Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent
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.
Cite
@article{arxiv.2510.05641,
title = {Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent},
author = {Ruimeng Hu and Daniel Ralston and Xu Yang and Haosheng Zhou},
journal= {arXiv preprint arXiv:2510.05641},
year = {2025}
}