ZaneMeyers/zmVault
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

vault backup: 2026-05-28 18:30:17

Browse Source
This commit is contained in:
Zane Meyers
2026-05-28 18:30:17 -04:00
parent e42491a238
commit 67d3358c86
47 changed files with 1708 additions and 144 deletions
Download Patch File Download Diff File Expand all files Collapse all files
hubbard_2020_failure.md
+37 -33
View File
@@ -1,16 +1,18 @@
---
title: "The Failure of Risk Management: Why It's Broken and How to Fix It, Second Edition"
title: The Failure of Risk Management
tags:
- authorship/other
- exclude-from-word-count
- topic/risk
- type/media/book
authors: Douglas W. Hubbard
publisher: John Wiley & Sons, Inc.
author: Douglas W. Hubbard
edition: Second
publisher: John Wiley & Sons
subtitle: Why It's Broken and How to Fix It
type: book
year: 2020
---
# The Failure of Risk Management: Why It's Broken and How to Fix It, Second Edition
# The Failure of Risk Management
%%
This note, with the exception of comments like this one
@@ -146,10 +148,10 @@ For commentary see the companion
#### A Note About Black Swans
The *exsupero ursus* fallacy is reinforced by authors of very popular books
The _exsupero ursus_ fallacy is reinforced by authors of very popular books
who seem to depend heavily on some version of the fallacy.
One such author is former Wall Street trader and mathematician Nassim Taleb.
He wrote *The Black Swan*
He wrote _The Black Swan_
and other books critical of common practice in risk management,
especially in (but not limited to) the financial world,
as well as the nonquantitative hubris of Wall Street.
@@ -168,7 +170,7 @@ he riled up one such prizewinner to the point of red-faced, fist-pounding anger.
Taleb bases a lot of his thesis on the fact that the impact of chance
is unappreciated by mostly everyone.
He sees the most significant events in history as being completely unforeseeable.
He calls these events *black swans* in reference to an old European expression
He calls these events _black swans_ in reference to an old European expression
that went something like "That's about as likely as finding a black swan."
The expression was based on the fact that no European
had ever seen a swan that was black---until Europeans traveled to Australia.
@@ -176,7 +178,7 @@ Until the first black swans were sighted, black swans were a metaphor for imposs
Taleb puts September 11, 2001, stock market crashes, major scientific discoveries,
and the rise of Google in his set of black swans.
Each event, he argues, was not only unforeseen
but *utterly unforeseeable* based on our previous experience.
but _utterly unforeseeable_ based on our previous experience.
People will routinely confuse luck with competence
and they will presume that the lack of seeing an unusual event to date
is somehow proof that the event cannot occur.
@@ -197,7 +199,7 @@ and will discuss this further in the next chapter.
I might even include Taleb as one source of inspiration
for identifying new categories of fallacies
(and giving it a Latin name in order to sound official).
Taleb coined a fallacy he refers to as the *ludic fallacy*,
Taleb coined a fallacy he refers to as the _ludic fallacy_,
derived from the Latin word for "games of chance."
Taleb defines the ludic fallacy as the assumption that the real world
necessarily follows the same rules as well-defined games of chance.
@@ -205,7 +207,7 @@ necessarily follows the same rules as well-defined games of chance.
Now, here is where Taleb errs.
He doesn't just argue that risk management is flawed.
He argues that risk management itself is impossible
and that all we can do is make ourselves *antifragile*.
and that all we can do is make ourselves _antifragile_.
I think he is just using a very different definition of risk management---
which even he uses inconsistently.
No matter what he calls it, he is promoting a particular set of (vaguely defined) methods
@@ -221,7 +223,7 @@ He focuses on particular approaches to it, but it is risk management just the sa
Confusion and inconsistency about whether managing fragility is, in practice,
part of managing risks is not the only problem in his thesis.
