have probability distributions over their
possible values. It typically predicts the
future values of the variable by computing a mean of the distribution and
a confidence interval based on its standard deviation. For example, in 1962
Everett Rogers studied the adoption
times of the members of a community
in response to a proposed innovation. 5
He found they follow a Normal (Bell)
curve that has a mean and a standard
deviation. A prediction of adoption time
is the mean time bracketed by a confidence interval: for example, 68% of the
adoption times are within one standard
deviation of the mean and 95% are within two standard deviations.
In 1987, researchers Per Bak, Chao
Tang, and Kurt Wiesenfeld published
the results of a simple experiment that
Eventually they cannot keep up and the
whole scene collapses into chaos.
The threshold between order and
chaos seems thin. A small perturbation—such as a slight increase in the
speed of Lucy’s conveyor belt—can either do nothing or it can trigger an avalanche of disorder. The speed of events
within an avalanche overwhelms us,
sweeps away structures that preserve
order, and robs our ability to function.
Quite a number of disasters, natural or
human-made, have an avalanche character—earthquakes, snow cascades,
infrastructure collapse during a hurricane, or building collapse in a terror attack. Disaster-recovery planners would
dearly love to predict the onset of these
events so that people can safely flee and
first responders can restore order with
recovery resources standing in reserve.
Disruptive innovation is also a form
of avalanche. Businesses hope their
new products will “go viral” and sweep
away competitors. Competitors want to
anticipate market avalanches and sidestep them. Leaders and planners would
love to predict when an avalanche might
occur and how extensive it might be.
In recent years complexity theory
has given us a mathematics to deal
with systems where avalanches are pos-
sible. Can this theory make the needed
predictions where classical statistics
cannot? Sadly, complexity theory can-
not do this. The theory is very good at
explaining avalanches after they have
happened, but generally useless for
predicting when they will occur.
In 1984, a group of scientists founded
the Santa Fe Institute to see if they could
apply their knowledge of physics and
mathematics to give a theory of chaotic
behavior that would enable profession-
als and managers to move productively
amid uncertainty. Over the years the best
mathematical minds developed a beau-
tiful, rich theory of complex systems.
Traditional probability theory pro-
vides mathematical tools for dealing
with uncertainty. It assumes the uncer-
tainty arises from random variables that
The Profession of IT
Considering how to best navigate stability and randomness.