Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909829203755008 |
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| author | Hu, Ruimeng Ralston, Daniel Yang, Xu Zhou, Haosheng |
| author_facet | Hu, Ruimeng Ralston, Daniel Yang, Xu Zhou, Haosheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05641 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent Hu, Ruimeng Ralston, Daniel Yang, Xu Zhou, Haosheng Optimization and Control 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. |
| title | Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.05641 |