Taleb commits every form of the *exsupero ursus* fallacy
Taleb commits every form of the _exsupero ursus_ fallacy
throughout most of what he writes.
Specifically,
@@ -232,7 +234,7 @@ when looking for evidence of relative performance, and
(3) he presumes that a given model was even being used
when he identifies them as the culprit in major risk events.
In an interview for *Fortune* Taleb claimed,
In an interview for _Fortune_ Taleb claimed,
"No model is better than a faulty model."
Again, having no model is never an option.
One way or another, a model is being used.
@@ -286,7 +288,7 @@ Yes, the rare events---black swans---
are individually impossible to predict precisely.
But unless he can show that his alternative model (apparently his intuition)
would also have predicted such events exactly,
then he commits *exsupero ursus* when he says imperfection alone
then he commits _exsupero ursus_ when he says imperfection alone
is sufficient to prefer intuition over statistics.
In addition to Kahneman,
@@ -298,20 +300,21 @@ Now, if the objective of card counting was to predict every hand,
even the most extraordinarily rare combinations as Taleb would seem to require,
then Ed Thorp's method certainly fails.
But Ed Thorp's method works---that's why the casinos quit letting him play---
because his system resulted in better bets on average after a large number of hands.
because his system resulted in better bets on average
after a large number of hands.
Taleb is also a fan of the mathematician Benoit Mandelbrot,
who used the mathematics of *fractals* to model financial markets.
who used the mathematics of _fractals_ to model financial markets.
Similar to Thorp and Taleb,
Mandelbrot was equally unable to predict specific extraordinary events exactly,
but his models are preferred by some
because they seem to generate more realistic patterns
that look like they *could* be from real data.
that look like they _could_ be from real data.
If anecdotal evidence were sufficient to compare model performance,
one could simply point out that Taleb's investment firm, Empirica Capital LLC,
closed in 2004 after several years of mediocre returns.[^09-13]
He had one very good year in 2000 (a 60 percent return)
because while everyone else was betting on dot-com, he bet on *dot-bomb*.
because while everyone else was betting on dot-com, he bet on _dot-bomb_.
But the returns the following years were far enough below the market average
that the good times couldn't outweigh the bad for his fund.
@@ -366,19 +369,19 @@ Taleb criticizes the use of historical data in forecasts
but apparently sees no irony in his argument.
He looks at several examples in which history was a poor predictor.
In other words, he is assessing the validity of using historical examples
by using *historical examples*.
by using _historical examples_.
What Taleb and others prove with such examples
is merely that what I will call a *naive* historical analysis can be very misleading.
is merely that what I will call a _naive_ historical analysis can be very misleading.
Taleb demonstrates his point by using the example of a turkey.
The turkey had a great life right up until Thanksgiving.
So, for that turkey, history was a poor indicator.
So how is Taleb able to see this problem?
He simply looks at the larger history of turkeys.
All he is doing is using what we may call a *history of histories*,
or *meta-historical analysis*, to show how wrong naive historical analysis can be.
All he is doing is using what we may call a _history of histories_,
or _meta-historical analysis_, to show how wrong naive historical analysis can be.
The error in historical analysis in a stock price, for example,
is to look only at the history of *that* stock and only for recent history.
is to look only at the history of _that_ stock and only for recent history.
If we look at all historical analysis for a very long period of time,
we find how often naive historical analysis can be wrong.
@@ -392,8 +395,8 @@ It just doesn't happen.
Even Taleb's ludic fallacy seems to be a fallacy itself.
Sam Savage calls it the "ludic fallacy-fallacy."
As Savage describes it, we cannot rationally address real-world problems of uncertainty
"*without* first understanding the simple arithmetic of dice, cards, and spinners."
Of course, Taleb is right when he says we shouldn't *assume*
"_without_ first understanding the simple arithmetic of dice, cards, and spinners."
Of course, Taleb is right when he says we shouldn't _assume_
that we have defined any problem perfectly.
