Tell me about the future," I plead.
I'm sitting on a sofa in the guru's office. I've trekked to this high
mountain outpost at one of the planet's power points, the national
research labs at Los Alamos, New Mexico. The office of the guru is
decorated in colorful posters of past hi-tech conferences that trace his
almost mythical career: from a maverick physics student who formed an
underground band of hippie hackers to break the bank at Las Vegas with a
wearable computer, to a principal character in a renegade band of
scientists who invented the accelerating science of chaos by studying a
dripping faucet, to a founding father of the artificial life movement,
to current head of a small lab investigating the new science of
complexity in an office kitty-corner to the museum of atomic weapons at
The guru, Doyne Farmer, looks like Ichabod Crane in a bolo tie. Tall,
bony, looking thirty-something, Doyne (pronounced Doan) was embarking on
his next remarkable adventure. He was starting a company to beat the
odds on Wall Street by predicting stock prices with computer
"I've been thinking about the future, and I have one question," I
"You want to know if IBM is gonna be up or down!" Farmer suggests with a
"No. I want to know why the future is so hard to predict."
"Oh, that's simple."
I was asking about predicting because a prediction is a form of control.
It is a type of control particularly suited to distributed systems. By
anticipating the future, a vivisystem can shift its stance to preadapt
to it, and in this way control its destiny. John Holland says,
"Anticipation is what complex adaptive systems do."
Farmer likes to use a favorite example when explaining the anatomy of a
prediction. "Here catch this!" he says tossing you a ball. You grab it.
"You know how you caught that?" he asks. "By prediction."
Farmer contends you have a model in your head of how baseballs fly. You
could predict the trajectory of a high-fly using Newton's classic
equation of f=ma, but your brain doesn't stock up on elementary physics
equations. Rather, it builds a model directly from experiential data. A
baseball player watches a thousand baseballs come off a bat, and a
thousand times lifts his gloved hand, and a thousand times adjusts his
guess with his mitt. Without knowing how, his brain gradually compiles a
model of where the ball lands -- a model almost as good as f=ma, but not as
generalized. It's based entirely on a series of hand-eye data from past
catches. In the field of logic such a process is known as induction, in
contradistinction to the deduction process that leads to f=ma.
In the early days of astronomy before the advent of Newton's f=ma,
planetary events were predicted on Ptolemy's model of nested circular
orbits -- wheels within wheels. Because the central premise upon which
Ptolemy's theory was founded (that all heavenly bodies orbited the
Earth) was wrong, his model needed mending every time new astronomical
observations delivered more exact data for a planet's motions. But
wheels-within-wheels was a model amazingly robust to amendments. Each
time better data arrived, another layer of wheels inside wheels inside
wheels was added to adjust the model. For all its serious faults, this
baroque simulation worked and "learned." Ptolemy's simple-minded scheme
served well enough to regulate the calendar and make practical celestial
predictions for 1400 years!
An outfielder's empirically based "theory" of missiles is reminiscent of
the latter stages of Ptolemic epicyclic models. If we parsed an
outfielder's "theory" we would find it to be incoherent, ad-hoc,
convoluted, and approximate. But it would also be evolvable. It's a
rat's-nest of a theory, but it works and improves. If humans had to wait
until each of our minds figured out f=ma (and half of f=ma is worse than
nothing), no one would ever catch anything. Even knowing the equation
now doesn't help. "You can do the flying baseball problem with f=ma, but
you can't do it in the outfield in real-time," says Farmer.
"Now catch this!" Farmer says as he releases an inflated balloon. It
ricochets around the room in a wild, drunken zoom. No one ever catches
it. It's a classic illustration of chaos -- a system with sensitive
dependence on initial conditions. Imperceptible changes in the launch
can amplify into enormous changes in flight direction. Although the f=ma
law still holds sway over the balloon, other forces such as propulsion
and airlift push and pull, generate an unpredictable trajectory. In its
chaotic dance, the careening balloon mirrors the unpredictable waltz of
sunspot cycles, Ice Age's temperatures, epidemics, the flow of water
down a tube, and, more to the point, the flux of the stock market.
