LLM Economist: Large Population Models and Mechanism Design in
  Multi-Agent Generative Simulacra
We present the LLM Economist, a novel framework that uses agent-based
modeling to design and assess economic policies in strategic environments with
hierarchical decision-making. At the lower level, bounded rational worker
agents -- instantiated as persona-conditioned prompts sampled from U.S.
Census-calibrated income and demographic statistics -- choose labor supply to
maximize text-based utility functions learned in-context. At the upper level, a
planner agent employs in-context reinforcement learning to propose
piecewise-linear marginal tax schedules anchored to the current U.S. federal
brackets. This construction endows economic simulacra with three capabilities
requisite for credible fiscal experimentation: (i) optimization of
heterogeneous utilities, (ii) principled generation of large, demographically
realistic agent populations, and (iii) mechanism design -- the ultimate nudging
problem -- expressed entirely in natural language. Experiments with populations
of up to one hundred interacting agents show that the planner converges near
Stackelberg equilibria that improve aggregate social welfare relative to Saez
solutions, while a periodic, persona-level voting procedure furthers these
gains under decentralized governance. These results demonstrate that large
language model-based agents can jointly model, simulate, and govern complex
economic systems, providing a tractable test bed for policy evaluation at the
societal scale to help build better civilizations.
687efe2633947f780d9b4afa
Seth Karten
claimed_verified
2025-07-22T07:48:01.542Z
6658e1c8ce1b2838885b2d7f
/avatars/8623555f14b62f40fd372da20cb59ccc.svg
Seth Karten
user
milkkarten
687efe2633947f780d9b4afb
Wenzhe Li
687efe2633947f780d9b4afc
Zihan Ding
687efe2633947f780d9b4afd
Samuel Kleiner
687efe2633947f780d9b4afe
Yu Bai
687efe2633947f780d9b4aff
Chi Jin
LLM Economist: Large Population Models and Mechanism Design in
  Multi-Agent Generative Simulacra
We present the LLM Economist, a novel framework that uses agent-based
modeling to design and assess economic policies in strategic environments with
hierarchical decision-making. At the lower level, bounded rational worker
agents -- instantiated as persona-conditioned prompts sampled from U.S.
Census-calibrated income and demographic statistics -- choose labor supply to
maximize text-based utility functions learned in-context. At the upper level, a
planner agent employs in-context reinforcement learning to propose
piecewise-linear marginal tax schedules anchored to the current U.S. federal
brackets. This construction endows economic simulacra with three capabilities
requisite for credible fiscal experimentation: (i) optimization of
heterogeneous utilities, (ii) principled generation of large, demographically
realistic agent populations, and (iii) mechanism design -- the ultimate nudging
problem -- expressed entirely in natural language. Experiments with populations
of up to one hundred interacting agents show that the planner converges near
Stackelberg equilibria that improve aggregate social welfare relative to Saez
solutions, while a periodic, persona-level voting procedure furthers these
gains under decentralized governance. These results demonstrate that large
language model-based agents can jointly model, simulate, and govern complex
economic systems, providing a tractable test bed for policy evaluation at the
societal scale to help build better civilizations.
The LLM Economist framework uses agent-based modeling and reinforcement learning to design and assess economic policies, demonstrating improved social welfare through simulations of large, demographically realistic agent populations.
agent-based modeling bounded rational worker agents persona-conditioned prompts text-based utility functions in-context reinforcement learning piecewise-linear marginal tax schedules Stackelberg equilibria mechanism design natural language periodic persona-level voting procedure