梳理人机协作在灾害决策中的四大模式，揭示提升应急响应效率的关键机制。
Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review
- 基于51篇论文归纳出四类人机协同模式
- AI可增强态势感知与响应效率，但存在可解释性短板
- 适合应急指挥、智能系统设计者参考
在高风险灾害场景中，及时且明智的决策至关重要，却常受不确定性、动态环境和资源有限的影响。本文系统综述了覆盖灾害管理全阶段的人机协作决策模式。基于51篇同行评审研究，识别出四大类别：人机决策支持系统、任务与资源协调、信任与透明度、模拟与训练。其中分析了认知增强智能、多智能体协同、可解释AI及虚拟训练环境等子模式。研究发现，AI能提升态势感知能力、改善响应效率并支持复杂决策，但存在可扩展性、可解释性与系统互操作性的关键局限。最后提出未来研究方向，强调需构建自适应、可信且情境感知的人机系统，以提升灾害韧性与公平恢复效果。
原文摘要 · Abstract (English)
In high-stakes disaster scenarios, timely and informed decision-making is critical yet often challenged by uncertainty, dynamic environments, and limited resources. This paper presents a systematic review of Human-AI collaboration patterns that support decision-making across all disaster management phases. Drawing from 51 peer-reviewed studies, we identify four major categories: Human-AI Decision Support Systems, Task and Resource Coordination, Trust and Transparency, and Simulation and Training. Within these, we analyze sub-patterns such as cognitive-augmented intelligence, multi-agent coordination, explainable AI, and virtual training environments. Our review highlights how AI systems may enhance situational awareness, improves response efficiency, and support complex decision-making, while also surfacing critical limitations in scalability, interpretability, and system interoperability. We conclude by outlining key challenges and future research directions, emphasizing the need for adaptive, trustworthy, and context-aware Human-AI systems to improve disaster resilience and equitable recovery outcomes.
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