Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions
Large language model (LLM) agents offer a generative approach to simulating human behavior under conditions that may have few or no direct historical analogues, a common challenge in disaster and infrastructure-disruption planning. However, this generative capacity creates a validity problem: individually plausible agent reasoning may fail to reproduce empirical population behavior. We evaluate whether empirical grounding improves the statistical realism of LLM-agent simulations during disruptions. Specifically, we develop an empirically grounded LLM-agent framework that embeds demographic profiles from the American Community Survey, baseline routines from the American Time Use Survey, an...
| Authors | Chen Xia, Zexi Kuang, Yuqing Hu |
|---|---|
| Primary category | cs.AI |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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