How Technology Can Spot a War Before It Starts

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March 5 2014 8:47 AM

Symptoms of Violence

How technology can help spot civil unrest and even war before they start.

Mexican president Enrique Pena Nieto in November 2011 in Washington, DC.
Mexican president Enrique Peña Nieto in November 2011 in Washington, DC.

Photo by Mandel Ngan/AFP/Getty Images

This essay is excerpted from The Naked Future, by Patrick Tucker, published by Current.

The date is June 30, 2012. Computer scientist Naren Ramakrishnan is in his Virginia Tech lab watching a map of the Americas on his computer screen. A band of hundreds of red dots hovers over Mexico City; another band is over the Brazil–Paraguay border. The dot cluster is ringed by concentric circles of yellow, green, and blue. It looks almost like a radiant heat map, as though the capital of Mexico and the Brazilian border town of Foz do Iguaçu are on fire, but they aren’t—at least, not yet. These dots represent geotagged tweets containing the terms país,” “trabajador,” “trabaj,” “president,” and “protest.” The controversial Enrique Peña Nieto is about to be officially elected the president of Mexico, and the geotagged tweets represent a march taking form to protest his election.

In 2012 Nieto represented the return to power of the Partido Revolucionario Institucional (PRI). Despite the insurgent‑sounding moniker, the PRI is very much the old‑power party in Mexico, having governed the country for 71 years until 2000. It has long been associated with chronic corruption and even collusion with drug cartels. Nieto, a young, handsome, not conspicuously bright former governor of the state of México, is seen by many as something of a figurehead for a murky, well‑funded machine.

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Having met him, I can attest that he can be very charming, smiles easily, and has a firm handshake. As a governor, he is best known for allowing a particularly brutal army assault on protesters in the city of San Salvador Atenco. The June 30 red‑dot cluster over Mexico indicates a lit fuse around the topic of Nieto on Twitter.

At 11:15 p.m. on July 1, as soon as the election is called for the PRI, the student movement group Yo Soy 132 (I Am 132) will spring into action, challenging the results and accusing the PRI of fraud and voter suppression. The next month will be marked by massive protests, marches, clashes with police, and arrests. This is the future that these red dots on Ramakrishnan’s monitor foretell.

The cluster in Brazil relates to a sudden rise in the use of “país” (“country”), “protest,” “empres” (businesspeople), ciudad” (“city”), and “gobiern” (“govern”). In a few days 2,500 people will close the Friendship Bridge connecting the Brazilian city of Foz de Iguaçu to the Paraguayan Ciudad del Este, another episode in the impeachment drama of Paraguayan President Fernando Lugo.

As soon as clusters appear on Ramakrishnan’s computer, the system automatically sends an alert to government analysis with the Intelligence Advanced Research Projects Activity (IARPA), which is funding Ramakrishnan through a program called Open Source Indicators (OSI). The program seeks to use available public data to model potential future events before they happen. Ramakrishnan and his team are one of several candidates competing for IARPA funds for further development. The different teams are evaluated monthly on the basis of what their predictions were, how much lead time the prediction provided, confidence in the prediction, and other factors.

The OSI program is a descendent to the intelligence practice of analyzing “chatter,” a method of surveillance that first emerged during the Cold War. U.S. intelligence agents would listen in on the Soviet military communication network for clues about impending actions or troop movements. Most of this overheard talk was unremarkable, but when the amount of chatter between missile silo personnel and military headquarters increased, this indicated that a big military exercise was about to get underway. This analysis was a purely human endeavor and a fairly straightforward one, with one enemy, one network to watch, and one set of events to watch out for.

In the post‑9/11 world, where—we are told—potential enemies are everywhere and threats are too numerous to mention, the IARPA considers any event related to “population‑level changes in communication, consumption, and movement” worthy of predicting. That could include a commodity‑price explosion, a civil war, a disease outbreak, the election of a fringe candidate to an allied nation’s parliament—anything that could impact U.S. interests, security, or both. The hope is that if such events can be seen advance, their potential effects can be calculated, different responses can be simulated, and decision-makers can then select the best action.

What this means is that amount of potentially useful data has grown to encompass a far greater number of signals. For U.S. intelligence personnel, Facebook, Twitter, and other social networks now serve the role that chatter served during the Cold War. But as Ramakrishnan admits, Facebook probably is not where the next major national security threat is going to pop up. So intelligence actively monitors about 20,000 blogs, RSS feeds, and other sources of information in the same way newsroom reporters constantly watch Associated Press bulletins and listen to police scanners to catch late‑breaking developments.

