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_The Failure of Risk Management_ |
The Failure of Risk Management
The Failure of Risk Management (Why It's Broken and How to Fix It) by Douglas W. Hubbard
Mentioned Topics and Abbreviations
- Analytic Hierarchy Process (AHP)
- Multi-Attribute Utility Theory (MAUT)
- Actuarial Science
- Options Theory (OT)
- Modern Portfolio Theory (MPT)
- Probabalistic Risk Analysis (PRA)
- Value at Risk (VaR)
- Loss-Exceedance Curve (LEC)
- Risk Tolerance
- Certain Monetary Equivalent (CME)
- also called Certainty Equivalent
Key Takeaways
Definition of Risk
As it is most commonly understood, risk always implies a negative impact.
For boolean cases, risk can be represented as a vector of probability and loss.
Qualitative Metrics Must Be Avoided
Qualitative risk analysis (i.e. risk matrices, scoring charts) departs from legitimate statistical methodology and has no robust evidence to suggest its efficacy. There is good reason to believe that such methods are deleterious to their intended purpose in contradiction to the common response that they are "better than nothing".
Utility as a Measure of Value
Expected Value (Probability × Magnitude) alone can not predict or inform risky decisions, except for risk-neutral parties, but people and organizations are risk-averse.
Game 1: Which would you pick?
- Option 1: a 100% chance to receive $10,000
- Option 2: a 10% chance to receive $100,000
Most people, being risk-averse, will pick option 1. Suppose the prize of option 1 were lowered until you would pick option 2.
That value is your Certain Monetary Equivalent (CME) for a 10% chance of $100,000.
For risk-neutral parties, expected value = CME
Another factor at play here is that utility is not proportional to monetary value.
Consider these additional choices:
Game 2:
- Option 1: a 100% chance to receive $1,000
- Option 2: a 10% chance to receive $10,000
Game 3:
- Option 1: a 100% chance to receive $100,000
- Option 2: a 10% chance to receive $1,000,000
Assuming that the value of one dollar were linear, all three games should have the same solution, but in reality one's answers will differ.
Expert Opinion Must Be Adjusted
Expert opinion is valuable despite its flaws.
The book details the statistically observable tendency for people to underestimate risk and to be overconfident in their beliefs. It describes the process of "calibration" by which people can be trained to compensate for this bias and make predictions far more accurately.
See estimator-calibration for more.
Experts tend to be good at creating heuristics, but do not apply them consistently in practice.
[!example] Chapter 7 describes a study where individual experts were shown to estimate risk differently for identical cases.
Chapter 13 introduces the Brier Score as a method of evaluating the performance of an estimator, evaluated as the mean squared error of their forecasts.
[!example] p. 198 Models based on expert opinion consistently outperform the same experts.
Luck Looks Like Skill
Chapter 7 p.154
Hubbard describes a study which concluded that, given the number of German pilots and their overall victory/defeat figures, there was a ~30% chance an individual would achieve The Red Baron's record by luck alone.
He later refers to the popular tendency to overvalue competence and undervalue luck in the role of achieving improbable accomplishments as the "Red Baron effect".
How many success stories are simply cases of winning a coin flipping tournament?
Qualitative Labels are Problematic
[!example] p. 170 Experts do not agree on the bounds of terms expressing probability. "Likely" vs. "Very Likely"
[!example] p. 182 risk matrix type bucketing tends to inflate the significance of small risks.
There's Always Enough Data
[!quote] Voltaire Perfect is the enemy of good.
[!quote] Jon Von Neumann The truth is much too complicated to allow anything but approximations.
Hubbard challenges the popular rebuttal that any industry is so niche that data sufficient for quantitative models does not exist.
[!quote] Fallacy of Close Analogy - p.236 ...the belief that unless two things are identical in every way, nothing learned from one can be applied to the other.
Value of Information
- Expected Value of Information (EVI)
- Expected Opportunity Loss (EOL)
\text{EVI} = \text{EOL} - \text{EOL}|I
EOL translates well to continuous probabilities.
Single Point Estimates are Problematic
[!example] p. 232 (pp.) Hubbard describes a case in the oil industry where decent estimating is simplified to the point of serious error (collapsing distributions to a single point for "accounting purposes") leading to the widespread underestimating of Earth's oil reserves.
The case closely mirrors construction estimating.
Critiques
Exsupero Ursus
Hubbard uses exsupero ursus to describe the tendency of his detractors to dismiss quantitative methods as inappropriate for their industry-specific risks.
[!quote] Chapter 9 p.195 Suppose a car buyer had a choice between buying two nearly identical automobiles, Car A and Car B. The only difference is that Car B is $1,000 more expensive, has fifty thousand more miles, and was once driven into a lake. But buyer chooses Car B because Car A doesn't fly. Neither does Car B, of course, but for some reason the buyer believes that Car A should fly and therefore chooses Car B. The buyer is committing the exsupero ursus fallacy.
Based on this strawman it is clear Hubbard believes his detractors are correct that qualitative methods can not capture the entire nuance of risk probability, but that they are failing to acknowledge that their preferred alternatives are not demonstrably more effective at doing so.
The nuance Hubbard dismisses without addressing is the possibility of model improvement. A most competent detractor would be aware of the apparent contradiction but argue that their methods will eventually surpass quantitative methods if they are further developed. Such a position would additionally contextualize Hubbard's observations that detractors become emotional in their defense. To them, Hubbard's methods represent an attractive short-term gain that would exclude a long-term payoff.
Hubbard's dismissal rubs me wrong because it reads exactly as he describes the "at least we're doing something" argument throughout the book and just pages earlier.