  While game-theoretic models and algorithms have been developed to combat
illegal activities, such as poaching and over-fishing, in green security
domains, none of the existing work considers the crucial aspect of community
engagement: community members are recruited by law enforcement as informants
and can provide valuable tips, e.g., the location of ongoing illegal
activities, to assist patrols. We fill this gap and (i) introduce a novel
two-stage security game model for community engagement, with a bipartite graph
representing the informant-attacker social network and a level-$\kappa$
response model for attackers inspired by cognitive hierarchy; (ii) provide
complexity results and exact, approximate, and heuristic algorithms for
selecting informants and allocating patrollers against level-$\kappa$
($\kappa<\infty$) attackers; (iii) provide a novel algorithm to find the
optimal defender strategy against level-$\infty$ attackers, which converts the
problem of optimizing a parameterized fixed-point to a bi-level optimization
problem, where the inner level is just a linear program, and the outer level
has only a linear number of variables and a single linear constraint. We also
evaluate the algorithms through extensive experiments.
