Model Integration Techniques for Sustainability Decision-Making

地球系统模型提供了物理气候系统的详细模拟，以及它如何响应人类活动，如温室气体排放。然而，目前的建模能力与决策者的需要之间存在很大差距，决策者必须制定基于证据的气候变化应对措施。为了缩小这一差距，我们需要能够将不同的计算模型整合在一起，以推理跨越自然科学和社会科学多个专业领域的系统交互，我们需要更好的工具来描述模型中不确定性的性质以及这种不确定性对决策的影响。拟议的研究将探讨如何构建，验证和使用系统的系统集成模型，为气候变化世界建设弹性，可持续社区的挑战提供决策支持。这将需要探索自然和社会系统之间的相互依存关系，以及对复杂动态行为进行建模和推理的能力，并整合由不同专家组构建的模型组件，每个组件都有自己的假设和限制。我将开始比较气候科学家使用的模式集成技术（我已经深入研究过）与其他领域采用的方法。例如，气候建模最初是模拟大气和海洋的热力学，但已扩大到包括与其他系统的相互作用，如冰盖、植被和土壤、大气化学和人类活动。同样，城市规划中使用的模型必须考虑到土地使用、人口、交通、能源、水、废物等的相互作用。对城市系统建模的初步评估表明，综合建模目前很少见，但越来越需要支持可持续性的长期决策，因为不同城市系统之间的行为互动影响到整个城市的复原力和可持续性。本研究将重点关注用于模型集成的软件工具和技术，以及科学家围绕测试和部署集成模型的实践，包括将不确定性传达给模型和模型结果的下游用户。为了应对这一挑战，我将采用混合方法，使用多个案例研究提供比较和对比方法，行动研究，与现有的模拟社区合作，以应对具体挑战，试点研究，以评估新的解决方案。这项研究的长期目标是为可持续发展的循证决策奠定更坚实的基础，并帮助克服跨学科问题的学科障碍和研究孤岛。一个指导性的假设是，模型集成计划可以加速跨学科思维的转变，因为它们为发现和解决具有不同学科背景的科学家之间在假设、术语和方法上的差异提供了一个焦点。从这个意义上说，计算模型是对科学理论的详细而明确的描述，而将它们整合在一起可以促进更深入的跨学科理解的发展。这一工作的示例应用包括减缓和适应气候变化的政策制定、可持续城市的城市规划和可持续农业。这项研究的预期成果包括科学模型的集成技术和软件架构的类型学，确定科学验证集成模型的最佳做法，以及从长远来看，系统的系统预测建模的软件框架。

Earth system models provide detailed simulations of the physical climate system and how it responds to human activities, such as emission of greenhouse gases. However, there is a large gap between current modelling capabilities, and the needs of decision-makers who must develop evidence-based responses to climate change. To close this gap, we need the ability to bring together disparate computational models to reason about systems interactions that span multiple areas of expertise across the natural and social sciences, and we need better tools to characterization the nature of uncertainty in the models and the implications of this uncertainty for decision-making. The proposed research will investigate how to construct, validate and use integrated models of systems-of-systems to provide decision support for the challenge of building resilient, sustainable communities for a climate changed world. This will require an exploration of inter-dependencies across natural and social systems, and the ability to model and reason about complex dynamical behaviour and integrate model components built by diverse groups of experts, each with its own assumptions and limitations. I I I will begin by comparing the model integration techniques used by climate scientists (which I have already studied in depth) with approaches taken in other areas. For example, climate modelling began with simulations of the thermodynamics of atmosphere and oceans, but has expanded to incorporate interactions with other systems, such as ice sheets, vegetation and soils, atmospheric chemistry, and human activities. Similarly, the models used in urban planning must take into account the interaction of land use, demographics, transport, energy, water, waste, etc. An initial assessment of urban systems modelling indicates that integrated modelling is currently rare, but increasingly in demand to support longer term decision-making for sustainability, as the interaction of behaviours across different urban systems affect the resilience and sustainability of the city as a whole. The study will focus on the software tools and techniques used for model integration, and the scientists' practices around testing and deploying integrated models, including the communication of uncertainty to downstream users of models and model results.To tackle this challenge, I will apply a mixed-methods approach, using multiple case studies to provide a compare-and-contrast approach, action research to work with existing modelling communities to tackle specific challenges, and pilot studies to evaluate new solutions. The long term aim of this research is build a stronger foundation for evidence-based decision-making for sustainability, and to help overcome disciplinary barriers and research silos for trans-disciplinary problems. A guiding hypothesis is that model integration initiatives can accelerate the move to trans-disciplinary thinking, as they provide a focus for the process of discovering and resolving differences in assumptions, terminology, and methodology between scientists with different disciplinary backgrounds. In this sense, computational models act as detailed, explicit descriptions of scientific theories, and integrating them stimulates the development of a deeper cross-disciplinary understanding.Example applications for this work include policymaking for mitigation and adaptation to climate change, urban planning for sustainable cities, and sustainable agriculture. Expected outcomes of this research include a typology of integration technologies and software architectures for scientific models, identification of best practices for scientific validation of integrated models, and, in the longer term, software frameworks for predictive modelling of systems-of-systems.