用行为理论增强大模型，预测山火疏散决策更准。
From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
- 融合行为理论的LLM框架，优化思维链推理
- 在三个灾后调查数据集上提升20.47%准确率
- 适合应急响应与人因建模研究者使用
疏散决策预测对高效应对山火至关重要，有助于预判交通拥堵、调配资源并减少负面影响。传统统计方法难以捕捉个体间复杂的多样化行为逻辑。本文首次提出FLARE（facilitating LLM for advanced reasoning on wildfire evacuation decision prediction），一种基于大语言模型（LLM）的框架，融合行为理论与模型，优化思维链（CoT）推理，并结合基于记忆的强化学习（RL）模块，实现精准的疏散决策预测与机制理解。该方法解决了现有LLM在疏散行为预测中的局限性，如调查数据有限、与行为理论不符、个体偏好冲突、心理状态隐含复杂及心理-行为映射不可解等问题。在三个灾后调查数据集上的实验表明，相比传统理论驱动的行为模型，平均性能提升20.47%，具备强跨事件泛化能力。完整代码已公开于https://github.com/SusuXu-s-Lab/FLARE。
原文摘要 · Abstract (English)
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation decision prediction, a Large Language Model (LLM)-based framework that integrates behavioral theories and models to streamline the Chain-of-Thought (CoT) reasoning and subsequently integrate with memory-based Reinforcement Learning (RL) module to provide accurate evacuation decision prediction and understanding. Our proposed method addresses the limitations of using existing LLMs for evacuation behavioral predictions, such as limited survey data, mismatching with behavioral theory, conflicting individual preferences, implicit and complex mental states, and intractable mental state-behavior mapping. Experiments on three post-wildfire survey datasets show an average of 20.47% performance improvement over traditional theory-informed behavioral models, with strong cross-event generalizability. Our complete code is publicly available at https://github.com/SusuXu-s-Lab/FLARE
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