Towards Intelligent Emergency Management: A Scenario–Learning–Decision Framework Enabled by Large Language Models

Yi Wang, Chengliang Wang, Xueqing Zhang and Li Zeng ()
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Yi Wang: School of International Business and Management, Sichuan International Studies University, Chongqing 400031, China
Chengliang Wang: School of Big Data and Software, Chongqing University, Chongqing 401331, China
Xueqing Zhang: School of International Business and Management, Sichuan International Studies University, Chongqing 400031, China
Li Zeng: School of International Business and Management, Sichuan International Studies University, Chongqing 400031, China

Mathematics, 2025, vol. 13, issue 21, 1-19

Abstract: To address the governance challenges of “delayed response, fragmented strategies, and cognitive disconnection” in traditional emergency management, this paper proposes an intelligent framework—Scenario–Learning–Decision (SLD)—powered by Large Language Models (LLMs). The framework integrates Multi-Agent Systems (MAS) and prospect theory-based parameter modeling to build an emergency simulation platform featuring scenario perception, human–AI learning, and collective decision-making. Using the 2022 wildfire in City C as a case study, the research verifies the effectiveness of the SLD model in complex emergency contexts and provides theoretical support and practical pathways for developing human-centered intelligent emergency decision-making systems.

Keywords: emergency management; Scenario–Learning–Decision; large language models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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