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    <abstract>Large Language Models (LLMs) are increasingly adopted in macroeconomic agent-based modeling(ABM). However, existing research focuses on replicating macro-level stylized facts while often neglecting verification of micro-level decision-making. We investigate this gap by comparing LLM agents to human responses from the Survey of Consumer Expectations (SCE) dataset. Our empirical analysis identifies specific limitations: weak trend responsiveness, mode collapse, and a potential data leakage. We propose the Heterogeneous Shock-Response Causal Transmission Framework to tackle these issues. To ensure theoretical consistency, we use LLMs to build a literature-verified causal graph in which macroeconomic shocks influence decisions via generated mediator nodes, while agent profiles serve as edge moderators. Building on this, during inference, we perform a path search to retrieve relevant causal chains and inject them as an explicit Chain-of-Thought(CoT), prioritizing mechanistic logic over statistical pattern matching. To evaluate the effectiveness of our inference approach, we validate it via a two-stage process that combines micro-level dataset testing and macro-level simulation in the EconAgent system. Results from these experiments indicate that our framework improves alignment with human trends and effectively captures behavioral heterogeneity. Overall, this work contributes to the development of reliable and grounded economic simulations.</abstract>
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%0 Conference Proceedings
%T Do LLM Agents Really Mimic Humans? Diagnosing and Aligning Microeconomic Behaviors in Macro-ABMs
%A Liu, Guangya
%A Wang, Cheng
%A Li, Jiangtong
%A Wu, Huafei
%A Jiang, Changjun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-llm
%X Large Language Models (LLMs) are increasingly adopted in macroeconomic agent-based modeling(ABM). However, existing research focuses on replicating macro-level stylized facts while often neglecting verification of micro-level decision-making. We investigate this gap by comparing LLM agents to human responses from the Survey of Consumer Expectations (SCE) dataset. Our empirical analysis identifies specific limitations: weak trend responsiveness, mode collapse, and a potential data leakage. We propose the Heterogeneous Shock-Response Causal Transmission Framework to tackle these issues. To ensure theoretical consistency, we use LLMs to build a literature-verified causal graph in which macroeconomic shocks influence decisions via generated mediator nodes, while agent profiles serve as edge moderators. Building on this, during inference, we perform a path search to retrieve relevant causal chains and inject them as an explicit Chain-of-Thought(CoT), prioritizing mechanistic logic over statistical pattern matching. To evaluate the effectiveness of our inference approach, we validate it via a two-stage process that combines micro-level dataset testing and macro-level simulation in the EconAgent system. Results from these experiments indicate that our framework improves alignment with human trends and effectively captures behavioral heterogeneity. Overall, this work contributes to the development of reliable and grounded economic simulations.
%U https://aclanthology.org/2026.findings-acl.1799/
%P 36102-36122

Markdown (Informal)

[Do LLM Agents Really Mimic Humans? Diagnosing and Aligning Microeconomic Behaviors in Macro-ABMs](https://aclanthology.org/2026.findings-acl.1799/) (Liu et al., Findings 2026)

• Do LLM Agents Really Mimic Humans? Diagnosing and Aligning Microeconomic Behaviors in Macro-ABMs (Liu et al., Findings 2026)

ACL

• Guangya Liu, Cheng Wang, Jiangtong Li, Huafei Wu, and Changjun Jiang. 2026. Do LLM Agents Really Mimic Humans? Diagnosing and Aligning Microeconomic Behaviors in Macro-ABMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 36102–36122, San Diego, California, United States. Association for Computational Linguistics.