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

Primary research

#1485

T1new
Topic
unassigned (set during synthesis)
First seen
2026-08-09 07:16:03
Last seen
2026-08-09 07:16:03

Source raw items (1)

  • Semantic Scholar2026-08-09 07:15:26
    SimuGov: A Simulation Optimization Framework for Generative AI Governance Strategy Design

    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 watermark governance testbed. We design a Representative Shadow Clone mechanism to construct a lightweight surrogate model. This approach significantly reduces the computational overhead of large-scale simulations while preserving behavioral diversity at the population level. Furthermore, we develop an automated strategy optimization system based on multi-objective evolutionary search. By introducing a stability regularization objective, the system generates governance strategies that balance effectiveness with execution robustness. Experimental results on the AI watermark governance testbed show that SimuGov reproduces nonlinear social dynamics such as the ''chilling effect,'' discovers robust governance strategies that outperform examined heuristic and random-search baselines, and reduces computational costs by approximately 91.6%. This work provides a scalable computational pathway for evidence-driven, ex-ante strategy design in bounded Generative AI governance settings. To facilitate reproduction and verification, the complete implementation code of SimuGov has been open-sourced.