Can LLMs scale ABMs to millions of agents?
On the limits of agency in agent-based models
September 18, 2024
https://arxiv.org/pdf/2409.10568This paper introduces AgentTorch, a framework for simulating large-scale, complex systems with millions of agents using LLMs to drive agent behavior. The researchers apply this to model COVID-19 spread and economic impact, demonstrating:
- LLMs can capture realistic population-level behaviors: By prompting LLMs with demographic and situational information, the model replicated real-world trends in isolation and employment better than simplified agent models.
- "LLM Archetypes" enable scaling: Instead of querying LLMs for every agent's action, grouping similar agents into archetypes and sampling their behavior allows for simulating millions of agents while retaining behavioral nuances.
- AgentTorch facilitates analysis: Beyond just simulation, the framework enables retrospective (what happened), counterfactual (what-if scenarios), and prospective (policy testing) analysis, crucial for understanding complex systems.
This has significant implications for LLM-based multi-agent systems, showing their potential in modeling real-world phenomena at scale while highlighting the need for efficient architectures like archetypes.