We've now lived through the same new disaster twice. Computer simulations, more
or less universally adopted as the solution to a major problem, turned out to
have been based on flawed assumptions and faulty data. As a result, policy or
markets became heavily skewed in an inappropriate direction.
Wall Street's risk managers and climate-change scientists both acted as
super-salesmen for a paradigm that turned out to be flawed. After two examples
of the same error have each cost the world a substantial percentage of a year's
gross domestic product (GDP), we'd better figure out how to avoid further
examples of this syndrome.
Alexander Pope put the problem best, in his 1728 Dunciad:
Mathesis alone was unconfined,
Too mad for mere material chains to bind,
Now to pure space lifts her ecstatic stare,
Now, running round the circle, finds it square.
never had access to computer technology, or he would have
realized that proving circles to be square was only the beginning of the
chimerical wonders that could be created with the right software.
As the credit crisis of 2008 recedes into history, the part in it played by
misguided computer models, particularly in the risk management area, is
becoming generally agreed. Rating agencies made assumptions about the
probabilistic independence of different home mortgages that were unfounded. As
a result, many of their AAA ratings proved to be completely spurious,
particularly in the subprime area where the loans' vulnerability to a house
price downturn was especially extreme.
Investment banks managed their risks based on the "value-at-risk" (VaR) risk
management paradigm, which assumed that the distribution of securities' returns
was approximately Gaussian (normally distributed), with a very low probability
of high losses.
The Basel II system of global capital adequacy standards for banks, which came
into effect in 2008, just in time for the crash, was so impressed with these
models that it ruled that any bank using such obviously sophisticated and
superior modeling techniques could calculate risks on its own, without
reference to the crude guidelines deemed appropriate for smaller, less
mathematically attuned houses.
The US Securities and Exchange Commission (SEC) essentially agreed with the
Basel committee; from 2004, it allowed the largest US investment banks to
manage their own leverage, under the theory that no mere regulator could match
the exquisite precision of a modern VaR-based risk management system.
As a result of the near-universal use of VaR-based risk management systems,
David Viniar, chief financial officer of no less august an institution than
Goldman Sachs, was moaning even as early as August 2007 that he was seeing
"25-standard deviation events" several days in a row.
As the lucky chap (because still employed at a senior level by the richest
financial institution in the world) probably now realizes, if he was seeing
25-standard-deviation events, his model was wrong. In a truly Gaussian system,
you would be unlikely to see a 25-standard deviation event during the entire
history of the universe.
It's not as if Wall Street had no warning; mathematical models based on modern
financial theory had caused huge losses as far back as 1987, and had caused the
collapse of Long-Term Capital Management in 1998. Yet the world's best
remunerated people went on using the mathematical models that had caused
moderate-sized disasters before, only to watch them cause a truly impressive
disaster in 2008. It must have been some kind of compulsion.
Turning now to my other example, that of global warming: the possibility that
excess carbon dioxide, through a "greenhouse effect", might cause a global rise
in temperature is based on well-established chemistry and physics. Deniers of
the possibility of global warming are thus being as irrational as the extreme
eco-alarmists; global warming is indeed possible because of physical and
chemical processes that are perfectly well understood, that are indeed are
The difficulty arises in estimating whether it is actually happening. The rise
in temperatures so far observed is well within the level of "noise" in global
temperatures over a period of a century or so, let alone the more extreme
fluctuations that have taken place when the observation period is extended to
millennia. It is thus necessary to match the very limited temperature data we
have, stretching back no more than a century on a worldwide basis, with
secondary observations of such things as tree rings and ice cores, synthesizing
the result with a computer model of what is believed to be the carbon forcing
process in order to predict the range of possible future warming effects.
