Did you ever wonder what the world would be like if a
notable person had never been born? Did
you ever realize the futility of that musing and wonder how it is that the work
of one person could shape the whole of humanity in profound ways far beyond
their intentions? Well, today we
celebrate a birthday. No, not mine! And my how I loved turning 50 this past week
and the great celebration that attended the event. No, the birthday in question was 190 years
earlier – April 30, 1777 to be exact.
Like me, he was intrigued with mathematics. Like me, he taught about magnetism and the
universal principles associated therewith.
And like me, he was fascinated by light and the manipulation of light
through lenses to understand its essence.
But unlike me, he thought that, “the world would be nonsense, the whole
creation an absurdity without immortality.”
The birthday we celebrate today was none other than Johann
Carl Friedrich Gauss. And the reason why
I care about his birthday is singular.
He gave humanity one of its most toxic cognitive forms of bondage. And together with his French collaborator (of
sorts) Adrien-Marie Legendre, Gauss is unconsidered at our collective great
peril.
In an effort to predict astronomical movements – principally
the orbits of planets – Gauss developed a computation known as the Gaussian
gravitational constant which is built on a mathematical notion of least squares. This algebraic notion – that observable
phenomenon can lead to forward predictions and thereby minimize measurement
error – was innocent enough for its immediate application. When one is trying to figure out where a
planet is going to be in three days hence, this math had its utility. But, like other astronomical innovations,
Gauss’ work unleashed a toxic divination
impulse that has become the root of our modern scientific inquiry and the bane
of humanity. The notion of prediction
based on linear regression.
Now some of you hate math and, acknowledging that, let me
explain something. As the abject failure of pundits and analysts have shown in the 2016
U.S. Presidential election, if you measure consensus assumptions, your
conclusions are entirely wrong. In 2006
and 2007, I correctly described the conditions and the timing of the Global
Financial Crisis in 2008[1]. Was I forecasting an outcome using predictive
analytics? No. I was merely observing irrefutable documented
behaviour in an occult industry and critiquing the system level convergence
that was certain. From mass pandemics
(the Asian bird flu) to resource shocks to social paroxysm (the Egyptian
multi-coups), the “trained” and the “expert” are left agape when linear
regression behaviour is punctuated by disequilibrium events. Regrettably, education’s obsession with the
scientific method have taught regression but have assiduously ignored its
dominant fallacy – that we know the variables that matter and
we recognize
that which is significant. Elementary
statistics teach us that interrogatory inquiry presupposes:
1.
Known
variables;
2.
Known scale in
which these variables operate;
3.
And
Measurement Error.
Interestingly, the
same discipline teaches us the error of untested assumptions about normalcy,
kurtosis, skewness, and orthogonality.
However, the modern education system and the scientific method upon
which it is built fails to account for these in every instance diminishing the
efficacy of social and technical interventions.
Put another way, in the world of obsessive prediction and outcomes,
we rely on our elementary algebra which seeks to solve for y in the classic
linear predictive model:
y= mx +b
where y is the expected outcome; x is the variable(s) we think have an
association with an observable; m is
the scale or range in which x operates;
and, b is unexplained variance. This formula presumes that we know both the
association between observations and effects (an entirely fallacious
assumption), we understand the scale in which variables operate (an entirely
untested assumption), and that the remainder is “unexplained”. We don’t hold the possibility that the entire
ontology projected in regression may in fact be prima facie false.
Here's the
problem. We are not conditioned to ask
any of the fundamental assumptions that underpin the error of statistical divination. We want to “know” what’s going to
happen. We want to “control” outcomes by
manipulating variables. But what we
constantly ignore is the fact that the human analog experience does not happen
on 2-dimensional scatterplots through which lines can be drawn. Every struggle you’ve had; every emotional
pain; every sense of loss; is based largely on the fact that you projected a
plane in a dimension around which you built a narrative. Often those narratives involved others. But they had their narratives, their
frameworks, their projections. And just
because a dot showed up in your world and a dot showed up in mine doesn’t mean
that the lines that you connect and the lines I connect go in the same
direction. In fact, it’s certain that
they do not. So one day, using the exact
“same” data, we arrive at different conclusions. And then we expend amazing energy trying to
re-narrate what we didn’t understand in the first place.
Gauss gave us a 2-D
god-complex in a multi-dimensional world.
And as long as we subscribe to either of those features (the error of
2-D or the god-complex) we insure nothing but pain for our existence. Our obsession with the “future” is nonsensical. Making up stories and myths about immortality
– a prerequisite for Gauss – implies that the present is insufficient. And with that measurement error, all other
measurements (and the measurers) will be disappointments.
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