RIPS Type 2: Participatory Modeling of Complex Urban Infrastructure Systems (Model Urban SysTems)

城市基础设施(特别是水、能源和交通基础设施)的适宜性和功能对城市的生存、可持续发展、弹性和成功至关重要。但是，由于基础设施系统通常被视为彼此独立，我们往往没有认识到系统之间的相互作用和相互关系的优势、劣势、机会和威胁。这种巴尔干化加剧了城市集中基础设施建设和控制的历史，这些历史导致了较少但更大的互不相连的系统，事实证明，这些系统很容易发生故障，而且可能是不可持续的。该项目的中心假设是，通过增加响应多样性，相互关联和分散的基础设施系统比孤立和集中的基础设施更具弹性。第二个假设是，分散的基础设施系统更具伸缩性和适应变化的能力。然而，评估这些假设的手段并不是现成的。虽然存在评估子系统的指标和模型，但无法将它们的性能和功能作为一个整体来考虑，也无法在社会、行为和经济决策(SBEDM)的背景下进行考虑。该项目将创建这一能力，然后以佐治亚州亚特兰大为试验台，在大都市和社区层面开发必要的全面了解集中式与分散式水、能源和交通(湿)系统的恢复能力。本课题的研究内容主要有四个。首先，它将为湿基础设施开发一个系统动力学模型，该模型将用于评估系统如何响应和适应两种不同的城市增长情景的外源和内源压力。系统动力学模型将把技术实施的挑战和影响与SBEDM结合起来。其次，将开发一个模型来量化湿基础设施的弹性。该模型将采用复原力的生态工程概念，并在这一过程中吸收人口统计学上具有代表性的利益攸关方队列。然后，建议的湿基础设施的弹性将与预期的气候变化应激源范围进行基准比较，并与复杂工程和生态系统理论定义的不同弹性指标进行比较。第三，将开发一套工具来模拟SBEDM环境中的决策。基于代理的模拟工具将捕获利益相关者内部的服务影响级别，贝叶斯网络模型将检查基础设施投资决策。该工具将与系统动力学模型交互，以告知利益相关者系统在压力因素下的性能，并将决策传达回系统模型，为未来的发展/恢复提供指导。服务影响水平将用基于代理的模型确定，投资决策将用概率方法进行。最后，将建立湿基础设施的复原力和可持续性的帕累托最优模型，以评估极端高温事件、干旱和洪水等气候变化应激源对水-能源-交通关系的影响。这项研究代表了一种新的系统系统方法，以设计关键城市基础设施在其物质和社会经济环境中的复原力。它将以复杂的系统工程方法和生态系统研究为基础，发展关于相互依存的基础设施系统的基本理论。这里提出的见解将有助于创建设计和评估复杂城市基础设施系统的复原力的工具和方法，检查工程方法与生态方法的价值，为弥合社会决策和城市设计之间的差距提供开创性的方法，并通过确定必要的数据和方法，为创建将城市复原力和可持续性纳入城市规划的国家研究议程做出贡献。与复杂工程系统相关的高级课程模块和课程材料(本科生和研究生)将通过可持续工程中心开发、推广和广泛分发。结果和方法将被整合到佐治亚理工学院现有的项目中，包括整合科学、数学和计算的教育中心，该中心专门针对与非裔美国高中生的科学互动。该小组将促使私营和公共部门的利益攸关方：1)开发模型结构和安全数据，以供行业合作伙伴实施和验证模型；2)探索全面的决策空间，从而使分析和模型与利益攸关方相关；3)通过互动模型开发，包括参与式游戏，提高模型透明度和刺激小组学习；以及4)使科学和工程成果更容易获得(例如，通过可视化)。

The fitness and function of infrastructure in urban areas (particularly infrastructure for water, energy, and transportation) is critically important for the survival, sustainability, resilience, and success of cities. But because infrastructure systems generally are viewed as independent from each other, we often fail to recognize the strengths, weaknesses, opportunities, and threats of the interactions and interrelations between systems. This balkanization is compounded by cities' histories of centralized infrastructure creation and control that has led to fewer, but bigger, disconnected systems that have proven to be susceptible to failure, and may be unsustainable moving forward. The central hypothesis of this project is that interconnected and decentralized infrastructure systems are more resilient than isolated and centralized infrastructure by increasing response diversity. A secondary hypothesis is that decentralized infrastructure systems are more scalable and adaptable to change. The means to assess these hypotheses, however, are not readily available. While metrics and models exist to evaluate the subsystems, there is no way to consider their performance and function working together as a whole and in the context of social, behavioral, and economic decision making (SBEDM). This project will create that capability and then use it to develop the necessary comprehensive understanding of the resilience of centralized versus decentralized water, energy, and transportation (WET) systems at the metropolitan city and community level using Atlanta, GA as a test bed. There are 4 main research elements in this project. First, it will develop a systems dynamics model for the WET infrastructure, and the model will be used to assess how the system responds and adapts to exogenous and endogenous stressors for two alternate urban growth scenarios. The systems dynamics model will integrate the challenges and impacts of technology implementation with SBEDM. Second, a model will be developed to quantify the resilience of the WET infrastructures. The model will adopt an ecological engineering conceptualization of resilience and engage a demographically representative cohort of stakeholders in the process. The resilience of the proposed WET infrastructure will then be benchmarked to the expected range of climate change stressors, and compared to different measures of resilience as defined by theories of complex engineered and ecological systems. Third, a set of tools will be developed to simulate decision-making in the SBEDM environment. An agent-based simulation tool will capture the level of service impacts within the stakeholders, and a Bayesian network model will examine infrastructure investment decision-making. This tool will interact with the systems dynamics model to inform the stakeholders about the system performance under stressors and convey the decision back to the systems model providing directives for future development/ rehabilitation. The level of service impacts will be determined with the agent based model and the investment decisions will be conducted with a probabilistic approach. Lastly, a Pareto optimality model will be created of resilience and sustainability in WET infrastructure to assess the effect of climate change stressors like extreme heat events, droughts and floods on the water-energy-transportation nexus. This research represents a new system-of-systems approach to engineering the resilience of critical urban infrastructures in the context of their physical and socio-economic environments. It will develop fundamental theories about interdependent infrastructure systems based on a complex systems engineering approach as well as from the study of ecological systems. The insights developed here will be useful in creating tools and methods for designing and evaluating the resilience of complex urban infrastructure systems, examining the value of engineering vs. ecological approaches, pioneering methods to bridge the gap between social decision making and urban design, and contributing to the creation of a national research agenda for integrating urban resilience and sustainability into urban planning by identifying necessary data and methodologies. Advanced course modules and curriculum materials (undergraduate and graduate) related to complex engineering systems will be developed, promoted, and widely distributed through the Center for Sustainable Engineering. Results and methods will be integrated into existing programs at Georgia Tech, including the Center for Education Integrating Science, Math, and Computing that specifically target scientific interactions with African American high school students. The team will engage stakeholders from the private and public sectors to: 1) develop model structures and secure data for model implementation and validation by industrial partners; 2) explore the full breadth of decision space thus making the analyses and models relevant to stakeholders; 3) increase model transparency and stimulate group learning through interactive model development including a participatory game; and 4) make science and engineering results more accessible (e.g., through visualization).