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arXiv · 2510.05641

Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent

Ruimeng Hu·Daniel Ralston·Xu Yang·Haosheng Zhou
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Abstract

We study a continuous-time stochastic Stackelberg game in which a leader seeks to accomplish a primary objective while inferring a hidden parameter of a rational follower. The follower solves an entropy-regularized tracking problem and responds to the leader's trajectory with a randomized policy. Anticipating this response, the leader designs informative controls to maximize the estimation efficiency for the follower's latent intent, through maximum likelihood estimation. Unlike prior work on discrete-time or finite-candidate inverse learning, our framework enables continuous parameter inference without prior assumptions and endogenizes the information source through the follower's strategic feedback. We derive semi-explicit solutions, prove well-posedness, and develop recurrent neural network algorithms to approximate the leader's path-dependent control. Numerical experiments demonstrate how the leader balances task performance and information gain, highlighting the practical value of our approach for adversarial strategic inference.

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Ruimeng Hu, Daniel Ralston, Xu Yang, Haosheng Zhou. 2025-10-07. Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent. https://arxiv.org/abs/2510.05641

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