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According to Farmer, there are two kinds of complexity: inherent and apparent. Inherent complexity is the "true" complexity of chaotic systems. It leads to dark unpredictability. The other kind of complexity is the flip side of chaos -- apparent complexity obscuring exploitable order.

Farmer draws a square in the air. Going up the square increases apparent complexity; going across the square increases inherent complexity. "Physics normally works down here," Farmer says, pointing to the bottom corner of low complexity for both sorts, home of the easy problems. "Out there," pointing to the opposite upper corner, "it's all hard. But we are now sliding up to here, where it gets interesting -- where the apparent complexity is high, but the true complexity is still low. Up here complex problems have something in them you can predict. And those are exactly the ones we are looking for in the stock market."

With crude computer tools that take advantage of the flip side of chaos, the Prediction Company hopes to knock off the easy problems in financial markets.

"We are using every method we can find," says partner Norman Packard, a former Chaos Cabalist. The idea is to throw proven pattern-finding strategies of any stripe at the data and "keep pounding on them" to optimize the algorithms. Find the merest hint of a pattern, and then exploit the daylights out of it. The mindset here is that of a gambler's: any advantage is an advantage.

Farmer and Packard's motivating faith that chaos possesses a flip side firm enough to bank on is based on their own experience. Nothing overcomes doubts like the tangible money they won from their Las Vegas roulette wheel experiments. It seems dumb not to take advantage of these patterns. As the chronicler of their high-rolling adventure exclaims in the book The Eudaemonic Pie, "Why would anyone play roulette without wearing a computer in his shoe?"

In addition to experience, Farmer and Packard place a lot of faith in the well-respected theories they invented during their years in chaos research. Now they are testing their wildest, most controversial theory yet. They believe, against the unbelief of most economists, that certain regions of otherwise complicated phenomenon can be predicted accurately. Packard calls these areas "pockets of predictability" or "local predictability." In other words, the distribution of unpredictability is not uniform throughout systems. Most of the time, most of a complex system may not be forecastable, but some small part of it may be for short times. In hindsight, Packard believes local predictability is what allowed the Santa Cruz Cabal to make money forecasting the approximate path of a roulette ball.

If there are pockets of predictability, they will surely be buried under a haystack of gross unpredictability. The signal of local predictability can be masked by a swirling mess of noise from a thousand other variables. The Prediction Company's six rocket scientists use a mixture of old and new, hi-tech and low-tech search techniques to scan this combinatorial haystack. Their software examines the mathematically high-dimensional space of financial data and searches for local regions -- any local region -- that might match low-dimensional patterns they can predict. They search the financial cosmos for hints of order, any order.

They do this in real time, or what might be called hyperreal time. Just as the simulated bouncing roulette ball in the shoe-computer comes to rest before the real ball does, the Prediction Company's simulated financial patterns are played out faster than they happen on Wall Street. They reenact a simplified portion of the stock market in a computer. When they detect the beginnings of a wave of unfolding local order, they simulate it faster than real life and then bet on where they think the wave will approximately end.

David Berreby, writing in the March 1993 Discover, puts the search for pockets of predictability in terms of a lovely metaphor: "Looking at market chaos is like looking at a raging white-water river filled with wildly tossing waves and unpredictably swirling eddies. But suddenly, in one part of the river, you spot a familiar swirl of current, and for the next five or ten seconds you know the direction the water will move in that section of the river."

Sure, you can't predict where the water will go a half-mile downstream, but for five seconds -- or five hours on Wall Street -- you can predict the unfolding show. That's all you really need to be useful (or rich). Find any pattern and exploit it. The Prediction Company's algorithms grab a fleeting bit of order and exploit this ephemeral archetype to make money. Farmer and Packard emphasize that while economists are obliged by their profession to unearth the cause of such patterns, gamblers are not bound so. The exact reason why a pattern forms is not important for the Prediction Company's purposes. In inductive models -- the kind the Prediction Company constructs -- the abstracted causes of events are not needed, just as they aren't needed for an outfielder's internalized ballistic notions, or for a dog to catch a tossed stick.

Rather than worry about the dim relationships between causes and effects in these massively swarmy systems crowded with circular causality, Farmer says, "The key question to ask in beating the stock market is, what patterns should you pay attention to?" Which ones disguise order? Learning to recognize order, not causes, is the key.

Before a model is used to bet with, Farmer and Packard test it with backcasting. In backcasting techniques (commonly used by professional futurists) a model is built withholding the most recent data from the human managing the model. Once the system finds order in past data, say from the 1980s, it is fed the record of the last several years. If it can accurately predict the 1993 outcome, based on what it found in the 1980s, then the pattern seeker has won its wings. Farmer: "The system makes twenty models. We run them each through a sieve of diagnostic statistics. Then the six of us will get together to select the one to run live." Each round of model-building may take days on the Company's computers. But once local order is detected, a prediction based on it can be spun in milliseconds.

For the final step -- running it live with bundles of real money in its fists -- one of the Ph.D.'s still has to hit the "enter" button. This act thrusts the algorithm into the big-league world of very fast, mind-boggling big bucks. Cut loose from theory, running on automatic, the fleshed out algorithm can only hear the murmurs of its creators: "Trade, sucker, trade!"

