Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations

• URL: http://arxiv.org/abs/2410.08345v1
• Date: Thu, 10 Oct 2024 20:04:58 GMT
• Title: Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations
• Authors: Henry Gasztowtt, Benjamin Smith, Vincent Zhu, Qinxun Bai, Edwin Zhang,
• Abstract summary: AI policymaking holds the potential to surpass human performance through the ability to process data quickly at scale. Existing RL-based methods exhibit sample inefficiency, and are further limited by an inability to flexibly incorporate nuanced information into their decision-making processes. We propose a novel method in which we instead utilize pre-trained Large Language Models (LLMs), as sample-efficient policymakers.
• Score: 4.153442346657272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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