The failure of risk management : why it's broken and how to fix it / Douglas W. Hubbard.

By: Hubbard, Douglas W, 1962- [author.]
Language: English Publisher: Hoboken, New Jersey : Wiley, [2020]Edition: Second editionDescription: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119522041; 9781119522027; 9781119521914Subject(s): Risk managementGenre/Form: Electronic books.DDC classification: 658.15/5 LOC classification: HD61Online resources: Full text available at Wiley Online Library Click here to view
Contents:
TABLE OF CONTENTS About the Author xi Preface xiii Acknowledgments xvii Part One An Introduction To The Crisis 1 Chapter 1 Healthy Skepticism for Risk Management 3 A “Common Mode Failure” 5 Key Definitions: Risk Management and Some Related Terms 8 What Failure Means 14 Scope and Objectives of This Book 17 Chapter 2 A Summary of the Current State of Risk Management 21 A Short and Entirely-Too-Superficial History of Risk 21 Current State of Risk Management in the Organization 25 Current Risks and How They are Assessed 26 Chapter 3 How Do We Know What Works? 35 Anecdote: The Risk of Outsourcing Drug Manufacturing 36 Why It’s Hard to Know What Works 40 An Assessment of Self-Assessments 44 Potential Objective Evaluations of Risk Management 48 What We May Find 57 Chapter 4 Getting Started: A Simple Straw Man Quantitative Model 61 A Simple One-for-One Substitution 63 The Expert as the Instrument 64 A Quick Overview of “Uncertainty Math” 67 Establishing Risk Tolerance 72 Supporting the Decision: A Return on Mitigation 73 Making the Straw Man Better 75 Part Two Why It’s Broken 79 Chapter 5 The “Four Horsemen” of Risk Management: Some (Mostly) Sincere Attempts to Prevent an Apocalypse 81 Actuaries 83 War Quants: How World War II Changed Risk Analysis Forever 86 Economists 90 Management Consulting: How a Power Tie and a Good Pitch Changed Risk Management 96 Comparing the Horsemen 103 Major Risk Management Problems to Be Addressed 105 Chapter 6 An Ivory Tower of Babel: Fixing the Confusion about Risk 109 The Frank Knight Definition 111 Knight’s Influence in Finance and Project Management 114 A Construction Engineering Definition 118 Risk as Expected Loss 119 Defining Risk Tolerance 121 Defining Probability 128 Enriching the Lexicon 131 Chapter 7 The Limits of Expert Knowledge: Why We Don’t Know What We Think We Know about Uncertainty 135 The Right Stuff: How a Group of Psychologists Might Save Risk Analysis 137 Mental Math: Why We Shouldn’t Trust the Numbers in Our Heads 139 “Catastrophic” Overconfidence 142 The Mind of “Aces”: Possible Causes and Consequences of Overconfidence 150 Inconsistencies and Artifacts: What Shouldn’t Matter Does 155 Answers to Calibration Tests 160 Chapter 8 Worse Than Useless: The Most Popular Risk Assessment Method and Why It Doesn’t Work 163 A Few Examples of Scores and Matrices 164 Does That Come in “Medium”?: Why Ambiguity Does Not Offset Uncertainty 170 Unintended Effects of Scales: What You Don’t Know Can Hurt You 173 Different but Similar-Sounding Methods and Similar but Different-Sounding Methods 183 Chapter 9 Bears, Swans and Other Obstacles to Improved Risk Management 193 Algorithm Aversion and a Key Fallacy 194 Algorithms versus Experts: Generalizing the Findings 198 A Note about Black Swans 203 Major Mathematical Misconceptions 209 We’re Special: The Belief That Risk Analysis Might Work, but Not Here 217 Chapter 10 Where Even the Quants Go Wrong: Common and Fundamental Errors in Quantitative Models 223 A Survey of Analysts Using Monte Carlos 224 The Risk Paradox 228 Financial Models and the Shape of Disaster: Why Normal Isn’t So Normal 236 Following Your Inner Cow: The Problem with Correlations 243 The Measurement Inversion 248 Is Monte Carlo Too Complicated? 250 Part Three How to Fix It 255 Chapter 11 Starting with What Works 257 Speak the Language 259 Getting Your Probabilities Calibrated 266 Using Data for Initial Benchmarks 272 Checking the Substitution 280 Simple Risk Management 285 Chapter 12 Improving the Model 293 Empirical Inputs 294 Adding Detail to the Model 305 Advanced Methods for Improving Expert’s Subjective Estimates 312 Other Monte Carlo Tools 315 Self-Examinations for Modelers 317 Chapter 13 The Risk Community: Intra- and Extra-organizational Issues of Risk Management 323 Getting Organized 324 Managing the Model 327 Incentives for a Calibrated Culture 331 Extraorganizational Issues: Solutions beyond Your Office Building 337 Practical Observations from Trustmark 339 Final Thoughts on Quantitative Models and Better Decisions 341 Additional Calibration Tests and Answers 345 Index 357
Summary: "The Failure of Risk Management is about a serious problem in the business of risk analysis and how to fix it. Basic analysis methods are unused, or misapplied, in many major corporate and government decisions. This book shows how some of the most popular "risk analysis" methods are no better than astrology -they are not based on anything an actuary or statistician would recognize as sound, quantitative analysis. Businesses, governments, and the public have completely unrealistic perceptions of risk, currently. This book addresses proper risk methodology, to educate decision makers across industries. This new edition will include new examples citing recent events (e.g. hurricanes and data breaches), new statistical methods, and updated data"-- Provided by publisher.Summary: A practical guide to adopting an accurate risk analysis methodology The Failure of Risk Management provides effective solutionstosignificantfaults in current risk analysis methods. Conventional approaches to managing risk lack accurate quantitative analysis methods, yielding strategies that can actually make things worse. Many widely used methods have no systems to measure performance, resulting in inaccurate selection and ineffective application of risk management strategies. These fundamental flaws propagate unrealistic perceptions of risk in business, government, and the general public. This book provides expert examination of essential areas of risk management, including risk assessment and evaluation methods, risk mitigation strategies, common errors in quantitative models, and more. Guidance on topics such as probability modelling and empirical inputs emphasizes the efficacy of appropriate risk methodology in practical applications. Recognized as a leader in the field of risk management, author Douglas W. Hubbard combines science-based analysis with real-world examples to present a detailed investigation of risk management practices. This revised and updated second edition includes updated data sets and checklists, expanded coverage of innovative statistical methods, and new cases of current risk management issues such as data breaches and natural disasters. Identify deficiencies in your current risk management strategy and take appropriate corrective measures Adopt a calibrated approach to risk analysis using up-to-date statistical tools Employ accurate quantitative risk analysis and modelling methods Keep pace with new developments in the rapidly expanding risk analysis industry Risk analysis is a vital component of government policy, public safety, banking and finance, and many other public and private institutions. The Failure of Risk Management: Why It's Broken and How to Fix It is a valuable resource for business leaders, policy makers, managers, consultants, and practitioners across industries. Provided by publisher.
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Includes index.

