paperAugust 2026UnreviewedOpen access

SimuGov: A Simulation Optimization Framework for Generative AI Governance Strategy Design

Bingxue Zhang, Jinbiao Li, Q. Tang, Feida Zhu

Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2

Abstract

This study addresses a concrete challenge in Generative AI governance through the lens of AI watermarking: how to evaluate and optimize governance strategies before deployment in a bounded socio-technical setting. To this end, we propose SimuGov, a simulation framework for governance strategy optimization. This framework incorporates psychological traits and adversarial environment perception into agent modeling, enabling behaviorally rich simulation of stakeholder responses within the AI waterm

Cite

@inproceedings{zhang2026simugov,
  title = {{SimuGov: A Simulation Optimization Framework for Generative AI Governance Strategy Design}},
  author = {Bingxue Zhang and Jinbiao Li and Q. Tang and Feida Zhu},
  year = {2026},
  month = aug,
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
  doi = {10.1145/3770855.3818903},
  url = {https://www.semanticscholar.org/paper/bc0b9d2566ae15ad56fe3cb6824594d0646d44ce}
}