Abstract

Many high-profile societal problems involve an individual or group repeatedly attacking another – fromchild-parent disputes, sexual violence against women, civil unrest, violent conflicts and acts of terror, tocurrent cyber-attacks on national infrastructure and ultrafast cyber-trades attacking stockholders. There isan urgent need to quantify the likely severity and timing of such future acts, shed light on likely perpetrators,and identify intervention strategies. Here we present a combined analysis of multiple datasets across all thesedomains which account for.100,000 events, and show that a simple mathematical law can benchmark themall. We derive this benchmark and interpret it, using a minimal mechanistic model grounded bystate-of-the-art fieldwork. Our findings provide quantitative predictions concerning future attacks; a tool tohelp detect common perpetrators and abnormal behaviors; insight into the trajectory of a ‘lone wolf’;identification of a critical threshold for spreading a message or idea among perpetrators; an interventionstrategy to erode the most lethal clusters; and more broadly, a quantitative starting point forcross-disciplinary theorizing about human aggression at the individual and group level, in both real andonline worlds.