Asymmetric-Information Resource Allocation Games: An LP Approach to Purposeful Deception

TL;DR

This paper introduces a game-theoretic LP approach to model and analyze purposeful deception in resource allocation, revealing strategies that balance allocation and belief manipulation.

Contribution

It formulates the Deceptive Resource Allocation Game (DRAG) within a Bayesian framework and provides an efficient LP method to find its equilibrium strategies.

Findings

The LP formulation efficiently computes the Perfect Bayesian Nash Equilibrium.

Policies naturally balance resource allocation and belief manipulation.

Numerical results show emergence of purposeful deception behaviors.

Abstract

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.