{"ID":2859188,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05641","arxiv_id":"2510.05641","title":"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.","short_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 resp...","url_abs":"https://arxiv.org/abs/2510.05641","url_pdf":"https://arxiv.org/pdf/2510.05641v1","authors":"[\"Ruimeng Hu\",\"Daniel Ralston\",\"Xu Yang\",\"Haosheng Zhou\"]","published":"2025-10-07T07:41:27Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}