Paper ID: 2410.08345

Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations

Henry Gasztowtt, Benjamin Smith, Vincent Zhu, Qinxun Bai, Edwin Zhang

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 this https URL

Submitted: Oct 10, 2024