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E-raamat: Failure of Risk Management: Why It's Broken and How to Fix It

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  • Ilmumisaeg: 26-Feb-2020
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119522027
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 26-Feb-2020
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119522027
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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. 

About the Author xi
Preface xiii
Acknowledgments xvii
PART ONE AN INTRODUCTION TO THE CRISIS
1(78)
Chapter 1 Healthy Skepticism for Risk Management
3(18)
A "Common Mode Failure"
5(3)
Key Definitions: Risk Management and Some Related Terms
8(6)
What Failure Means
14(3)
Scope and Objectives of This Book
17(4)
Chapter 2 A Summary of the Current State of Risk Management
21(14)
A Short and Entirely-Too-Superficial History of Risk
21(4)
Current State of Risk Management in the Organization
25(1)
Current Risks and How They Are Assessed
26(9)
Chapter 3 How Do We Know What Works?
35(26)
Anecdote: The Risk of Outsourcing Drug Manufacturing
36(4)
Why It's Hard to Know What Works
40(4)
An Assessment of Self-Assessments
44(4)
Potential Objective Evaluations of Risk Management
48(9)
What We May Find
57(4)
Chapter 4 Getting Started: A Simple Straw Man Quantitative Model
61(18)
A Simple One-for-One Substitution
63(1)
The Expert as the Instrument
64(3)
A Quick Overview of "Uncertainty Math"
67(5)
Establishing Risk Tolerance
72(1)
Supporting the Decision: A Return on Mitigation
73(2)
Making the Straw Man Better
75(4)
PART TWO WHY IT'S BROKEN
79(176)
Chapter 5 The "Four Horsemen" of Risk Management: Some (Mostly) Sincere Attempts to Prevent an Apocalypse
81(28)
Actuaries
83(3)
War Quants: How World War II Changed Risk Analysis Forever
86(4)
Economists
90(6)
Management Consulting: How a Power Tie and a Good Pitch Changed Risk Management
96(7)
Comparing the Horsemen
103(2)
Major Risk Management Problems to Be Addressed
105(4)
Chapter 6 An Ivory Tower of Babel: Fixing the Confusion about Risk
109(26)
The Frank Knight Definition
111(3)
Knight's Influence in Finance and Project Management
114(4)
A Construction Engineering Definition
118(1)
Risk as Expected Loss
119(2)
Defining Risk Tolerance
121(7)
Defining Probability
128(3)
Enriching the Lexicon
131(4)
Chapter 7 The Limits of Expert Knowledge: Why We Don't Know What We Think We Know about Uncertainty
135(28)
The Right Stuff: How a Group of Psychologists Might Save Risk Analysis
137(2)
Mental Math: Why We Shouldn't Trust the Numbers in Our Heads
139(3)
"Catastrophic" Overconfidence
142(8)
The Mind of "Aces": Possible Causes and Consequences of Overconfidence
150(5)
Inconsistencies and Artifacts: What Shouldn't Matter Does
155(5)
Answers to Calibration Tests
160(3)
Chapter 8 Worse Than Useless: The Most Popular Risk Assessment Method and Why It Doesn't Work
163(30)
A Few Examples of Scores and Matrices
164(6)
Does That Come in "Medium"?: Why Ambiguity Does Not Offset Uncertainty
170(3)
Unintended Effects of Scales: What You Don't Know Can Hurt You
173(10)
Different but Similar-Sounding Methods and Similar but Different-Sounding Methods
183(10)
Chapter 9 Bears, Swans and Other Obstacles to Improved Risk Management
193(30)
Algorithm Aversion and a Key Fallacy
194(4)
Algorithms versus Experts: Generalizing the Findings
198(5)
A Note about Black Swans
203(6)
Major Mathematical Misconceptions
209(8)
We're Special: The Belief That Risk Analysis Might Work, but Not Here
217(6)
Chapter 10 Where Even the Quants Go Wrong: Common and Fundamental Errors in Quantitative Models
223(32)
A Survey of Analysts Using Monte Carlos
224(4)
The Risk Paradox
228(8)
Financial Models and the Shape of Disaster: Why Normal Isn't So Normal
236(7)
Following Your Inner Cow: The Problem with Correlations
243(5)
The Measurement Inversion
248(2)
Is Monte Carlo Too Complicated?
250(5)
PART THREE HOW TO FIX IT
255(90)
Chapter 11 Starting with What Works
257(36)
Speak the Language
259(7)
Getting Your Probabilities Calibrated
266(6)
Using Data for Initial Benchmarks
272(8)
Checking the Substitution
280(5)
Simple Risk Management
285(8)
Chapter 12 Improving the Model
293(30)
Empirical Inputs
294(11)
Adding Detail to the Model
305(7)
Advanced Methods for Improving Expert's Subjective Estimates
312(3)
Other Monte Carlo Tools
315(2)
Self-Examinations for Modelers
317(6)
Chapter 13 The Risk Community: Intra- and Extra-organizational Issues of Risk Management
323(22)
Getting Organized
324(3)
Managing the Model
327(4)
Incentives for a Calibrated Culture
331(6)
Extraorganizational Issues: Solutions beyond Your Office Building
337(2)
Practical Observations from Trustmark
339(2)
Final Thoughts on Quantitative Models and Better Decisions
341(4)
Additional Calibration Tests and Answers 345(12)
Index 357
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