mlsanityData to 14 Oct 2026

Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations

arXiv 2410.08345 ↗First posted 10 Oct 2024cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 10 Oct 2024.

Builds on

15 of 28 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  5. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  6. 2023A Comprehensive Overview of Large Language Modelscited by 648
  7. 2019Meta-Learning with Implicit Gradientscited by 525
  8. 2023Reward Design with Language Modelscited by 261
  9. 2021Multi-Task Reinforcement Learning with Context-based Representationscited by 174
  10. 2018Meta-Gradient Reinforcement Learningcited by 159
  11. 2020Learning to Incentivize Other Learning Agentscited by 57
  12. 2021A learning agent that acquires social norms from public sanctions in decentralized multi-agent settingscited by 24
3 more
  1. 2021Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learningcited by 19
  2. 2022Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learningcited by 12
  3. 2024Social Environment Designcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra
  2. 2025A Multi-LLM-Agent-Based Framework for Economic and Public Policy Analysis
  3. 2026Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting
  4. 2025Analyzing and Internalizing Complex Policy Documents for LLM Agents
  5. 2024Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games
  6. 2025Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making
  7. 2026PAWS: Policy-driven Agentic World Simulation
  8. 2025Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs