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：人工智能的发展推动了宏观经济领域对数据驱动建模的日益重视，其中基于智能体的建模（ABM）作为一种重要的自下而上模拟范式脱颖而出。在ABM中，智能体（如家庭、企业）在宏观经济环境中交互，共同生成市场动态。现有智能体建模通常采用预定规则或基于学习的神经网络进行决策。然而，定制每个智能体面临重大挑战，使得智能体异质性的建模变得复杂。此外，多期市场动态和多维宏观经济因素的影响在决策过程中常被忽视。本文提出经济智能体（EconAgent），这是一种具有类人特征的大型语言模型赋能的宏观经济模拟智能体。我们首先构建了一个模拟环境，该环境包含由智能体关于工作和消费的决策驱动的各种市场动态。通过感知模块，我们创建了具有不同决策机制的异质性智能体。此外，我们利用记忆模块对宏观经济趋势的影响进行建模，使智能体能够反思过去的个体经验和市场动态。模拟实验表明，与基于规则或基于学习的现有智能体相比，经济智能体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。我们的代码已发布于https://github.com/tsinghua-fib-lab/ACL24-EconAgent。