LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra

This Princeton University research introduces the LLM Economist, a novel framework that leverages large language models (LLMs) to simulate and evaluate economic policies, specifically taxation, within multi-agent environments. The framework models an economy as a Stackelberg game, where a planner LLM proposes tax schedules and worker LLMs adjust their labor to maximize their utility functions, which are based on U.S. Census data to ensure realistic demographic representation. Experiments demonstrate that this language-based optimization can approach optimal tax policies and social welfare gains similar to traditional economic models, even reproducing complex political phenomena like democratic voting and majority exploitation. This work positions LLMs as a tractable test bed for designing and understanding the societal impact of various fiscal policies.