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Applications of artificial intelligence for wildlife protection have focusedon learning models of poacher behavior based on historical patterns. However,poachers' behaviors are described not only by their historical preferences, butalso their reaction to ranger patrols. Past work applying machine learning andgame theory to combat poaching have hypothesized that ranger patrols deterpoachers, but have been unable to find evidence to identify how or even ifdeterrence occurs. Here for the first time, we demonstrate a measurabledeterrence effect on real-world poaching data. We show that increased patrolsin one region deter poaching in the next timestep, but poachers then move toneighboring regions. Our findings offer guidance on how adversaries should bemodeled in realistic game-theoretic settings.

Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers