RAPID: Evacuate or Not? Modeling the Decision Making of Individuals in Impending Disaster Areas

四级飓风正在逼近。一个可能受影响的个人应该遵循官方命令撤离，还是留在原地？由于迫在眉睫的灾害威胁，脆弱地区的数百万人面临这一严重问题。许多人选择离开，而有些人没有。与这些人的多次面谈清楚地表明，他们坚信自己做出了正确的选择。这个快速项目将确定对即将发生的灾害地区的个人决策有重大影响的变量，并将有助于了解不同的个人如何以不同的方式利用这些变量。这些见解将有助于建立新的计算模型，用于在飓风和其他自然灾害等极端情况下的不确定性下的个人决策。重点灾害将是飓风哈维对德克萨斯州海岸和飓风厄玛对佛罗里达和格鲁吉亚的影响。结果可能会加强疏散工作，在当地采取行动，针对那些最有可能忽视官方建议的人。此外，这种建模可能有助于救灾和救援工作更好地协调，并以更高的精度提供更快的救援。这项研究的结果将被整合到PI教授的课程的课堂教学中，这将使学生了解决策科学如何在最极端的情况下对现实世界产生影响。技术方法从描述感兴趣的个人的受影响类别开始。接下来，将收集有关它们的各种数据。特别是，根据各新闻机构的报道，在即将发生的灾难之前和之后对受影响的个人进行的采访，来自灾区的相关社交媒体消息，关于疏散人员及其人口统计的政府数据以及其他调查工具将用于建立一个全面的数据集进行分析。这些数据将被筛选，以推断重要的变量以及它们在个人决策中如何相互作用。这些分析和数据将用于建立基于经验的决策模型，该模型将联合收割机原则性的基于代理的建模与参数化的人类判断和选择模型相结合。这项研究的探索性使得模型评估尤为重要。将根据拟合和定性评估比较各种模型对数据的性能。这项研究计划预计将产生几个受影响的个人的决策过程的验证模型，供政府使用和进一步研究。

A category 4 hurricane is approaching. Should a potentially affected individual follow the official orders and evacuate, or stay in place? Millions of individuals situated in vulnerable areas face this grave question as imminent disaster threatens. Many choose to leave, whereas some do not. Numerous interviews with such persons clearly convey their conviction in having made the right choice. This RAPID project will identify the variables that significantly influence the decision making of individuals in impending disaster areas, and it will contribute to the understanding of how the variables are utilized differently by different individuals. These insights will help to build new computational models of the individual's decision making under uncertainty, in extreme situations such as hurricanes and other natural disasters. The focus disasters will be the impacts of Hurricane Harvey on the Texas coast and Hurricane Irma on Florida and Georgia. Outcomes could augment evacuation efforts with actions on the ground that target those most likely to ignore official recommendations. Furthermore, such modeling will likely help relief-and-rescue efforts to better coordinate and provide faster relief with increased precision. Outcomes from this research will be integrated into the classroom instruction of courses taught by the PIs, which will provide students with exposure to how decision-making science can have real-world impact even under the most extreme circumstances.The technical approach begins with characterizing the affected classes of individuals of interest. Next, various types of data about them will be gathered. In particular, interviews of affected individuals before the impending disaster and after, as reported by various news agencies, relevant social media messages originating from disaster areas, government data on evacuees and their demographics, and other survey instruments will be used to build a comprehensive data set for analysis. These data will be sifted to infer the significant variables and how they interact in individual decision making. The analysis and data will be used to build empirically-informed decision making models, which will combine principled agent-based modeling with parametric human judgment and choice models. The exploratory nature of this research makes model evaluation particularly important. Performance of the various models on the data will be compared based on their fits and qualitative assessments. This research plan is expected to yield validated models of the decision-making processes of several affected individuals for government use and further study.