 General distribution characteristics 317 Continuous distributions 320 Discrete distributions 329 Reference 338 Additional Reading 338 Appendix C Methods for Statistical Estimation 339 General principles of estimation 339 Method of moments 342 Least squares 343 Maximum likelihood 346 Bayesian approaches 353 References 372 Appendix D Parsimony, Prediction, and Multi-Model Inference 373 General approaches to multi-model inference 373 Multi-model inference and model averaging 376 Multi-model Bayesian inference 380 References 383 Appendix E Mathematical Approaches to Optimization 384 Review of general optimization principles 385 Classical programming 392 Nonlinear programming 397 Linear programming 399 Dynamic decision problems 402 Decision making under structural uncertainty 419 Generalizations of Markov decision processes 427 Heuristic methods 427 References 429 Appendix F Guide to Software 430 Appendix G Electronic Companion to Book 432 Glossary 433 Index 449
List of boxes xi Preface xiii Acknowledgements xiv Guide to using this book xv Companion website xvii PART I. INTRODUCTION TO DECISION MAKING 1 1 Introduction: Why a Structured Approach in Natural Resources? 3 The role of decision making in natural resource management 4 Common mistakes in framing decisions 5 What is structured decision making (SDM)? 6 Why should we use a structured approach to decision making? 7 Limitations of the structured approach to decision making 8 Adaptive resource management 9 Summary 10 References 10 2 Elements of Structured Decision Making 13 First steps: defining the decision problem 13 General procedures for structured decision making 15 Predictive modeling: linking decisions to objectives prospectively 17 Uncertainty and how it affects decision making 18 Dealing with uncertainty in decision making 21 Summary 23 References 23 3 Identifying and Quantifying Objectives in Natural Resource Management 24 Identifying objectives 24 Identifying fundamental and means objectives 25 Clarifying objectives 28 Separating objectives from science 29 Barriers to creative decision making 30 Types of fundamental objectives 32 Identifying decision alternatives 34 Quantifying objectives 38 Dealing with multiple objectives 38 Multi-attribute valuation 41 Utility functions 43 Other approaches 50 Additional considerations 52 Decision, objectives, and predictive modeling 55 References 55 4 Working with Stakeholders in Natural Resource Management 57 Stakeholders and natural resource decision making 57 Stakeholder analysis 59 Stakeholder governance 62 Working with stakeholders 68 Characteristics of good facilitators 68 Getting at stakeholder values 71 Stakeholder meetings 72 The first workshop 74 References 76 Additional reading 76 PART II. TOOLS FOR DECISION MAKING AND ANALYSIS 77 5 Statistics and Decision Making 79 Basic statistical ideas and terminology 80 Using data in statistical models for description and prediction 100 Linear models 104 Hierarchical models 116 Bayesian inference 129 Resampling and simulation methods 140 Statistical significance 145 References 146 Additional reading 146 6 Modeling the Influence of Decisions 147 Structuring decisions 147 Influence diagrams 148 Frequent mistakes when structuring decisions 153 Defining node states 157 Decision trees 159 Solving a decision model 160 Conditional independence and modularity 164 Parameterizing decision models 165 Elicitation of expert judgment 179 Quantifying uncertainty in expert judgment 188 Group elicitation 189 The care and handling of experts 190 References 191 Additional reading 191 7 Identifying and Reducing Uncertainty in Decision Making 192 Types of uncertainty 192 Irreducible uncertainty 193 Reducible uncertainty 194 Effects of uncertainty on decision making 197 Sensitivity analysis 203 Value of information 217 Reducing uncertainty 220 References 230 Additional reading 231 8 Methods for Obtaining Optimal Decisions 232 Overview of optimization 233 Factors affecting optimization 234 Multiple attribute objectives and constrained optimization 239 Dynamic decisions 246 Optimization under uncertainty 249 Analysis of the decision problem 253 Suboptimal decisions and "satisficing" 256 Other problems 257 Summary 258 References 258 PART III. APPLICATIONS 261 9 Case Studies 263 Case study 1 Adaptive Harvest Management of American Black Ducks 263 Case study 2 Management of Water Resources in the Southeastern US 276 Case study 3 Regulation of Largemouth Bass Sport Fishery in Georgia 284 Summary 291 References 291 10 Summary, Lessons Learned, and Recommendations 294 Summary 294 Lessons learned 294 Structured decision making for Hector's Dolphin conservation 295 Landowner incentives for conservation of early successional habitats in Georgia 298 Cahaba shiner 299 Other lessons 303 References 304 PART IV. APPENDICES 307 Appendix A Probability and Distributional Relationships 309 Probability axioms 309 Conditional probability 309 Conditional independence 310 Expected value of random variables 311 Law of total probability 311 Bayes' theorem 312 Distribution moments 313 Sample moments 316 Additional reading 316 Appendix B Common Statistical Distributions 317 General distribution characteristics 317 Continuous distributions 320 Discrete distributions 329 Reference 338 Additional Reading 338 Appendix C Methods for Statistical Estimation 339 General principles of estimation 339 Method of moments 342 Least squares 343 Maximum likelihood 346 Bayesian approaches 353 References 372 Appendix D Parsimony, Prediction, and Multi-Model Inference 373 General approaches to multi-model inference 373 Multi-model inference and model averaging 376 Multi-model Bayesian inference 380 References 383 Appendix E Mathematical Approaches to Optimization 384 Review of general optimization principles 385 Classical programming 392 Nonlinear programming 397 Linear programming 399 Dynamic decision problems 402 Decision making under structural uncertainty 419 Generalizations of Markov decision processes 427 Heuristic methods 427 References 429 Appendix F Guide to Software 430 Appendix G Electronic Companion to Book 432 Glossary 433 Index 449
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