EAGER: SAI: New Decision Paradigms by Integrating Utility Theory into Infrastructure Investments

加强美国基础设施(SAI)是一项NSF计划，旨在刺激以人类为中心的基础性研究和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，增长经济，创造就业机会，提高公共部门服务提供效率，加强社区力量，促进机会均等，保护自然环境，增强国家安全，推动美国的领导地位。要实现这些目标，需要来自科学和工程学科的专业知识。SAI专注于人类推理和决策、治理以及社会和文化过程的知识如何使有效的基础设施的建设和维护能够改善生活和社会，并建立在技术和工程进步的基础上。该项目的目标是通过将更宏观的系统级别的可持续性分析与更多的利益相关者的“微观”分布偏好相结合，创建一个新的美国能源基础设施投资决策框架，以生成更现实的、多层次的决策模型。围绕能源系统基础设施投资的决策涉及许多决策者和支持者，每个人都有自己的解释和优先事项。不同的利益相关者目标可能会相互冲突，甚至可能会随着时间的推移而改变，因为决策者更多地了解了他们所选择的投资决策的影响。如果被边缘化的群体排除在受益范围之外，基础设施投资本来是有益的，也可能导致分配结果不佳。这个项目调查了关于结果和机会分配的优先事项和认知，研究了它们可能如何随着教育而改变，将它们纳入能源基础设施优化模型，并评估了对该模型的偏好。该框架是通过将能源系统优化模型与利益相关者启发相结合创建的，允许利益相关者确定和更新美国能源转型投资的环境、成本和分配结果之间的可接受权衡。首先，从未来的工程劳动力中得出分布偏好，以及对教育如何影响这些偏好的评估。其次，将这些偏好整合到能源微网投资决策模型(最大化能源获取模型)中。第三，对当地社区进行调查，以获得社区偏好，并将这些偏好整合到优化模型中。在整个过程中，工程学学生和社区成员之间的差异被审查。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.The aim of this project is to create a new framework for energy infrastructure investment decisions in the US by integrating more “macro” system-level sustainability analysis with more “micro” distributional preferences of stakeholders to generate more realistic, multi-level decision-making models. Decisions around energy system infrastructure investments involve numerous decision-makers and constituencies, each of which has their own interpretations and priorities. Different stakeholder objectives can conflict, and even may change over time as decision-makers learn more about the implications of their chosen investment decisions. Infrastructure investments otherwise beneficial can also lead to poor distributional outcomes if marginalized groups are excluded from the benefits. This project investigates priorities and perceptions around distributions of outcomes and opportunities, examines how they may change with education, incorporates them into an energy infrastructure optimization model, and evaluates preferences for this model.This framework is created by coupling an energy system optimization model with stakeholder elicitations, allowing stakeholders to determine and update acceptable tradeoffs between the environment, cost, and distributional outcomes for energy transition investment in the US. First, distributional preferences are elicited from the future engineering workforce along with an evaluation of how education can affect these preferences. Second, these preferences are integrated into an energy micro-grid investment decision model (Maximize Energy Access Model). Third, a survey is administered to the local community to elicit community preferences and these preferences are integrated into the optimization models. Throughout this process, the differences between engineering students and community members are examined.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.