The advent of artificial intelligence has led to a growing emphasis on data-driven modeling in macroeconomics, with agent-based modeling (ABM) emerging as a prominent bottom-up simulation paradigm. In ABM, agents (e.g., households, firms) interact within a macroeconomic environment, collectively generating market dynamics. Existing agent modeling typically employs predetermined rules or learning-based neural networks for decision-making. However, customizing each agent presents significant challenges, complicating the modeling of agent heterogeneity. Additionally, the influence of multi-period market dynamics and multifaceted macroeconomic factors are often overlooked in decision-making processes. In this work, we introduce EconAgent, a large language model-empowered agent with human-like characteristics for macroeconomic simulation. We first construct a simulation environment that incorporates various market dynamics driven by agents' decisions regarding work and consumption. Through the perception module, we create heterogeneous agents with distinct decision-making mechanisms. Furthermore, we model the impact of macroeconomic trends using a memory module, which allows agents to reflect on past individual experiences and market dynamics. Simulation experiments show that EconAgent can make realistic decisions, leading to more reasonable macroeconomic phenomena compared to existing rule-based or learning-based agents. Our codes are released at https://github.com/tsinghua-fib-lab/ACL24-EconAgent.

翻译：人工智能的兴起使得数据驱动建模在宏观经济学中日益受到重视，基于智能体的建模（ABM）已成为一种重要的自底向上仿真范式。在ABM中，智能体（如家庭、企业）在宏观经济环境中交互，共同产生市场动态。现有智能体建模通常采用预定义规则或基于学习的神经网络进行决策。然而，定制化每个智能体存在显著挑战，使得智能体异质性的建模变得复杂。此外，决策过程往往忽略多期市场动态与多维宏观经济因素的影响。本研究提出EconAgent，一种具备类人特征的大语言模型驱动智能体，用于宏观经济模拟。我们首先构建了一个仿真环境，其中包含由智能体工作与消费决策驱动的多种市场动态。通过感知模块，我们创建了具有差异化决策机制的异质智能体。进一步，我们利用记忆模块建模宏观经济趋势的影响，使智能体能够反思过往个体经验与市场动态。仿真实验表明，相较于现有基于规则或基于学习的智能体，EconAgent能够做出更贴近现实的决策，从而产生更合理的宏观经济现象。代码已发布于 https://github.com/tsinghua-fib-lab/ACL24-EconAgent。