@inproceedings{yuan-etal-2026-simulating,
    title = "Simulating Crisis Cognition: A Computational Framework for Hypothesis Generation in Crisis Communication",
    author = "Yuan, Changsen  and
      Zhou, Yanghao  and
      Feng, Chong  and
      Shi, Ge",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.692/",
    pages = "14140--14148",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) have demonstrated remarkable fidelity in simulating social dynamics, yet using them to inform high-stakes crisis policy requires rigorous causal evaluation. We introduce CRISIS COGNITION, a framework rooted in generative Structural Causal Models (SCM) that functions as an in-silico hypothesis generator. By coupling real-world telemetry with 1,813 agents, we conduct a counterfactual simulation to evaluate communication strategies. Unlike prior descriptive work, we employ a Stratified Analysis to strictly control for personality confounders. Our simulations generate a \textbf{computational hypothesis}: within the LLM{'}s generative process, emotional scaffolding serves as a functional prerequisite to unlock valid reasoning paths for high-neuroticism agents. Crucially, we identify a ``Sedative Effect'' in simultaneous interventions, confirming that the sequence of support is as vital as the content. This framework provides a rigorous testbed for evaluating strategies before human-subject trials."
}