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The Failure of Risk Management. Why It's Broken and How to Fix It

  • ID: 1031316
  • Book
  • May 2009
  • Region: Global
  • 304 Pages
  • John Wiley and Sons Ltd
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The Failure of Risk Management explains which risk analysis methods work, which don't, and how to tell the difference.
The Failure of Risk Management discusses topics relevant to the management of any risk including: Financial Risks, Natural Disasters, Industrial Accidents, Product Safety, Technology Risks, Project Failures, Engineering Disasters, Pandemic Viruses, Computer Security, Fraud, Loss of Reputation, Litigation

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"Doug Hubbard, a recognized expert among experts in the field of risk management, covers the entire spectrum of risk management in this invaluable guide. There are specific value–added take aways in each chapter that are sure to enrich all readers including IT, business management, students, and academic alike."
Peter Julian, former chief information officer of the New York Metro Transit Authority, President of The Alliance Group Consulting

"In his, trademark style, Doug asks the tough questions on risk management. A must–read not only for analysts, but also for the executive who is making critical business decisions."
Jim Franklin, VP Enterprise Performance Management and General Manager, Crystal Ball Global Business Unit, Oracle Corporation

"Doug Hubbard's book should be required reading for managers and practitioners responsible for mitigating risk. If corporations and government are to regain the public trust, effective and broad–based risk management must be as natural as breathing."
Ron Miller, FEMA CIO 2001–2002; former senior advisor, White House Homeland Security Transition Planning Office; Chairman, TeamRonMiller.com

"A seminal and timely book. The Failure of Risk Management challenges conventional wisdom and provides priceless support to decision makers navigating their company in these turbulent times."
Dr. John F.A. Spangenberg, CEO, SeaQuation (an ING spin–off)

"Doug Hubbard really knows his stuff. He is not just an author who has learned enough about a current popular topic to write a book, but instead is a talented consultant in this area, who has learned how to write, and write well. He displays a deep real–world understanding that ranges from mathematics to everyday human behavior, a trait that is all too rare in this age of specialization."
Prof. Sam Savage, Fellow, Judge Business School, Cambridge University; Consulting Professor, Stanford University School of Engineering
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Preface xi

Acknowledgments xv


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


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


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

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Douglas W. Hubbard
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