253 lines
6.2 KiB
Markdown
253 lines
6.2 KiB
Markdown
---
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id:
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aliases: []
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title: "The Failure of Risk Management: Why It's Broken and How to Fix It, Second Edition"
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tags:
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- authorship/other
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- exclude-from-word-count
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- status/incomplete
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- topic/risk
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- type/media
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authors: Douglas W. Hubbard
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publisher: John Wiley & Sons, Inc.
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type: book
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year: 2020
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---
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# The Failure of Risk Management: Why It's Broken and How to Fix It, Second Edition
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%%
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This note, with the exception of comments like this one
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(reserved for notes on transcription)
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consists only of content from the text.
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For commentary see the companion
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[[the-failure-of-risk-management]].
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%%
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## Part One: An Introduction To The Crisis
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### Chapter 1: Healthy Skepticism For Risk Management
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#### A "Common Mode Failure"
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#### Key Definitions: Risk Management And Some Related Terms
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#### What Failure Means
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#### Scope And Objectives Of This Book
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#### Notes
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### Chapter 2: A Summary Of The Current State Of Risk Management
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#### A Short And Entirely-Too-Superficial History Of Risk
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#### Current State Of Risk Management In The Organization
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#### Current Risks And How They Are Assessed
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#### Notes
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### Chapter 3: How Do We Know What Works?
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#### Anecdote: The Risk Of Outsourcing Drug Manufacturing
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#### Why It's Hard To Know What Works
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#### An Assessment Of Self-Assessments
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#### Potential Objective Evaluations Of Risk Management
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#### What We May Find
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#### Notes
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### Chapter 4: Getting Started: A Simple Straw Man Quantitative Model
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#### A Simple One-For-One Substitution
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#### The Expert As The Instrument
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#### A Quick Overview Of "Uncertainty Math"
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#### Establishing Risk Tolerance
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#### Supporting The Decision: A Return On Mitigation
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#### Making The Straw Man Better
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#### Note
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## Part Two: Why It's Broken
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### Chapter 5: The "Four Horsemen" Of Risk Management: Some (Mostly) Sincere Attempts To Prevent An Apocalypse
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#### Actuaries
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#### War Quants: How World War II Changed Risk Analysis Forever
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#### Economists
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#### Management Consulting: How A Power Tie And A Good Pitch Changed Risk Management
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#### Comparing The Horsemen
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#### Major Risk Management Problems To Be Addressed
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#### Notes
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### Chapter 6: An Ivory Tower Of Babel: Fixing The Confusion About Risk
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#### The Frank Knight Definition
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#### Knight's Influence In Finance And Project Management
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#### A Construction Engineering Definition
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#### Risk As Expected Loss
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#### Defining Risk Tolerance
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#### Defining Probability
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#### Enriching The Lexicon
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#### Notes
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### Chapter 7: The Limits Of Expert Knowledge: Why We Don't Know What We Think We Know About Uncertainty
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#### The Right Stuff: How A Group Of Psychologists Might Save Risk Analysis
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#### Mental Math: Why We Shouldn't Trust The Numbers In Our Heads
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#### "Catastrophic" Overconfidence
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#### The Mind Of "Aces": Possible Causes And Consequences Of Overconfidence
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#### Inconsistencies And Artifacts: What Shouldn't Matter Does
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#### Answers To Calibration Tests
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#### Notes
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### Chapter 8: Worse Than Useless: The Most Popular Risk Assessment Method And Why It Doesn't Work
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#### A Few Examples Of Scores And Matrices
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#### Does That Come In "Medium"?: Why Ambiguity Does Not Offset Uncertainty
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#### Unintended Effects Of Scales: What You Don't Know Can Hurt You
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#### Different But Similar-Sounding Methods And Similar But Different-Sounding Methods
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#### Notes
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### Chapter 9: Bears, Swans And Other Obstacles To Improved Risk Management
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#### Algorithm Aversion And A Key Fallacy
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#### Algorithms Versus Experts: Generalizing The Findings
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#### A Note About Black Swans
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#### Major Mathematical Misconceptions
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#### We're Special: The Belief That Risk Analysis Might Work, But Not Here
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#### Notes
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### Chapter 10: Where Even The Quants Go Wrong: Common And Fundamental Errors In Quantitative Models
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#### A Survey Of Analysts Using Monte Carlos
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#### The Risk Paradox
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#### Financial Models And The Shape Of Disaster: Why Normal Isn't So Normal
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#### Following Your Inner Cow: The Problem With Correlations
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#### The Measurement Inversion
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#### Is Monte Carlo Too Complicated?
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#### Notes
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## Part Three: How To Fix It
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### Chapter 11: Starting With What Works
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#### Speak The Language
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#### Getting Your Probabilities Calibrated
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#### Using Data For Initial Benchmarks
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#### Checking The Substitution
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#### Simple Risk Management
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#### Notes
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### Chapter 12: Improving The Model
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#### Empirical Inputs
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#### Adding Detail To The Model
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#### Advanced Methods For Improving Expert's Subjective Estimates
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#### Other Monte Carlo Tools
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#### Self-Examinations For Modelers
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#### Notes
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### Chapter 13: The Risk Community: Intra- And Extra-Organizational Issues Of Risk Management
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#### Getting Organized
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#### Managing The Model
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#### Incentives For A Calibrated Culture
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#### Extraorganizational Issues: Solutions Beyond Your Office Building
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##### Growing the Profession
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Of all the professions in risk management,
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that of the actuary is the only one
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that is actually a legally recognized profession.
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Becoming an actuary requires a demonstration of proficiency
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through several standardized tests.
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It also means adopting a code of professional ethics
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enforced by some licensing body.
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When actuaries sign their name
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to the Statement of Actuarial Opinion of an insurance company,
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they put their license on the line.
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As with doctors and lawyers,
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if they lose their license, they cannot just get another job next door.
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The industry of modelers of uncertainties outside of insurance
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could benefit greatly from this level of professional standards.
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Standards organizations,
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government affiliated and otherwise,
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have always been a key part of what makes a profession a profession.
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But standards organizations such as PMI, NIST, and others
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are all guilty of explicitly promoting
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the ineffectual methods previously debunked.
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The scoring methods developed by these institutions
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should be disposed of altogether.
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These organizations should stay out of the business
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of designing risk analysis methods
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until they begin to involve people with quantitative decision analysis backgrounds
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in their standards-development process.
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Professionals should take charge of the direction their profession evolves
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by insisting the standards move in this direction.
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#### Practical Observations From Trustmark
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#### Final Thoughts On Quantitative Models And Better Decisions
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#### Notes
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## Appendix: Additional Calibration Tests And Answers
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