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The Failure of Risk Management
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Table of Contents

Preface xi Acknowledgments xv PART ONE AN INTRODUCTION TO THE CRISIS 1 CHAPTER 1 Healthy Skepticism for Risk Management 3 Common Mode Failure 4 What Counts as Risk Management 8 Anecdote: The Risk of Outsourcing Drug Manufacturing 11 What Failure Means 16 Scope and Objectives of This Book 18 CHAPTER 2 Risk Management: A Very Short Introduction to Where We've Been and Where (We Think) We Are 21 The Entire History of Risk Management (in 800 Words or Less) 22 Methods of Assessing Risks 24 Risk Mitigation 26 The State of Risk Management According to Surveys 31 CHAPTER 3 How Do We Know What Works? 37 An Assessment of Self-Assessments 37 Potential Objective Evaluations of Risk Management 42 What We May Find 49 PART TWO WHY IT'S BROKEN 53 CHAPTER 4 The ?Four Horsemen? of Risk Management: Some (Mostly) Sincere Attempts to Prevent an Apocalypse 55 Actuaries 57 War Quants: How World War II Changed Risk Analysis Forever 59 Economists 63 Management Consulting: How a Power Tie and a Good Pitch Changed Risk Management 68 Comparing the Horsemen 74 Major Risk Management Problems to Be Addressed 76 CHAPTER 5 An Ivory Tower of Babel: Fixing the Confusion about Risk 79 The Frank Knight Definition 81 Risk as Volatility 84 A Construction Engineering Definition 86 Risk as Expected Loss 86 Risk as a Good Thing 88 Risk Analysis and Risk Management versus Decision Analysis 90 Enriching the Lexicon 91 CHAPTER 6 The Limits of Expert Knowledge: Why We Don't Know What We Think We Know about Uncertainty 95 The Right Stuff: How a Group of Psychologists Saved Risk Analysis 97 Mental Math: Why We Shouldn't Trust the Numbers in Our Heads 99 ?Catastrophic? Overconfidence 102 The Mind of ?Aces?: Possible Causes and Consequences of Overconfidence 107 Inconsistencies and Artifacts: What Shouldn't Matter Does 111 Answers to Calibration Tests 115 CHAPTER 7 Worse Than Useless: The Most Popular Risk Assessment Method and Why It Doesn't Work 117 A Basic Course in Scoring Methods (Actually, It's the Advanced Course, Too?There's Not Much to Know) 118 Does That Come in ?Medium??: Why Ambiguity Does Not Offset Uncertainty 123 Unintended Effects of Scales: What You Don't Know Can Hurt You 130 Clarification of Scores and Preferences: Different but Similar-Sounding Methods and Similar but Different-Sounding Methods 135 CHAPTER 8 Black Swans, Red Herrings, and Invisible Dragons: Overcoming Conceptual Obstacles to Improved Risk Management 145 Risk and Righteous Indignation: The Belief that Quantitative Risk Analysis Is Impossible 146 A Note about Black Swans 151 Frequentist versus Subjectivist 158 We're Special: The Belief that Risk Analysis Might Work, But Not Here 161 CHAPTER 9 Where Even the Quants Go Wrong: Common and Fundamental Errors in Quantitative Models 167 Introduction to Monte Carlo Concepts 168 Survey of Monte Carlo Users 172 The Risk Paradox 174 The Measurement Inversion 176 Where's the Science? The Lack of Empiricism in Risk Models 178 Financial Models and the Shape of Disaster: Why Normal Isn't so Normal 181 Following Your Inner Cow: The Problem with Correlations 187 ?That's Too Uncertain?: How Modelers Justify Excluding the Biggest Risks 191 Is Monte Carlo Too Complicated? 195 PART THREE HOW TO FIX IT 199 CHAPTER 10 The Language of Uncertain Systems: The First Step Toward Improved Risk Management 201 Getting Your Probabilities Calibrated 203 The Model of Uncertainty: Decomposing Risk with Monte Carlos 208 Decomposing Probabilities: Thinking about Chance the Way You Think about a Budget 212 A Few Modeling Principles 213 Modeling the Mechanism 215 CHAPTER 11 The Outward-Looking Modeler: Adding Empirical Science to Risk 221 Why Your Model Won't Behave 223 Empirical Inputs 224 Introduction to Bayes: One Way to Get around that ?Limited Data for Disasters? Problem 227 Self-Examinations for Modelers Who Care about Quality 233 CHAPTER 12 The Risk Community: Intra- and Extraorganizational Issues of Risk Management 241 Getting Organized 242 Managing the Global Probability Model 244 Incentives for a Calibrated Culture 250 Extraorganizational Issues: Solutions beyond Your Office Building 254 Miscellaneous Topics 256 Final Thoughts on Quantitative Models and Better Decisions 258 Appendix Calibration Tests and Answers 261 Index 273

About the Author

Douglas W. Hubbard is the inventor of Applied Information Economics (AIE). He is an internationally recognized expert in the field of measuring intangibles, risks, and value, especially in IT value, and is a popular speaker at numerous conferences. He has written articles for InformationWeek, CIO Enterprise, and DBMS magazine. His AIE method has been applied to dozens of large Fortune 500 IT investments, military logistics, venture capital, aerospace, and environmental issues. Doug is the author of How to Measure Anything: Finding the Value of Intangibles in Business (Wiley).

Reviews

"?shows how to identify and fix hidden problems in risk management. He uses real world examples to reveal serious problems in common quantitative and qualitiative approaches to risk analysis." (Book News, August 2009)

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