On the limits of agency in agent-based models
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.
66ea36e059dee4d0dbb6d7a9
Ayush Chopra
admin_assigned
2024-09-18T08:04:04.491Z
66ce3a5ad3d85430b7b10499
/avatars/3078964ef3df40fe2faa896792ba8aea.svg
Ayush Chopra
user
Godssidekick1
66ea36e059dee4d0dbb6d7aa
Shashank Kumar
66ea36e059dee4d0dbb6d7ab
Nurullah Giray-Kuru
admin_assigned
2024-09-18T08:04:39.945Z
63b7095e6fca9d2a1904ea8f
/avatars/578680cbc1b9592e0dfd6540420758e9.svg
Nurullah Giray Kuru
user
ngkuru
66ea36e059dee4d0dbb6d7ac
Ramesh Raskar
admin_assigned
2024-09-18T08:04:45.435Z
64785a36d946c2a46a57a19e
/avatars/ccf29d77b497204e47de108d99423fe5.svg
Ramesh Raskar
user
rameshraskar
66ea36e059dee4d0dbb6d7ad
Arnau Quera-Bofarull
admin_assigned
2024-09-18T08:04:50.551Z
6536638ed34e9f02b9e920b8
/avatars/d63d19534ce473afab940265e42c78e1.svg
Arnau Quera-Bofarull
user
arnauqb
On the limits of agency in agent-based models
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.
AgentTorch, a framework utilizing large language models as agents, scales agent-based modeling to millions of agents, enabling high-resolution simulations of complex systems like the COVID-19 pandemic.
Agent-based modeling large language models AgentTorch heuristic agents simulation scale individual agency disease waves unemployment rates retrospective analysis counterfactual analysis prospective analysis