ABOUT THE AUTHOR
DOUGLAS W. HUBBARD is the inventor of Applied Information Economics (AIE). His methodology has earned him critical praise from Gartner and Forrester Research. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and How to Measure Anything in Cybersecurity Risk. His articles appear in Nature, The American Statistician, The IBM Journal of R&D, InformationWeek and many more. He has over 30 years of experience in management consulting focusing on the application of quantitative methods in decision making

TABLE OF CONTENTS
About the Author xi

Preface xiii

Acknowledgments xvii

Part One An Introduction To The Crisis 1

Chapter 1 Healthy Skepticism for Risk Management 3

A “Common Mode Failure” 5

Key Definitions: Risk Management and Some Related Terms 8

What Failure Means 14

Scope and Objectives of This Book 17

Chapter 2 A Summary of the Current State of Risk Management 21

A Short and Entirely-Too-Superficial History of Risk 21

Current State of Risk Management in the Organization 25

Current Risks and How They are Assessed 26

Chapter 3 How Do We Know What Works? 35

Anecdote: The Risk of Outsourcing Drug Manufacturing 36

Why It’s Hard to Know What Works 40

An Assessment of Self-Assessments 44

Potential Objective Evaluations of Risk Management 48

What We May Find 57

Chapter 4 Getting Started: A Simple Straw Man Quantitative Model 61

A Simple One-for-One Substitution 63

The Expert as the Instrument 64

A Quick Overview of “Uncertainty Math” 67

Establishing Risk Tolerance 72

Supporting the Decision: A Return on Mitigation 73

Making the Straw Man Better 75

Part Two Why It’s Broken 79

Chapter 5 The “Four Horsemen” of Risk Management: Some (Mostly) Sincere Attempts to Prevent an Apocalypse 81

Actuaries 83

War Quants: How World War II Changed Risk Analysis Forever 86

Economists 90

Management Consulting: How a Power Tie and a Good Pitch Changed Risk Management 96

Comparing the Horsemen 103

Major Risk Management Problems to Be Addressed 105

Chapter 6 An Ivory Tower of Babel: Fixing the Confusion about Risk 109

The Frank Knight Definition 111

Knight’s Influence in Finance and Project Management 114

A Construction Engineering Definition 118

Risk as Expected Loss 119

Defining Risk Tolerance 121

Defining Probability 128

Enriching the Lexicon 131

Chapter 7 The Limits of Expert Knowledge: Why We Don’t Know What We Think We Know about Uncertainty 135

