@conference {1502026,
	title = {Divide to Defend: Collusive Security Games },
	booktitle = {Conference on Decision and Game Theory for Security (GameSec 2016)},
	year = {2016},
	abstract = {Research on security games has focused on settings where the defender must protect against either a single adversary or multiple, independent
adversaries. However, there are a variety of real-world security domains where
adversaries may benefit from colluding in their actions against the defender, e.g.,
wildlife poaching, urban crime and drug trafficking. Given such adversary collusion may be more detrimental for the defender, she has an incentive to break
up collusion by playing off the self-interest of individual adversaries. As we show
in this paper, breaking up such collusion is difficult given bounded rationality of
human adversaries; we therefore investigate algorithms for the defender assuming both rational and boundedly rational adversaries. The contributions of this
paper include (i) collusive security games (COSGs), a model for security games
involving potential collusion among adversaries, (ii) SPECTRE-R, an algorithm
to solve COSGs and break collusion assuming rational adversaries, (iii) observations and analyses of adversary behavior and the underlying factors including bounded rationality, imbalanced- resource-allocation effect, coverage perception, and individualism / collectivism attitudes within COSGs with data from
700 human subjects, (iv) a learned human behavioral model that incorporates
these factors to predict when collusion will occur, (v) SPECTRE-BR, an enhanced
algorithm which optimizes against the learned behavior model to provide demonstrably better performing defender strategies against human subjects compared
to SPECTRE-R.},
	author = {Gholami, Shahrzad and Bryan Wilder and Brown, Matthew and Thomas, Dana and Sintov, Nicole and Tambe, Milind}
}
