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

EAGER：SAI：将效用理论融入基础设施投资的新决策范式

基本信息

批准号：
2121730
负责人：
Destenie Nock
金额：
$ 30万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121730&HistoricalAwards=false
关键词：
EAGER SAI New Decision Paradigms

项目摘要

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.

加强美国基础设施 (SAI) 是一项 NSF 计划，旨在促进以人为本的基础性和潜在变革性研究，加强美国的基础设施，为社会经济活力和广泛的生活质量改善提供坚实的基础。私营部门创新、发展经济、创造就业机会、提供更多公共部门服务、加强社区、促进平等机会、保护自然环境、增强国家安全并增强美国的领导力。要实现这些目标，需要来自各个领域的专业知识。 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能够建设和维护有效的基础设施，从而改善生活和社会，并以技术和工程的进步为基础。该项目旨在通过将更多“宏观”系统级可持续性分析与更多“微观”利益相关者分配偏好相结合，创建更现实的多层次决策模型，为美国能源基础设施投资决策创建一个新框架。围绕能源系统基础设施的投资涉及众多决策者和每个群体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，甚至可能会随着时间的推移而发生变化，因为决策者会更多地了解他们所选择的投资决策的影响，否则有益的基础设施投资也可能会导致不良的分配结果。如果边缘化群体被排除在受益之外，该项目将调查对结果和机会分配的优先事项和看法，研究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，并评估对该模型的偏好。创建了该框架。通过耦合能源系统优化模型通过利益相关者的启发，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可接受的权衡。首先，从未来的工程人员中获取分配偏好，并评估教育如何影响这些偏好。其次，这些偏好被整合到能源微电网投资决策模型（最大化能源获取模型）中。第三，对当地社区进行调查以得出社区偏好，并将这些偏好整合到优化模型中。过程，工科学生和工科学生之间的差异这反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。