Eco3S: Complex Socio-Economic System Simulation via Agent-Based Models

2026-07-29

AI Summary

This work addresses the limitations of current large language model–driven agent-based modeling, which struggles to capture dynamically evolving agent–environment interactions, lacks counterfactual reasoning capabilities, and offers insufficient automation for scientific inquiry. To overcome these challenges, the authors propose a high-fidelity simulation framework tailored for socio-economic systems, innovatively integrating co-evolutionary mechanisms between agents and their environment, structural causal model (SCM)–based counterfactual reasoning, and a self-correcting paradigm that closes the loop among simulation, analysis, and optimization. By combining large language models with automated workflows, the framework enables end-to-end modeling. Empirical validation demonstrates its ability to successfully reproduce canonical economic phenomena—such as canal decline, the emergence of governance, and information diffusion—thereby confirming its intervenability, iterability, scalability, and generalizability across diverse scenarios.

Abstract

The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM). However, current LLM-based ABM research faces several key challenges: modeling evolving agent-environment interactions, enabling flexible counterfactual reasoning, and automating simulation workflows for scientific research. In this paper, we propose Eco3S, a socio-economic system simulation framework for economic research and policy analysis that addresses these challenges through three key mechanisms: (1) Co-evolving Environment Design, a bidirectional feedback loop where agents and the environment co-evolve, producing realistic emergent behaviors; (2) Structural Causal Simulation, a structural causal model (SCM)-inspired counterfactual mechanism that allows flexible interventions for diverse causal inference tasks; (3) Simulation-Analysis-Refinement Paradigm, a self-corrective mechanism that iteratively refines experimental designs based on prior simulation results. Experiments on diverse economic scenarios confirm \textit{Eco3S}'s effectiveness in replicating multiple established economic studies (canal decay, origins of governance, and information propagation) and phenomena across domains. Additional results further demonstrate its scalability and generalizability, highlighting the framework's potential for rigorous economic research and policy-making.

Problem

• agent-based modeling
• socio-economic system
• counterfactual reasoning
• agent-environment interaction
• simulation automation

Innovation

• Agent-Based Modeling
• Structural Causal Model
• Co-evolving Environment
• Counterfactual Reasoning
• Simulation Framework