  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.
