On the limits of agency in agent-based models
Authors: name: Ayush Chopra, name: Shashank Kumar, name: Nurullah Giray-Kuru, name: Ramesh Raskar, name: Arnau Quera-Bofarull

Agent-based modeling (ABM) seeks to understand the behavior of complex
systems by simulating a collection of agents that act and interact within an
environment. Their practical utility requires capturing realistic environment
dynamics and adaptive agent behavior while efficiently simulating million-size
populations. Recent advancements in large language models (LLMs) present an
opportunity to enhance ABMs by using LLMs as agents with further potential to
capture adaptive behavior. However, the computational infeasibility of using
LLMs for large populations has hindered their widespread adoption. In this
paper, we introduce AgentTorch -- a framework that scales ABMs to millions of
agents while capturing high-resolution agent behavior using LLMs. We benchmark
the utility of LLMs as ABM agents, exploring the trade-off between simulation
scale and individual agency. Using the COVID-19 pandemic as a case study, we
demonstrate how AgentTorch can simulate 8.4 million agents representing New
York City, capturing the impact of isolation and employment behavior on health
and economic outcomes. We compare the performance of different agent
architectures based on heuristic and LLM agents in predicting disease waves and
unemployment rates. Furthermore, we showcase AgentTorch's capabilities for
retrospective, counterfactual, and prospective analyses, highlighting how
adaptive agent behavior can help overcome the limitations of historical data in
policy design. AgentTorch is an open-source project actively being used for
policy-making and scientific discovery around the world. The framework is
available here: github.com/AgentTorch/AgentTorch.