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

加强美国基础设施（SAI）是美国国家科学基金会的一个项目，旨在促进以人为本的基础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的创新，发展经济，创造就业机会，使公共部门提供的服务更有效率，加强社区，促进机会平等，保护自然环境，加强国家安全，并推动美国的领导地位。为了实现这些目标，需要来自科学和工程学科的专业知识。SAI侧重于人类推理和决策、治理以及社会和文化过程的知识如何使有效基础设施的建设和维护成为可能，从而改善生活和社会，并以技术和工程的进步为基础。该项目的目的是通过整合更多的“宏观”系统级可持续性分析和更多的“微观”利益相关者的分配偏好，为美国能源基础设施投资决策创建一个新的框架，以产生更现实的、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众多决策者和选民，每个人都有自己的解释和优先事项。不同的利益相关者的目标可能会发生冲突，甚至可能随着时间的推移而改变，因为决策者对他们所选择的投资决策的含义了解得更多。如果边缘化群体被排除在利益之外，原本有益的基础设施投资也可能导致糟糕的分配结果。该项目调查了优先事项和对结果和机会分布的看法，研究了它们如何随着教育而变化，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引出相结合而创建的，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引出分配偏好，并评估教育如何影响这些偏好。其次，将这些偏好整合到能源微电网投资决策模型（最大化能源接入模型）中。第三，对当地社区进行调查，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，工程专业学生和社区成员之间的差异被检查。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.