This is of course a very similar process to that undertaken by Wall Street's
rating agencies and risk managers. Assumptions and simplifications are made,
without which it would be impossible to construct a model. Then the model is
matched up against a few years' observations in real time, being "tweaked" as
real data comes in that does not quite fit with it. By the time this has been
done, careers have been invested in the model, institutions have been built
around its predictions and eminent people have become enthralled by its
results. It thus takes on the appearance of a scientific reality as solid as
The shakiness of the mathematics underlying the global warming "consensus" was
highlighted by the recent "climategate" e-mails and computer tapes involving
researchers at the University of East Anglia. Like Wall Street risk managers,
climate scientists pooh-poohed the obvious flaws in the assumptions underlying
their mathematical models. Like Wall Street bankers, they asserted a consensus
behind those models - in Wall Street's case, to win from regulators a
profitable loosening of their leverage limits; in climate scientists' case, to
persuade politicians to provide them with hugely profitable research
opportunities and capital for their "new energy" start-ups.
Like Wall Street traders, the scientists rejected any modifications of the
models that had served them well and pushed those models to their outer limits;
the Wall Street gang to trade ever more exotic derivatives, the scientists to
justify ever more alarmist predictions of climate change.
The denouement in both cases may also turn out to be similar. In Wall Street's
case, the faulty models have led to losses in the financial system totaling in
excess of US$1 trillion. In the climate scientists' case, the precise degree of
error in their assumptions is not yet apparent. It is only clear that dubious
methods were used to cover up the flaws in their models and observations, and
that the more extreme predictions ("6 degrees Celsius by 2100") were made up
out of whole cloth to justify gargantuan economy-destroying projects of
Should the conference on climate change at present underway in Copenhagen
produce anything beyond alarmist blather, the net cost to the global economy is
likely to exceed by far that of the subprime mortgage fiasco. The difference
between the two cases is that in Wall Street, the first decent-sized downturn
showed the models to be rubbish, although admittedly that took 21 years to
happen after the first demonstration. On the other hand, with climate models we
will have to wait even longer, until 2100, to find out whether they were
completely spurious or merely exaggerated.
Since this has now happened to us twice in the same generation, we had better
assume it is a trend and decide how to combat it. Even today, in some other
sector of economic activity, "scientists" are doubtless creating further
mathematically based predictions with the potential to destroy yet more of our
wealth. We will again be asked to admire the beauty of the output and the
sophistication of the model, while being carefully steered away from the highly
dubious assumptions on which the model is based. Doubters of the model will
again be dismissed as ignorant peasants, too poorly educated to understand the
sophistication of the analysis.
We had better start hiring some mathematically trained skeptics. As a former
mathematician myself, who had even constructed a primitive computer model, I
was able to spot the modeling flaws in an early presentation of the "Club of
Rome" global catastrophe theory of the 1970s. Shortly thereafter I participated
in the other side of the process. For a term paper, I built an econometric
model of the Malaysian economy that contained a hopeless theoretical error (my
one lifetime course in economics, being at Keynesian Cambridge, had not
included any discussion of money supply) but produced a highly plausible and
indeed in the event correct prediction of a decade of non-inflationary growth
The latter example indeed is instructive. I was fairly sure from my reading
that Malaysia would do OK and so constructed an (utterly flawed) model to
"prove" it. Wall Street mathematicians seeking to show that a new derivative
had little risk, or climate scientists seeking to prove that Manhattan would be
under water by 2075, would have been proud of me.
Only those who have themselves built these kinds of models know how misleading
they can be. The rest of the world is "blinded by science" and trustingly
accepts the prognostications of the charlatans at face value. The result is a
more or less infinite potential for loss, except to the designers of the
models, who become rich and famous, the mixture according to taste.
The solution is simple. Every time a group of scientists produces a
mathematical model on which depends some economically serious outcome,
decision-makers must hire a rival team of scientists, promising to reward them
richly should they prove the model to be rubbish. Only by bringing the icy
blast of competition to the overstuffed halls of academia can spurious
computer-generated mathematics be fought off. Otherwise, it threatens our
Martin Hutchinson is the author of Great Conservatives (Academica
Press, 2005) - details can be found at www.greatconservatives.com.
(Republished with permission from PrudentBear.com.
Copyright 2005-09 David W Tice & Associates.)