Can LLM-driven agents mimic heterogeneous human behavior in GABMs?

Determine whether large language models, when used to drive agent decisions in generative agent-based models for epidemic modeling, can properly mimic the behavior of heterogeneous individuals across demographic and personality dimensions, specifically age, race, gender, and personality.

Background

Generative agent-based models (GABMs) replace hand-crafted behavioral rules with decisions produced by LLMs, aiming to better capture human decision-making during epidemics. While this approach can remove many simplifying assumptions, it raises concerns about whether LLMs can faithfully reproduce real human behaviors, especially across diverse populations.

The paper notes that LLM decisions can be biased and may not align with human behavior. A key unresolved issue is whether LLMs can accurately represent heterogeneity related to age, race, gender, and personality, which is crucial for realistic epidemic modeling and policy evaluation.