@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Simulating Crisis Cognition: A Computational Framework for Hypothesis Generation in Crisis Communication
%A Yuan, Changsen
%A Zhou, Yanghao
%A Feng, Chong
%A Shi, Ge
%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 yuan-etal-2026-simulating
%X 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 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.
%U https://aclanthology.org/2026.findings-acl.692/
%P 14140-14148
Markdown (Informal)
[Simulating Crisis Cognition: A Computational Framework for Hypothesis Generation in Crisis Communication](https://aclanthology.org/2026.findings-acl.692/) (Yuan et al., Findings 2026)
ACL