The Failure of Risk Management
Why It's Broken and How to Fix It
Published on 15. May 2009
Book
Hardback
304 pages
978-0-470-38795-5 (ISBN)
Description
An essential guide to the calibrated risk analysis approach The Failure of Risk Management takes a close look at misused and misapplied basic analysis methods and shows how some of the most popular "risk management" methods are no better than astrology! Using examples from the 2008 credit crisis, natural disasters, outsourcing to China, engineering disasters, and more, Hubbard reveals critical flaws in risk management methods-and shows how all of these problems can be fixed. The solutions involve combinations of scientifically proven and frequently used methods from nuclear power, exploratory oil, and other areas of business and government. Finally, Hubbard explains how new forms of collaboration across all industries and government can improve risk management in every field.
Douglas W. Hubbard (Glen Ellyn, IL) is the inventor of Applied Information Economics (AIE) and the author of Wiley's How to Measure Anything: Finding the Value of Intangibles in Business (978-0-470-11012-6), the #1 bestseller in business math on Amazon. He has applied innovative risk assessment and risk management methods in government and corporations since 1994.
"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 academics alike"
-Peter Julian, former chief-information officer of the New York Metro Transit Authority. President of 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.
Douglas W. Hubbard (Glen Ellyn, IL) is the inventor of Applied Information Economics (AIE) and the author of Wiley's How to Measure Anything: Finding the Value of Intangibles in Business (978-0-470-11012-6), the #1 bestseller in business math on Amazon. He has applied innovative risk assessment and risk management methods in government and corporations since 1994.
"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 academics alike"
-Peter Julian, former chief-information officer of the New York Metro Transit Authority. President of 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.
Reviews / Votes
"...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)More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Illustrations
Exhibits: 29 B&W, 0 Color
Dimensions
Height: 231 mm
Width: 155 mm
Thickness: 28 mm
Weight
499 gr
ISBN-13
978-0-470-38795-5 (9780470387955)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Douglas W. Hubbard
The Failure of Risk Management: Why It's Broken and How to Fix It
Why It's Broken and How to Fix It
Book
03/2020
2nd Edition
Wiley
€48.50
Shipment within 15-20 days
Additional editions
E-Book
04/2009
Wiley
€32.99
Available for download
Person
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).
Content
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
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