But is the balloon really unpredictable? If you tried to solve the
equations for the balloon's crazy flitter, its path would be nonlinear,
therefore almost unsolvable, and therefore unforeseeable. Yet, a
teenager reared on Nintendo could learn how to catch the balloon. Not
infallibly, but better than chance. After a couple dozen tries, the
teenage brain begins to mold a theory -- an intuition, an induction -- based
on the data. After a thousand balloon takeoffs, his brain has modeled
some aspect of the rubber's flight. It cannot predict precisely where
the balloon will land, but it detects a direction the missile favors,
say, to the rear of the launch or following a certain pattern of loops.
Perhaps over time, the balloon-catcher hits 10 percent more than chance
would dictate. For balloon catching, what more do you need? In some
games, one doesn't require much information to make a prediction that is
useful. While running from lions, or investing in stocks, the tiniest
edge over raw luck is significant.
Almost by definition, vivisystems -- lions, stock markets, evolutionary
populations, intelligences -- are unpredictable. Their messy, recursive
field of causality, of every part being both cause and effect, makes it
difficult for any part of the system to make routine linear
extrapolations into the future. But the whole system can serve as a
distributed apparatus to make approximate guesses about the future.
Farmer was into extracting the dynamics of financial markets so that he
could crack the stock market. "The nice thing about markets is that you
don't really have to predict very much to do an awful lot," says Farmer.
Plotted on the gray, end-pages of a newspaper, the graphed journey of
the stock market as it rises and falls has just two dimensions: time and
price. For as long as there has been a stock market, investors have
scrutinized that wavering two-dimensional black line in the hopes of
discerning some pattern that might predict its course. Even the vaguest,
if reliable, hint in direction would lead to a pot of gold. Pricey
financial newsletters promoting this or that method for forecasting the
chart's future are a perennial fixture in the stock market world.
Practitioners are known as chartists.
In the 1970s and 1980s chartists had modest success in predicting
currency markets because, one theory says, the strong role of central
banks and treasuries in currency markets constrained the variables so
that they could be described in relatively simple linear equations. (In
a linear equation, a solution can be expressed in a graph as a straight
line.) As more and more chartists exploited the easy linear equations
and successfully spotted trends, the market became less profitable.
Naturally, forecasters began to look at the wild and woolly places where
only chaotic nonlinear equations ruled. In nonlinear systems, the
outcome is not proportional to the input. Most complexity in the
world -- including all markets -- are nonlinear.
With the advent of cheap, industrial-strength computers, forecasters
have been able to understand certain aspects of nonlinearity. Money, big
money, is made by extracting reliable patterns out of the nonlinearity
behind the two-dimensional plot of financial prices. Forecasters can
extrapolate the graph's future and then bet on the prediction. On Wall
Street the computer nerds who decipher these and other esoteric methods
are called "rocket scientists." These geeks in suits, working in the
basements of trading companies, are the hackers of the '90s. Doyne
Farmer, former mathematical physicist, and colleagues from his earlier
mathematical adventures, set up in a small, four-room house which serves
as an office in adobe -- baked Santa Fe -- as far from Wall Street as one can
get in America -- are currently some of Wall Street's hottest rocket
In reality, the two-dimensional chart of stocks does not hinge on
several factors but on thousands of them. The stock's thousands of
vectors are whited-out when plotted as a line, leaving only its price
visible. The same goes for charts of sunspot activity and seasonal
temperature. You can plot, say, solar activity as a simple thin line
over time, but the factors responsible for that level are
mind-bogglingly complicated, multiple, intertwined, and recursive.
Behind the facade of a two-dimensional line seethes a chaotic mixture of
forces driving the line. A true graph of a stock, sunspot, or climate
would include an axis for every influence, and would become an
unpicturable thousand-armed monster.
Mathematicians struggle with ways to tame these monsters, which they
call "high dimensional" systems. Any living creature, complex robot,
ecosystem, or autonomous world is a high-dimensional system. The Library
of form is the architecture of a high-dimensional system. A mere 100
variables create a humongous swarm of possibilities. Because each
behavior impinges upon the 99 others, it is impossible to examine one
parameter without examining the whole interacting swarm at once. Even a
simple three-variable model of weather, say, touches back upon itself in
strange loops, breeding chaos, and making any kind of linear prediction
unlikely. (The failure to predict weather led to the discovery of chaos
theory in the first place.)