关于基于代理的模型中代理能力的限制

On the limits of agency in agent-based models

September 14, 2024

作者: Ayush Chopra, Shashank Kumar, Nurullah Giray-Kuru, Ramesh Raskar, Arnau Quera-Bofarull

cs.AI

摘要

基于代理的建模（ABM）旨在通过模拟一组在环境中行动和互动的代理来理解复杂系统的行为。它们的实际效用需要捕捉现实环境动态和适应性代理行为，同时有效地模拟百万规模的人口。最近大型语言模型（LLMs）的进展为通过将LLMs作为代理来增强ABMs提供了机会，进一步捕捉适应性行为的潜力。然而，由于在大规模人口中使用LLMs的计算不可行性，阻碍了它们的广泛采用。在本文中，我们介绍AgentTorch——一个能够将ABMs扩展到数百万代理并利用LLMs捕捉高分辨率代理行为的框架。我们评估LLMs作为ABM代理的效用，探讨模拟规模与个体代理之间的权衡。以COVID-19大流行为案例研究，我们展示了AgentTorch如何模拟代表纽约市的840万代理，捕捉隔离和就业行为对健康和经济结果的影响。我们比较基于启发式和LLM代理的不同代理架构在预测疾病波和失业率方面的性能。此外，我们展示了AgentTorch在回顾性、反事实和前瞻性分析方面的能力，突显了适应性代理行为如何帮助克服历史数据在政策设计中的局限性。AgentTorch是一个开源项目，正在全球范围内用于政策制定和科学发现。该框架可在此处获取：github.com/AgentTorch/AgentTorch。

English

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.