Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations

• Main Authors: Gasztowtt, Henry, Smith, Benjamin, Zhu, Vincent, Bai, Qinxun, Zhang, Edwin
• Format: Journal Article
• Language: English
• Published: 10-10-2024
• Subjects: Computer Science - Artificial Intelligence
• DOI: 10.48550/arxiv.2410.08345

The improvement of economic policymaking presents an opportunity for broad
societal benefit, a notion that has inspired research towards AI-driven
policymaking tools. AI policymaking holds the potential to surpass human
performance through the ability to process data quickly at scale. However,
existing RL-based methods exhibit sample inefficiency, and are further limited
by an inability to flexibly incorporate nuanced information into their
decision-making processes. Thus, we propose a novel method in which we instead
utilize pre-trained Large Language Models (LLMs), as sample-efficient
policymakers in socially complex multi-agent reinforcement learning (MARL)
scenarios. We demonstrate significant efficiency gains, outperforming existing
methods across three environments. Our code is available at
https://github.com/hegasz/large-legislative-models.