Simulating Economies with AI: Inside the LLM Economist Framework

Economic policy decisions affect billions, but traditional models often fail to capture human complexity. Enter the LLM Economist – an open-source framework that leverages large language models to simulate realistic economies with AI agents. Developed by researchers including Seth Karten, this innovative system enables unprecedented experimentation with tax policies, labor markets, and democratic mechanisms using synthetic populations that mirror real-world demographics.

Beyond Traditional Economic Models

Unlike conventional economic simulations constrained by mathematical abstractions, the LLM Economist creates:

• Diverse agent populations generated from real census data (occupation, age, gender)
• Behavioral complexity with rational, altruistic, and adversarial personality types
• Multi-tiered decision-making through a Stackelberg game structure
• Dynamic policy feedback loops where tax decisions influence labor allocation

"This framework models economic systems as a two-level multi-agent reinforcement learning problem," the developers explain. The Tax Planner (leader) sets policies to maximize social welfare, while Workers (followers) optimize labor based on taxes and personal utility functions – creating a continuous feedback loop.

Technical Architecture & Key Innovations

The framework's architecture supports:

# Project structure core components
LLMEconomist/
├── agents/       # Worker and planner logic
├── models/       # LLM integrations (OpenAI, Gemini, local)
├── utils/        # Tax bracket utilities
├── experiments/  # Reproducible research scripts
└── data/         # Demographic datasets

Key technical innovations include:

1. In-context optimization enabling agents to reason about utility functions
2. Synthetic persona generation creating unique economic agents like "a 55-year-old nurse prioritizing retirement savings"
3. Mechanism design for policy testing across rational, bounded rationality, and democratic voting scenarios
4. Scalable infrastructure supporting 3 to 1,000+ agents with parallel processing

Flexible Deployment Options

The framework supports multiple deployment strategies:

Researchers can run simulations with simple commands:

# Democratic voting scenario with 15 agents
python -m llm_economist.main --scenario democratic --num-agents 15

# Local Llama 3.1 via vLLM
vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000
python -m llm_economist.main --llm meta-llama/Llama-3.1-8B-Instruct --service vllm

Research-Grade Experimentation

The framework includes reproducible experiment scripts covering:
- Tax policy optimization
- Bounded rationality studies
- Democratic voting mechanisms
- LLM model comparisons
- Scalability analysis

"Our test suite validates everything from basic functionality to real API integrations," the documentation notes. Researchers can track experiments using Weights & Biases integration and reproduce paper findings through standardized configurations.

Implications for Policy and AI Research

The LLM Economist represents a paradigm shift in economic modeling. By incorporating nuanced human behaviors through LLMs, it enables:

"Testing policy impacts before real-world implementation with populations that actually reflect societal diversity – not just theoretical constructs"

Potential applications range from optimizing social welfare systems to stress-testing universal basic income models. As generative AI continues evolving, frameworks like this could fundamentally reshape how governments and institutions approach economic policy design – moving from abstract theory to evidence-based simulation.

The project is available under the MIT License, inviting collaboration from economists and AI researchers alike to explore how language agents might help solve society's most complex allocation problems.

Source: LLM Economist GitHub Repository