In looking for potential geopolitical hot spots, researchers also watch out for many of the broken‑window signals that play a role in neighborhood predictive policing, but on a global scale. The number of cars in hospital parking lots in a major city can suggest an emerging health crisis, as can a sudden jump in school absences. Even brush clearing or road building can predict an event of geopolitical consequence.

Spend enough time on Google Maps and you can even spot a war in the making.

Between January and April 2011, a group of Harvard researchers with the George Clooney–funded Satellite Sentinel Project (SSP) used publicly available satellite images to effectively predict that the Sudanese Armed Forces (SAF) were going to stage a military invasion of the disputed area of Abyei within the coming months. The giveaway wasn’t tank or troop buildup on the border. Sudan began building wider, less flood‑prone roads toward the target, the kind you would use to transport big equipment such as oil tankers. But there was no oil near where the SAF was working. “These roads indicated the intent to deploy armored units and other heavy vehicles south towards Abyei during the rainy season,” SSP researchers wrote in their final report on the incident. True to their prediction, the SAF began burning border villages in March and initiated a formal invasion on May 19 of that year.

Correctly forecasting a military invasion in Africa used to be the sort of thing only a superpower could do; now it’s a semester project for Harvard students.

Much of this data is hiding in plain sight, in reports already written and filed. In 2012 a group of British researchers applied a statistical model to the diaries and dispatches of soldiers in Afghanistan, obtained through the WikiLeaks project. They created a formula to predict future violence levels based on how troops described their firefights in their diaries. The model correctly (though retroactively) predicted an uptick in violence in 2010.

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Simple news reports when observed on a massive scale can reveal information that isn’t explicit in any single news item. As I originally wrote for the Futurist magazine, a researcher named Kalev Leetaru was able to retroactively pinpoint the location of Osama Bin Laden within a 124-mile radius of Abbottabad, Pakistan, where the terrorist leader was eventually found. He found that almost half of the news reports mentioning Bin Laden included the words “Islamabad” and “Peshawar,” two key cities in northern Pakistan. While only one news report mentioned Abbottabad (in the context of a terrorist player who had been captured there), Abbottabad is located easily within 124 miles of the two key cities. In a separate attempt to predict geopolitical events from news reports, Leetaru also used a 30‑year archive of global news put through a learning algorithm to detect “tone” in the news stories (the number of negatively charged words versus positively charged words) along 1,500 dimensions and ran the model on Nautilus, a large shared‑memory supercomputer capable of running more than 8.2 trillion floating-point operations per second. Leetaru’s model also retroactively predicted the social uprisings in Egypt, Tunisia, and Libya.

News reports, tweets, and media tone are correlated with violence. Predicting the actual cause of violence is more difficult. Yet researchers are making progress here as well. In Libya, Tunisia, and Egypt, the price of food, as measured by the food price index of the Food and Agriculture Organization of the United Nations, clearly plays a critical role in civil unrest, according to a 2011 paper by Marco Lagi, Karla Z. Bertrand and Yaneer Bar-Yam of the New England Complex Systems Institute. In 2008 an advance in this index of more than 60 base points easily preceded a number of low intensity “food riots.” Prices collapsed and then bounced back just before the 2011 Arab Spring events in Tunisia, Libya, and Egypt. Today, that same rapid inflation in food prices is playing a key role in uprisings in Ukraine and especially Venezuela, where inflation was 56 percent last year.

If you’re a humanitarian nongovernmental organization, knowing where and when civil unrest is going to strike can help you position relief resources and staff in advance. If you’re a company, you can pull your business interests out of a country where the shit’s about to hit the fan. But to law enforcement, predicting the time and place of an event of significance is less important than knowing who will be involved.

Unlike predicting an invasion, piecing together a model of what a particular individual will do involves a lot more variables. Not only is it more challenging technically, it’s also more costly. Researchers can’t just run lab experiments on who will or won’t commit a crime, so research has to take place in the real world. But experimentation here runs up against privacy concerns. In recent years researchers have found a clever way around these thorny issues by looking toward captive audiences, individuals in situations who have effectively relinquished any expectation of privacy.

Reprinted from The Naked Future: What Happens in a World That Anticipates Your Every Move by Patrick Tucker with permission of Current, a member of Penguin Group (USA) LLC. Copyright (c) Patrick Tucker, 2014.

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