mlsanityData to 7 Oct 2026

EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities

arXiv 2310.10436 ↗First posted 16 Oct 2023cs.AIcited by 154

On the map: World models → World models for language agents

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Abstract (arXiv)

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.

Key facts. Cited by 154 papers in the map (data through 7 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 11 of its 53 references that are in the map. First posted on arXiv 16 Oct 2023.

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11 of 53 references.

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  1. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 10

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  8. 2023CGMI: Configurable General Multi-Agent Interaction Frameworkcited by 41
  9. 2023The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agentscited by 24
  10. 2021Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economistcited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 30; 2025: 65; 2026: 53.

154 papers.

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50 most cited of 154 All 154 (JSON)

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