- Updated: June 16, 2026
- 2 min read
Informing AI Policy Assessment using Large‑Scale Simulation of Interventions
Informing AI Policy Assessment using Large‑Scale Simulation of Interventions
Artificial intelligence (AI) is advancing at an unprecedented pace, bringing both transformative benefits and new societal risks. Policymakers worldwide are grappling with how to prioritize and design effective AI governance strategies. In the recent arXiv paper “Informing AI Policy Assessment using Large‑Scale Simulation of Interventions”, Barnett, Kieslich, Helberger, and Diakopoulos propose a novel methodology that combines participatory policy evaluation, expert cost assessment, and large‑language‑model (LLM)‑driven harm‑mitigation scoring.
The approach uses a genetic‑algorithm‑based simulation to explore a vast solution space of possible policy combinations. By adjusting weightings for implementation cost, stakeholder participation, and projected harm reduction, the model reveals a diverse set of viable policy bundles. These results give decision‑makers concrete starting points for deliberation, helping them allocate resources where they matter most.
Key Contributions
- Integration of participatory inputs with expert cost estimates to create a balanced policy‑evaluation framework.
- Use of LLMs to estimate perceived harm mitigation for each policy scenario.
- Genetic algorithm exploration that surfaces multiple high‑performing policy portfolios, rather than a single “optimal” solution.
The methodology aligns with the growing movement toward participatory AI governance, offering a practical pipeline that can be embedded directly into policy‑development workflows.
For a deeper dive into the technical details, read the full paper on arXiv. Stay updated on our latest research and tools at ubos.tech/blog.
Author: Julia Barnett, Kimon Kieslich, Natali Helberger, Nicholas Diakopoulos
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.