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

城市地区基础设施（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、可持续性、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的，我们常常无法认识到系统之间相互作用和相互关系的优势、劣势、机会和威胁。这种割据化由于城市的集中式基础设施建设和控制的历史而变得更加复杂，这导致了更少、但更大、不相连的系统，这些系统已被证明容易发生故障，并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，互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可扩展性和适应性。然而，评估这些假设的方法并不容易获得。虽然存在用于评估子系统的度量和模型，但是没有办法将它们的性能和功能作为一个整体以及在社会、行为和经济决策制定（SBEDM）的上下文中一起考虑。该项目将创建这种能力，然后使用它来开发必要的综合理解集中与分散的水、能源和交通（WET）系统在大都市和社区层面的弹性，以亚特兰大为测试平台。在这个项目中有四个主要的研究元素。首先，它将开发一个WET基础设施的系统动力学模型，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性和内源性压力源。系统动力学模型将整合SBEDM技术实施的挑战和影响。其次，将开发一个模型来量化WET基础设施的弹性。该模型将采用弹性的生态工程概念，并在此过程中参与具有人口统计学代表性的利益相关者队列。然后，拟议的WET基础设施的恢复能力将以气候变化压力源的预期范围为基准，并与复杂工程和生态系统理论定义的不同恢复能力指标进行比较。第三，将开发一套工具来模拟SBEDM环境中的决策。基于代理的仿真工具将捕获利益相关者内部的服务影响水平，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，告知涉众在压力源下的系统性能，并将决策传达回系统模型，为未来的开发/恢复提供指示。服务影响的水平将由基于代理的模型确定，投资决策将采用概率方法进行。最后，将创建WET基础设施弹性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压力因素对水-能源-运输关系的影响。这项研究代表了一种新的系统的系统方法，在其物理和社会经济环境的背景下设计关键城市基础设施的弹性。它将发展基于复杂系统工程方法以及生态系统研究的相互依赖的基础设施系统的基本理论。在这里发展的见解将有助于创建设计和评估复杂城市基础设施系统弹性的工具和方法，检查工程与生态方法的价值，开创性的方法来弥合社会决策和城市设计之间的差距，并有助于创建一个国家研究议程，通过确定必要的数据和方法将城市弹性和可持续性纳入城市规划。与复杂工程系统相关的高级课程模块和课程材料（本科和研究生）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乔治亚理工学院现有的项目中，包括专门针对非裔美国高中生进行科学互动的综合科学、数学和计算教育中心。该团队将与来自私营和公共部门的利益相关者合作：1)开发模型结构并确保数据安全，供工业合作伙伴实施和验证模型；2)探索决策空间的广度，建立与利益相关者相关的分析和模型；3)通过包括参与式游戏在内的互动模型开发，提高模型透明度，促进小组学习；4)使科学和工程结果更容易获得（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).