DMUU: Center for Robust Decision-Making Tools for Climate and Energy Policy

这个合作研究小组将进行研究，旨在改进评估环境和能源政策所需的模型，并根据结果作出有力的决定。应对不断变化的环境对经济、粮食和水安全以及能源供应的影响是一项日益紧迫的挑战。需要新的理解和分析工具，使决策者能够在存在不确定性的情况下采取行动。将要开发的模型将提供关于地球和人类社会潜在未来的更深入的理解和可操作的信息，提高预测未来条件的能力，并量化预测的不确定性，以便决策者能够平衡风险和回报。这个合作小组包括经济学、物理科学、能源技术、法律、计算数学、统计学和计算机科学方面的专家，以改进和连接评估能源、经济、农业和其他部门所需的方法和模型。该合作小组将吸引高中生、本科生、研究生和博士后学者参与其工作，为多样化人口的教育做出贡献。协作小组开发的所有模型、数据和工具将向研究、政策和公民社区开放，以鼓励使用和参与。这个协作小组将努力实现新数据源、计算能力、参与者和协作中固有的机会。该合作小组将在五个关键领域进行研究：(1)改进未来气候的特征。该合作小组将对最先进的模型对极端气候和变异可能发生的变化的看法进行新的理解。(2)环境影响表征。该合作小组将开发更强大的计算工具，以预测环境变化对经济、农业、土地利用、人口统计和其他部门的影响。来自多个学科的专家将合作建立新的计算模型，对现有模型进行比较，分发模型评估和验证的历史数据，并使用多个模型进行影响评估。(3)整合不确定信息。防止环境破坏的最佳政策可能是那些在面对许多不确定因素时最好地平衡回报和风险的政策。协作小组将开发模型，以确定在广泛的场景中表现良好的稳健策略。(4)模型评价。该合作小组将制定评估对未来气候和影响的不同预测的可信度的方法。(5)计算技术。该合作小组将开发新的网络工具、数值解方法和充分利用现代超级计算机的大规模模型。这些工具将允许研究人员为模型添加额外的细节，探索不确定性，并提供以前不可能的结果。该合作小组项目由美国国家科学基金会社会、行为和经济科学理事会通过其不确定决策制定（DMUU）资助机会支持。

This collaborative research group will undertake research aimed at improving the models needed to evaluate environmental and energy policy and to make robust decisions based on outcomes. Addressing the consequences of a changing environment for the economy, food and water security, and energy supply is an increasingly urgent challenge. New understanding and analysis tools are needed that can allow decision makers to act in the presence of uncertainty. The models to be developed will provide deeper understanding and actionable information about the potential future of Earth and human society, improve capabilities to forecast future conditions, and quantify the uncertainty around projections so that decision makers can balance risk and reward. This collaborative group includes experts in economics, physical sciences, energy technologies, law, computational mathematics, statistics, and computer science to improve and connect the methods and models needed to evaluate energy, economics, agriculture, and other sectors. The collaborative group will engage high school students, undergraduates, graduate students, and postdoctoral scholars in its work, contributing to the education of a diverse population. All models, data, and tools developed by the collaborative group will be openly available to the research, policy, and citizen communities to encourage use and participation.This collaborative group will work to realize opportunities inherent in new data sources, computational capabilities, participants, and collaborations. The collaborative group will conduct research in five key areas: (1) Improved characterizations of future climate. The collaborative group will develop new understanding of what state-of-the-art models have to say about likely changes in climate extremes and variability. (2) Characterization of environmental impacts. The collaborative group will develop more powerful computational tools to forecast the influence of environmental change upon economics, agriculture, land use, demographics, and other sectors. Experts from multiple disciplines will collaborate on building new computational models, conducting comparisons of existing models, distributing historical data for model evaluation and validation, and using multiple models for impact assessments. (3) Integrating uncertain information. The best policies for preventing environmental damage are likely to be those that best balance return and risk in the face of many uncertainties. The collaborative group will develop models to identify robust strategies that perform well over a wide range of scenarios. (4) Evaluation of models. The collaborative group will develop methods for evaluating the confidence that can be placed in different projections of future climate and impacts. (5) Computational technologies. The collaborative group will develop new web tools, numerical solution methods, and large-scale models that make full use of modern supercomputers. These tools will allow researchers to add additional detail to models, explore uncertainty, and deliver previously impossible results. This collaborative group project is supported by the NSF Directorate for Social, Behavioral, and Economic Sciences through its Decision Making Under Uncertainty (DMUU) funding opportunity.