The Failure of Risk Management

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).

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