Researchers aim to predict the unpredictable

Bryn Nelson Columnist

How do you predict the unpredictable?

Analysts have spent careers trying to anticipate dips and surges in the stock market, and meteorologists have likewise struggled to tame long-range weather patterns. But the imperfect science of foreseeing when terrorists or enemy factions might strike has proven an especially daunting challenge.

Researchers at the University of Arizona in Tucson have now begun work on a set of computer algorithms that may be able to make sense of mountains of intelligence data that would overwhelm human analysts. Known as the Asymmetric Threat Response and Analysis Project, or ATRAP, the effort is aimed at dispassionately sifting through everything from fingerprints to cultural influences to establish useful links and connections.

Jerzy Rozenblit, head of the electrical and computer engineering department at the University of Arizona, said the main goal of ATRAP is to avoid conflicts by identifying and resolving unstable situations before they blow up into bigger problems. Equipped with interlinking computer algorithms that analyze apparent patterns, the program could ultimately predict the courses of action for terrorists, criminal groups, ethnic factions and other destabilizing forces. That information, in turn, would help military commanders devise the best strategies for overcoming enemy combatants.

Rozenblit stressed that the program also could prove invaluable for civilian applications, such as countering identification theft, predicting the course of disease outbreaks and helping communities recovery more quickly in the aftermath of natural disasters such as Hurricane Katrina.

“The first part is to ingest massive amounts of data acquired by intelligence, sensors, satellite data, data from the Internet, images, you name it,” Rozenblit said. “And once we are able to vacuum it up, we need to not just ingest the data, but also digest it.” After that initial cataloguing, the next hurdle is taking the massive body of information “and trying to discern connections among the points to a level at which patterns might appear.”

Recognizing patterns
One example of a bottom-up approach to determining links and patterns has come from Iraq, where GIs patrolling neighborhoods began noticing that as they approached specific intersections or areas, many of the inhabitants covered their ears. “Clearly this was an indication that there was a bomb that was planted, and they (the locals) would learn to cover their ears,” Rozenblit said. The bombers have since changed their tactics, but GIs who recognized the pattern at the time and immediately stopped their vehicle undoubtedly saved numerous soldiers’ lives.

Similarly, most fraudulent transactions yield a series of events that can be linked by data. Identifying a common denominator in the pattern could allow law enforcement officials to zero in on a criminal ring. “Once you know how that game is being played, you can look for indications that it may be happening again,” said Brian Ten Eyck, the university’s ATRAP project manager. And once a pattern is discovered, it could be fed back into the database to refine it and further draw out the “spider web of connections” that might link people, groups or actions.

A third element of the project will be its ability to visualize the connections in a meaningful, user-friendly way. A dynamic bar graph that vacillates between red and green, for instance, could convey the amount of fuel left in a vehicle, the density of forces in a geographic region or the status of an organization’s bank account.

If all goes well, ATRAP’s final product will be a sophisticated prediction of enemy courses of action. Calculating the likelihood of specific actions in advance would enable commanders to proactively counter with actions of their own. An initial $2.2 million for the four-year project has been funded by the Army Battle Command Battle Laboratory at Fort Huachuca, Ariz., which will handle the classified portion of the collaboration.

The system is designed to respond to changing conditions, Rozenblit said, just as IBM’s Deep Blue computer recalculated the playing field after every move by world chess champion Garry Kasparov. Even so, when Deep Blue beat Kasparov in a famous 1997 chess match, the computer had yet to fully master the game. “The number of possible moves is staggering large, but it is finite,” Rozenblit said. Given enough resources, a computer could eventually chart an unbeatable course because every move is bound by the rules of the game.

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