The Right Stuff: How a Group of Psychologists Might Save Risk Analysis 137

Mental Math: Why We Shouldn’t Trust the Numbers in Our Heads 139

“Catastrophic” Overconfidence 142

The Mind of “Aces”: Possible Causes and Consequences of Overconfidence 150

Inconsistencies and Artifacts: What Shouldn’t Matter Does 155

Answers to Calibration Tests 160

Chapter 8 Worse Than Useless: The Most Popular Risk Assessment Method and Why It Doesn’t Work 163

A Few Examples of Scores and Matrices 164

Does That Come in “Medium”?: Why Ambiguity Does Not Offset Uncertainty 170

Unintended Effects of Scales: What You Don’t Know Can Hurt You 173

Different but Similar-Sounding Methods and Similar but Different-Sounding Methods 183

Chapter 9 Bears, Swans and Other Obstacles to Improved Risk Management 193

Algorithm Aversion and a Key Fallacy 194

Algorithms versus Experts: Generalizing the Findings 198

A Note about Black Swans 203

Major Mathematical Misconceptions 209

We’re Special: The Belief That Risk Analysis Might Work, but Not Here 217

Chapter 10 Where Even the Quants Go Wrong: Common and Fundamental Errors in Quantitative Models 223

A Survey of Analysts Using Monte Carlos 224

The Risk Paradox 228

Financial Models and the Shape of Disaster: Why Normal Isn’t So Normal 236

Following Your Inner Cow: The Problem with Correlations 243

The Measurement Inversion 248

Is Monte Carlo Too Complicated? 250

Part Three How to Fix It 255

Chapter 11 Starting with What Works 257

Speak the Language 259

Getting Your Probabilities Calibrated 266

Using Data for Initial Benchmarks 272

Checking the Substitution 280

Simple Risk Management 285

Chapter 12 Improving the Model 293

Empirical Inputs 294

Adding Detail to the Model 305

Advanced Methods for Improving Expert’s Subjective Estimates 312

Other Monte Carlo Tools 315

Self-Examinations for Modelers 317

Chapter 13 The Risk Community: Intra- and Extra-organizational Issues of Risk Management 323

Getting Organized 324

Managing the Model 327

Incentives for a Calibrated Culture 331

Extraorganizational Issues: Solutions beyond Your Office Building 337

Practical Observations from Trustmark 339

Final Thoughts on Quantitative Models and Better Decisions 341

Additional Calibration Tests and Answers 345

Index 357

"The Failure of Risk Management is about a serious problem in the business of risk analysis and how to fix it. Basic analysis methods are unused, or misapplied, in many major corporate and government decisions. This book shows how some of the most popular "risk analysis" methods are no better than astrology -they are not based on anything an actuary or statistician would recognize as sound, quantitative analysis. Businesses, governments, and the public have completely unrealistic perceptions of risk, currently. This book addresses proper risk methodology, to educate decision makers across industries. This new edition will include new examples citing recent events (e.g. hurricanes and data breaches), new statistical methods, and updated data"-- Provided by publisher.

A practical guide to adopting an accurate risk analysis methodology

The Failure of Risk Management provides effective solutionstosignificantfaults in current risk analysis methods. Conventional approaches to managing risk lack accurate quantitative analysis methods, yielding strategies that can actually make things worse. Many widely used methods have no systems to measure performance, resulting in inaccurate selection and ineffective application of risk management strategies. These fundamental flaws propagate unrealistic perceptions of risk in business, government, and the general public. This book provides expert examination of essential areas of risk management, including risk assessment and evaluation methods, risk mitigation strategies, common errors in quantitative models, and more. Guidance on topics such as probability modelling and empirical inputs emphasizes the efficacy of appropriate risk methodology in practical applications.

Recognized as a leader in the field of risk management, author Douglas W. Hubbard combines science-based analysis with real-world examples to present a detailed investigation of risk management practices. This revised and updated second edition includes updated data sets and checklists, expanded coverage of innovative statistical methods, and new cases of current risk management issues such as data breaches and natural disasters.

Identify deficiencies in your current risk management strategy and take appropriate corrective measures
Adopt a calibrated approach to risk analysis using up-to-date statistical tools
Employ accurate quantitative risk analysis and modelling methods
Keep pace with new developments in the rapidly expanding risk analysis industry
Risk analysis is a vital component of government policy, public safety, banking and finance, and many other public and private institutions. The Failure of Risk Management: Why It's Broken and How to Fix It is a valuable resource for business leaders, policy makers, managers, consultants, and practitioners across industries. Provided by publisher.

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