Asymmetric-Information Resource Allocation Games: An LP Approach to Purposeful Deception
This work provides a novel game-theoretic framework and efficient solution for studying purposeful deception in resource allocation, relevant to security and adversarial settings.
The paper introduces the Deceptive Resource Allocation Game (DRAG) to model purposeful deception in a Bayesian game, and solves for the Perfect Bayesian Nash Equilibrium using an efficient linear programming formulation. Numerical results show that the policies naturally balance resource allocation and belief manipulation, leading to emergent deceptive behaviors.
In this work, we introduce the Deceptive Resource Allocation Game (DRAG), which studies purposeful deception within a Bayesian game framework. In DRAG, a Defender allocates resources across the true asset and several decoys to influence an Attacker's beliefs and actions, with the goal of diverting the Attacker away from the true asset. We seek to characterize purposeful deception, whereby the Defender deceives only when doing so improves its performance. To this end, we solve for the Perfect Bayesian Nash Equilibrium (PBNE) of the corresponding game. We show that, despite the coupled belief-policy interdependence, the problem admits an efficient, non-iterative linear programming formulation. Numerical results demonstrate that the resulting policies naturally balance effective allocation and belief manipulation, giving rise to purposeful and emergent deceptive behaviors.