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