CAREER: RecovUS - An Agent Based Model of Collective Post Disaster Housing Recovery

这项教师早期职业发展（Career）计划赠款将研究灾后住房恢复，使用家庭重建决策的微观层面。它研究了这些决定如何受到可量化的内部和外部控制变量的汇合的影响，并传播到影响社区恢复。预计未来极端事件将呈指数级增长，加上易受灾地区的人口不断增加，因此迫切需要更好地了解灾害恢复过程，并采取更有效的战略来加强这一过程。虽然许多研究侧重于评估灾害的经济影响或在宏观一级估计其损失，但很少（如果有的话）侧重于模拟家庭采取的集体行动，而这些行动被认为是整个社区恢复的关键。这项研究的结果将产生广泛的影响，因为它们可以帮助恢复援助框架的成员确定优先次序和整合政策，以加强灾后恢复。这项研究的多学科性质及其多样化的教学方法以及其嵌入的社会互动协议将允许更有效的研究，教学和更广泛地参与代表性不足的群体以及相关研究的多个利益相关者。这项研究的目的是提供有关家庭在社区范围内恢复的集体性质的基本知识。本研究将通过在社区更广泛的社会经济背景下捕捉家庭行为的微观动态，开发一个基于gis的基于主体的灾后住房集体恢复行为模型。将检查内部和外部控制变量。内部控制变量包括家庭人口统计、社会网络、经验、社会经济、社会心理因素和灾害暴露。外部控制变量是指能够影响家庭恢复决策的活动，包括旨在恢复经济和生命线基础设施并提供财政激励的恢复援助框架成员的活动，以及邻居进行的任何住房恢复活动，从而产生时空效应。该项目将整合基于主体的建模、地理信息系统和博弈论，以帮助解读家庭互动对受灾社区集体恢复的影响。这种自下而上的方法将允许模拟家庭之间的互动，以模拟真实事件。这些模拟将使保单持有人能够通过开发的模型评估其恢复政策的有效性。行为模型将是模块化的、可扩展的，并且可以很容易地扩展到任何类型的灾难。

This Faculty Early Career Development (CAREER) Program grant will study post-disaster housing recovery, using the micro-level of households' reconstruction decisions. It examines how those decisions are affected by a confluence of quantifiable internal and external control variables and propagated to influence community recovery. Anticipated exponential increases in future extreme events coupled with the growing population in disaster-prone regions has created an urgent need for better understanding of the process of disaster recovery and more effective strategies to enhance it. While many studies have focused on assessing the economic impacts of disasters or estimating its losses on a macro-level, very few, if any, have focused on modeling the collective actions taken by households which is deemed to be the key to community-wide recovery. The results from this research will have a broad impact, as they can be instrumental to the members of recovery assistance framework in prioritizing and integrating policies to enhance post-disaster recovery. The multidisciplinary nature of this research and its diversified pedagogy together with its embedded social interaction protocols will allow for a more effective research, teaching and a broader involvement of underrepresented groups as well as multiple stakeholders in related research. This research is aimed at contributing fundamental knowledge on the collective nature of households' community-wide recovery. This research will develop a GIS-enabled agent-based behavioral model of collective post-disaster housing recovery by capturing the micro-level dynamics of households' behavior within a broader socioeconomic context of their community. Both internal and external control variables will be examined. Internal control variables include households' demographics, social networks, experience, socio-economic, psychosocial factors, and disaster exposure. External control variables refer to activities that can influence households' recovery decisions and include the activities of members of the recovery assistance framework aimed at restoring economy and lifeline infrastructure and providing financial incentives along with any housing recovery activities by neighbors creating spatiotemporal effects. The project will integrate agent-based modeling, GIS, and game theory to help decipher the impact of households' interactions on the collective recovery of an affected community. This bottom-up approach will allow for simulation of interactions among households to mimic real events. These simulations will enable policyholders to assess the efficacy of their recovery policies through the developed model. The behavioral model will be modular, scalable, and can be easily extended to any type of disaster.