An Israeli tourist feigns surrender in front of a metallic cut-out of a soldier at the Golan Heights outpost of Mount Bental.
On Oct. 27, a Katyusha rocket was fired from Lebanon and struck down in an open area outside the northern Israeli town of Kiryat Shmone. This was the ninth such rocket strike since the end of the 2006 war between Israel and Hezbollah. No group claimed responsibility for the attack, but smaller Palestinian groups hoping to spark another round of fighting are the most likely suspect. Hezbollah, despite its extreme anti-Israel politics, did not join the fight, even after Israeli counterstrikes.
The "Blue Line" separating Israel from Lebanon is one of the most volatile borders in the world. But predicting when this area, and other tense regions throughout the world, will erupt into violence often appears to be little more than guesswork. How can policymakers overcome their own biases and limited information to anticipate if an incident like the recent rocket strike on Israel will spark a larger conflict, like the 2006 war, or fizzle out?
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Increasingly, the answer is: Develop a computer model from historical data. The University of Maryland's Laboratory for Computational Cultural Dynamics (LCCD) constructed one such model that predicted this period of quiet along the Israeli-Lebanese border, and also provides insight into Hezbollah's priorities. LCCD developed a framework, known as Stochastic Opponent Modeling Agents (SOMA), that examines historical data and automatically generates rules assessing the probability that a group will take certain actions under certain conditions.
SOMA examines historical data about the group's behavior and tries to find conditions such that, when the condition is true, the group takes a given action with high probability and, when the condition is false, the group takes the action with very low probability. A human analyst could make these connections when there are relatively few variables being tracked. But when there are dozens of variables there are millions of such possible rules -- far more than an analyst can process. This is often the case in the interconnected world of Middle East politics, where events are shaped by the actions of many actors working in a diverse array of countries.
SOMA rules have also been extracted on the behavior of other Middle Eastern groups. Hamas, for example, is twice as likely to commit kidnappings during periods of conflict with other Palestinian organizations (the probability increases from approximately 33 percent to 67 percent). If another round of Fatah-Hamas fighting erupts in the West Bank, this may present a new challenge for Israeli security. While the rules had not been extracted in 2006, it is worth noting that the Israeli soldier Galid Shalit was kidnapped as the conflict between Hamas and Fatah expanded after the 2006 Palestinian elections.
SOMA is not specifically designed to model the behavior or Hezbollah or even of terrorist organizations -- it has also examined the behaviors of various actors in the Afghan drug trade under different circumstances. This model was built on a hypothetical situation, not systematically gathered data, but demonstrates the way in which SOMA can be applied to a broad range of conflicts and scenarios. The analysis showed that frequently used strategies such as burning poppy fields and destroying drug labs in Afghanistan are unlikely to lead to a long-term decline in the Afghan drug trade.
Models require data, and limitations of that data can limit the accuracy of a system such as SOMA. For the analysis of Hezbollah (and several other groups) SOMA used the Minorities at Risk Organization Behavior (MAROB) data set created at the University of Maryland's Center for International Development and Conflict Management. MAROB identifies factors that motivate members of ethnic minorities to form activist organizations and move from conventional politics to terrorism. MAROB has systematically collected information on more than 150 variables from over 100 organizations across the Middle East during the last several decades. Hezbollah is one of the organizations profiled; the data collected covers Hezbollah from its 1982 founding through 2004.
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