Orateur
Description
We study an inspection game of incomplete information, in which an inspector randomizes the allocation of heterogeneous detectors to identify multiple illegal commodities strategically hidden by an adversary within a system (e.g., drugs smuggled in containers). Detectors vary in their detection accuracies, and illegal commodities differ in their associated damage values. The inspector (resp. adversary) seeks to maximize (resp. minimize) the expected damage value of detected commodities, while facing uncertainty about the opponent’s resources. We analytically characterize the marginal detection probabilities and the expected damage values at each system location in equilibrium. We then design a polynomial-time algorithm to construct Nash equilibria by randomizing each player’s resource allocation to match these marginal quantities, subject to location-specific capacity constraints. Our equilibrium analysis offers inspection and security agencies actionable insights into optimal detector acquisition and the strategic value of adversarial intelligence.
