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Register a new userGreen Security Game with Community Engagement
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Added by
Taoan Huang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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In this paper, we employ a hypergame framework to analyze the single-leader-multiple-followers (SLMF) Stackelberg security game with two typical misinformed situations: misperception and deception. We provide a stability criterion with the help of hyper Nash equilibrium (HNE) to analyze both strategic stability and cognitive stability of equilibria in SLMF games with misinformation. To this end, we find mild stable conditions such that the equilibria with misperception and deception can derive HNE. Moreover, we analyze the robustness of the equilibria to reveal whether the players have the ability to keep their profits.
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