CAREER: Information Accuracy and the Use of Social Data in Planning for Disaster Response

这个学院早期职业发展（Career）项目奖的重点是一类新的决策模型，能够利用不确定的社会数据的力量进行灾害响应后勤规划。传统上，对规划后勤活动以支持救灾至关重要的信息是通过诸如实地评估等耗时的工作收集的。在紧急情况下使用社交媒体可以在更短的时间内收集更多可能挽救生命的信息。许多应急管理人员表示，他们的机构只有在核实社会数据后才会对其采取行动。这种策略与社交数据的时效性相矛盾；这是它的主要优点之一。本研究的产品将通过量化在各个验证阶段考虑信息的价值，直接解决对社会数据在决策中的有用性的关注。结果将通过模拟游戏转化为第一响应者社区，以提供有和没有社会数据的响应计划的比较示范。通过将游戏和案例研究整合到课程和暑期项目中，并让学生参与研究，新一代的工程师将受到启发，追求人道主义物流领域的职业生涯，并将社会数据概念渗透到该领域。将开发用于实时物流规划的不确定性的新模型，以多种方式促进动态和随机路由。首先，建模随机变量的稳态概率分布的传统假设不成立，因为众包努力不断提供关于不确定社会数据准确性的相对信任程度的新信息。其次，模型将允许对不确定的请求及时采取行动，而不是延迟资源分配，直到完整的需求场景是已知的。将制定抽样方法来解释这些差异。将与第一响应者社区进行研讨会和专家访谈，以选择相关的路由问题变体和信息格式。这些活动还将确定一套对应急管理人员具有实际意义的社会数据后勤战略，这些战略被定义为具体规定应在何种程度上将社会数据纳入应急计划的政策。开发的模型将用于评估基于真实灾难的不同测试实例集的策略性能。这将导致确定社会数据集成可以提高响应效率的场景，并可能通过能够在更短的时间内服务（挽救）更多需求（生命）的方法改变灾难响应。

The focus of this Faculty Early Career Development (CAREER) Program award is a new class of decision models capable of harnessing the power of uncertain social data for disaster response logistics planning. Information critical in planning logistics activities to support disaster response has traditionally been gathered via time-consuming efforts such as on the ground assessments. The use of social media during emergencies enables collecting a larger amount of potentially life saving information in a shorter amount of time. Many emergency managers have indicated their agency would take action on social data only after verifying it. This strategy contradicts the timeliness of social data; one of its primary advantages. The products of this research will directly address concerns over the usefulness of social data in decision making by quantifying the value of considering the information at various stages of verification. Results will be translated to the first responder community via a simulated game to provide a comparative demonstration of response planning with and without social data. New generations of engineers will be inspired to pursue careers in humanitarian logistics and infiltrate the field with social data concepts by integrating games and case studies into courses and summer programs and involving students in the research.Novel models for uncertainty in real-time logistics planning will be developed that contribute to dynamic and stochastic routing in a number of ways. First, traditional assumptions of homeostatic probability distributions for modeled random variables do not hold, as crowdsourcing efforts constantly provide new information regarding the relative degree of belief in the accuracy of uncertain social data. Second, the models will allow timely action on uncertain requests instead of delaying resource allocation until the complete demand scenario is known. Sampling methods to account for these differences will be developed. Seminars and expert interviews with the first responder community will be conducted to select relevant routing problem variants and information formats. These activities will also determine a set of social data logistics strategies of practical interest to emergency managers, defined as policies that specify to what extent social data should be incorporated in a response plan. Developed models will be used to assess strategy performance across a diverse set of test instances based on real disasters. This will result in the identification of scenarios where social data integration can improve response efficacy, potentially transforming disaster response with methods that enable serving (saving) a larger number of needs (lives) in a shorter amount of time.