New predictive algorithms provide insights into worldwide terrorist attacks

A team of researchers has used machine learning to predict and explain terrorist attacks. Their tests suggest the models can accurately predict attacks in regions that are already affected by terrorism. However, they found that “black swan events,” which occur sporadically, are almost impossible to predict. The researchers, led by Dr Andre Python from Zhejiang University in China, used publicly available data to analyze the location and data of attacks that occurred between 2002 and 2016 in 13 regions of the world. “We define terrorism as politically motivated attacks outside legitimate warfare (i.e., targeting noncombatants) perpetrated by non-state actors to communicate to a wider audience,” the group…

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