Calculated Terror: Modeling Hezbollah
By Aaron Mannes
Foreign Policy has just published an article I co-wrote with my colleague VS Subrahmanian discussing our work modeling the behavior of terrorist organizations. This piece focuses on our results on Hezbollah, which provide a credible explanation to Hezbollah's decision to keep the Israeli-Lebanese border quiet since the end of the 2006 war.
For more on our work, visit the website of my employer the Laboratory for Computational Cultural Dynamics. The specific system discussed here is the Stochastic Opponent Modeling Agents.
Foreign Policy
Argument
Calculated Terror
How a computer model predicts the future in some of the world's most volatile hotspots.
BY AARON MANNES, V.S. SUBRAHMANIAN | DECEMBER 15, 2009
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?
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.
Read the complete post here.