That certainly would be an error, and if that were Taleb's point, that would be valid.
But, again, whether a particular model is perfect is not the right question.
@@ -427,18 +430,19 @@ So, which one would you measure first and how much would you be willing to spend
For years, I've been computing the value of additional information on every uncertain variable in a model.
Suppose we ran ten thousand scenarios in a simulation and determined that 1,500 of these scenarios resulted in a net loss.
If we decide to go ahead with this product development, and we get one of these undesirable scenarios, the amount of money we would lose is the *opportunity loss (OL)*---the cost of making the wrong choice.
If we decide to go ahead with this product development, and we get one of these undesirable scenarios, the amount of money we would lose is the _opportunity loss (OL)_---the cost of making the wrong choice.
If we didn't lose money, then the OL was zero.
We can also have an OL if we decide not to approve the product but then find out we *could* have made money.
We can also have an OL if we decide not to approve the product but then find out we _could_ have made money.
In the case of rejecting the product, the OL is the difference between the lease and the money we made on the widgets if we would have made money---zero if the equipment did not make money (in which case we were right to reject the idea).
The *expected opportunity loss (EOL)* is each possible opportunity loss times the chance of that loss---in other words, the chance of being wrong times the cost of being wrong.
The _expected opportunity loss (EOL)_ is each possible opportunity loss times the chance of that loss---in other words, the chance of being wrong times the cost of being wrong.
In our Monte Carlo simulation, we simply average the OL for all of the scenarios.
For now, let's say that given the current level of uncertainty about this product, you still think the lease is a good idea.
So we average all 1500 scenarios the OL was positive (we lost money) and 8500 scenarios where OL was zero (me made the right choice).
Suppose we find that the EOL is about $600,000.
The EOL is equivalent to another term called the *expected value of perfect information (EVPI)*.
The EOL is equivalent to another term
called the _expected value of perfect information (EVPI)_.
The EVPI is the most you would reasonably be willing to pay if you could eliminate all uncertainty about this decision.
Although it is almost impossible to ever get perfect information and eliminate all uncertainty, this value is useful as an absolute upper bound.
If we can reduce the $600,000 EOL by half with a market survey that would cost $18,000, then the survey is probably a good deal.
@@ -451,14 +455,14 @@ From this, I've seen patterns that still persist every time I add more analysis
The two main findings are:
* Relatively few variables require further measurement---
but there are almost always *some*.
but there are almost always _some_.
* The uncertain variables with the highest EVPI
(highest value for further measurement)
tend to be those that the organization almost never measures,
*and* the variables they *have* been measuring have, on average, the lowest EVPI.
_and_ the variables they _have_ been measuring have, on average, the lowest EVPI.
I call this second finding the *measurement inversion*,
I call this second finding the _measurement inversion_,
and I've seen it in IT portfolios, military logistics, environmental policy,
venture capital, market forecasts, and every other place I've looked.
@@ -486,7 +490,7 @@ I've even seen risks estimated to be 80 percent, 90 percent,
or even 100 percent probable in the next twelve months.
At that level, that is more of a reliable cost of doing business.
Of course, cost control is also important but it's not the same as risk management.
If it is something you routinely *budget* for, it might not be the kind of risk
If it is something you routinely _budget_ for, it might not be the kind of risk
upper management needs to see in a risk assessment.
Also, as an analyst myself as well as a manager of many analysts,
@@ -707,7 +711,7 @@ by insisting the standards move in this direction.
11. Most of the fresh water on Earth is in the polar ice caps.
12. The Academy Awards ("Oscars") began over a century ago.
13. There are fewer than two hundred billionaires in the world.
14. In Excel, ^ means "take to the power of."
14. In Excel, `^` means "take to the power of."
15. The average annual salary of airline captains is over \$150,000.
16. By 1997, Bill Gates was worth more than \$10 billion.
17. Cannons were used in European warfare by the eleventh century.