LIFE IMITATES BASEBALL:

There are prediction techniques of all sorts in development. Some are designed to predict the spread of disease; some to determine fluctuations in the popularity of tourist destinations. Some are geared towards specific problems, like predicting the results of an election or determining the amount of electricity that a town will use during the winter. Other tools are more generic, and are flexible enough to be used by, say, both retailers looking to predict popular products and global policy analysts trying to envision future conflict.

Meanwhile, as our ability to mine vast amounts of information improves, the effort to invent the next generation of prediction tools has been fueled by an explosion of personal data, which offer the tantalizing prospect of much more fine-grained predictions through the analysis of details about people's lives.

"We're finally in a position where people volunteer information about their specific activities, often their location, who they're with, what they're doing, how they're feeling about what they're doing, what they're talking about," said Johan Bollen, a professor at the School of Informatics and Computing at Indiana University Bloomington who developed a way to predict the ups and downs of the stock market based on Twitter activity. "We've never had data like that before, at least not at that level of granularity." Bollen added: "Right now it's a gold rush."

One thing the latest prediction techniques still aren't necessarily good at, however, is the thing we want most: telling us exactly what we're in for in the year 2012 and beyond. That's because, by and large, the models used to make predictions tend to be very specialized, or proprietary, or simply untested. But as researchers continue to refine them, that may begin to change -- and this, in turn, promises to raise new questions about just how much we want to know about what lies ahead.

If you want to know what's going to happen next, it might seem natural to ask an expert -- but when it comes to accurate predictions, it turns out that one thing you should stay away from is expert opinion. That was the conclusion reached by University of Pennsylvania psychologist Philip Tetlock, who over the course of 20 years tracked the predictions of 284 experts who had made careers of "commenting or offering advice on political and economic trends." His findings were startling: The academics, analysts, and journalists in his sample weren't significantly better at predicting events in their fields than nonexperts, and most of them would have been beaten by a "dart-throwing chimpanzee.''

Tetlock's findings, which he collected in a 2005 book called "Expert Political Judgment: How Good Is It? How Can We Know?", were disturbing because they seemed to imply that prediction was impossible. But Tetlock's study did not cause him to give up on forecasting entirely -- it just convinced him that individual experts were never going to be very good at it. There could be other ways to predict the future, he believed -- ones that relied on formulas instead of opinions, and which could be tested, tweaked, and improved rather than merely trusted.

The basic idea behind this kind of prediction is the same one that propels all of science: You create a hypothesis based on your understanding of whatever you're trying to study, test it to see if it fits with reality, and then make adjustments if it doesn't. Science essentially offers predictions: how high a ball will bounce if you drop if off the table; what happens if you mix two chemicals together. This kind of certainty has long been elusive in the fuzzier realms of politics and culture, but an increasing amount of data about how we live -- and an ever-improving ability to process it -- has changed the ways we can apply that basic insight. Criminologists are crunching vast amounts of crime data to predict where in a given city murders and robberies are likely to take place. Terrorism researchers mine data on attacks for patterns, and turn it into clues about where future attacks are likely to take place.

The new year will bring plenty of splashy stories about iPads and IPOs. There is a more important theme gathering around us: How analytics harvested from massive databases will begin to inform our day-to-day business decisions. Call it Big Data, analytics, or decision science. Over time, this will change your world more than the iPad 3.

Computer systems are now becoming powerful enough, and subtle enough, to help us reduce human biases from our decision-making. And this is a key: They can do it in real-time. Inevitably, that "objective observer" will be a kind of organic, evolving database.

These systems can now chew through billions of bits of data, analyze them via self-learning algorithms, and package the insights for immediate use. Neither we nor the computers are perfect, but in tandem, we might neutralize our biased, intuitive failings when we price a car, prescribe a medicine, or deploy a sales force. This is playing "Moneyball" at life.

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