title: On the limits of agency in agent-based models
summary: 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.
paper_authors._id: 66ea36e059dee4d0dbb6d7a9
paper_authors.name: Ayush Chopra
paper_authors.status: admin_assigned
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paper_authors._id: 66ce3a5ad3d85430b7b10499
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paper_authors.fullname: Ayush Chopra
paper_authors.type: user
paper_authors.user: Godssidekick1
paper_authors._id: 66ea36e059dee4d0dbb6d7aa
paper_authors.name: Shashank Kumar
paper_authors._id: 66ea36e059dee4d0dbb6d7ab
paper_authors.name: Nurullah Giray-Kuru
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:04:39.945Z
paper_authors._id: 63b7095e6fca9d2a1904ea8f
paper_authors.avatarUrl: /avatars/578680cbc1b9592e0dfd6540420758e9.svg
paper_authors.fullname: Nurullah Giray Kuru
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paper_authors.user: ngkuru
paper_authors._id: 66ea36e059dee4d0dbb6d7ac
paper_authors.name: Ramesh Raskar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:04:45.435Z
paper_authors._id: 64785a36d946c2a46a57a19e
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paper_authors.fullname: Ramesh Raskar
paper_authors.type: user
paper_authors.user: rameshraskar
paper_authors._id: 66ea36e059dee4d0dbb6d7ad
paper_authors.name: Arnau Quera-Bofarull
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:04:50.551Z
paper_authors._id: 6536638ed34e9f02b9e920b8
paper_authors.avatarUrl: /avatars/d63d19534ce473afab940265e42c78e1.svg
paper_authors.fullname: Arnau Quera-Bofarull
paper_authors.type: user
paper_authors.user: arnauqb
paper_title: On the limits of agency in agent-based models
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) AgentTorch adaptive agent behavior heuristic agents simulation scale individual agency COVID-19 pandemic disease waves unemployment rates retrospective analysis counterfactual analysis prospective analysis open-source project