"If we can earn 5 percent better than what the market does, then our investors will make money," Packard says. Packard clarifies that number by explaining that they can predict 55 percent of market moves, that is, 5 percent more than by random guessing, but that when they do guess right their result can be 200 percent better. The fat-cat Wall Street financial backers who invest in the Prediction Company (currently O'Connor & Associates) get exclusive use of the algorithms in exchange for payments according to the performance of the predictions. "We have competitors," Packard states with a smile. "I know of four other companies with the same thing in mind" -- capturing patterns in chaos with nonlinear dynamics and predicting from them. "Two of them are up and going. Some involve friends."

One competitor trading real money is Citibank. Since 1990, British mathematician Andrew Colin has been evolving trading algorithms. His forecasting program randomly generates several hundred hypotheses of which parameters influence currency data, and then tests the hundred against the last five years of data. The most likely influences are sent to a computer neural net which juggles the weight of each influence to better fit the data, rewarding the best combinations in order to produce better guesses. The neural net system keeps feeding the results back in so that the system can hone its guess in a type of learning. When a model fits the past data, it is sent out into the future. In 1992 the Economist said, "After two years of experiments, Dr. Colin reckons his computer can make returns of 25 percent a year on its notional dealing capital....That is several times more than most human traders hope to make." Midland Bank in London has eight rocket scientists working on prediction machinery. In their scheme, computers breed algorithms. However, just as at the Prediction Company, humans evaluate them before "hitting the return button." They were trading real money by late 1993.

A question investors like to ask Farmer is how can he prove you can make money in markets with the advantage of only a small bit of information. As an "existence proof" Farmer points to the people such as George Soros earning millions year after year trading currencies and whatnot on Wall Street. Successful traders, sniffs Farmer "are pooh-poohed by the academics as being extremely lucky -- but the evidence goes the other way." Human traders unconsciously learn how to spot patterns of local predictability streaking through the ocean of random data. The traders make millions of dollars because they detect patterns (which they cannot articulate), then make an internal model (which they are unconscious of), in order to make predictions (which they are rewarded or punished for, sharpening the feedback loop). They have no more idea of what their model or theory is than of how they catch fly balls. They just do. Yet both kinds of models were empirically constructed in the same inductive Ptolemaic way. And that's how the Prediction Company employs computers to build models of high-flying stocks -- from the data up.

Says Farmer, "If we are successful on a broad basis in what we are doing, it will demonstrate that machines are better forecasters than people, and that algorithms are better economists than Milton Friedman. Already, traders are hesitant about this stuff. They feel threatened by it."

The hard part is keeping it simple. Says Farmer, "The more complex the problem is, the simpler the models that you end up having to use. It's easy to fit the data perfectly, but if you do that you invariably end up just fitting to the flukes. The key is to generalize."

Prediction machinery is ultimately theory-making machinery -- devices for generating abstractions and generalizations. Prediction machinery chews on the mess of seemingly random chicken-scratched data produced by complex and living things. If there is a sufficiently large stream of data over time, the device can discern a small bit of pattern. Slowly the technology shapes an internal ad-hoc model of how the data might be produced. The apparatus shuns "overfitting" the pattern on specific data and leans to the fuzzy fit of a somewhat imprecise generalization. Once it has a general fit -- a theory -- it can make a prediction. In fact prediction is the whole point of theories. "Prediction is the most useful, the most tangible and, in many respects, the most important consequence of having a scientific theory," Farmer declares. Manufacturing a theory is a creative act that human minds excel in, although, ironically we have no theory of how we do it. Farmer calls this mysterious general-pattern-finding ability "intuition." It's the exact technology "lucky" Wall Street traders use.

Prediction machinery is found in biology, too. As David Liddle, the director of a hi-tech think tank called Interval, says, "Dogs don't do math," yet dogs can be trained to predictively calculate the path of a Frisbee and catch it precisely. Intelligence and smartness in general is fundamentally prediction machinery. In the same way, all adaptation and evolution are milder and more thinly spread apparatus for anticipation and prediction.

Farmer confessed to a private gathering of business CEOs, "Predicting markets is not my long-term goal. Frankly, I'm the kind of guy who has a hard time opening to the financial page of the Wall Street Journal." For an unrepentant ex-hippie, that's no surprise. Farmer sees himself working for five years on the problem of predicting the stock market, scoring big time, and then moving on to more interesting problems -- such as real artificial life, artificial evolution, and artificial intelligence. Financial forecasting, like roulette, is just another hard problem. "We are interested in this because our dream is to produce prediction machinery that will allow us to predict lots of different things" -- weather, global climate, epidemics -- "anything generating a lot of data we don't understand well."

"Ultimately," says Farmer, "we hope to imbue computers with a crude form of intuition."

By late 1993, Farmer and Company publicly reported success in predicting markets with "computerized intuition" while trading real money. Their agreement with their investors prohibits them from talking about specific performance, as much as Farmer is dying to. He did say, though, that in a few years they should have enough data to prove "by scientific standards" that their trading success is not a statistical fluke: "We really have found statistically significant patterns in financial data. There really are pockets of predictability out there."