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