Active preference learning to aid public decisions

主动偏好学习有助于公共决策

• 批准号: 2049333
• 负责人: Destenie Nock
• 金额: $ 40.03万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2049333&HistoricalAwards=false
• 关键词: Active; preference; learning; aid; public

When individuals need to choose between options, such as whether to buy an electric or hybrid car, they must first characterize those options, for example in terms of price or miles per gallon, then select the option that best satisfies their preferences. This can be a daunting task when there are many options, complex ways of characterizing those options, and when individuals are unsure about how to make tradeoffs among them. Decision aids help individuals formulate such decision problems by providing a simple characterization of the risks, costs, and benefits of the available options. Yet, many important decisions involve multiple decision-makers, such as a family purchasing a car, a group of friends choosing a movie to watch, or even members of the public choosing the future of energy policy for their city, state, or country. In this research we generalize the individual decision aid to a public decision aid, that helps groups of heterogeneous decision-makers come to consensus using information about individual and group choices. To do this, we combine methods from active preference learning with the use of social welfare functions that map individual to group preferences. Our two public decision aids 1) learn individual preferences by asking the minimum number of questions of a decision-maker to precisely learn preferences, 2) efficiently learn group social welfare functions, and then 3) make recommendations to groups based on the learned individual and group preferences. Using this approach, we aim to answer three research questions: 1) What choice rules do individuals and groups use for energy and environmental policy? 2) What active learning methods can best estimate those choice rules? 3) To what degree does heterogeneity in social preferences affect group consensus? The research forwards fundamental knowledge of decision-making by combining theories and models at the intersection of behavioral decision research, decision analysis, active machine learning, and techno-economic analysis. This project forwards research into the conceptual, methodological, and empirical foundations of a public decision aid approach for helping groups of stakeholders come to consensus on public policies. To do this, the project combines active preference learning methods that select the most informative choice sets to learn preferences, with social welfare optimization, that learns a mapping from individual to group preferences based on group behavior. Three aims advance this research. Aim 1 develops a novel twinned neural network architecture that can actively learn the individual preferences of decision-makers across many different types of behavioral choice rules, using simulations and prior data to test the architecture against strong benchmarks. Aim 2 extends that architecture with a homogeneous degree 1 penalty to learn group social welfare functions from group choice behavior, using simulations to test the neural network against social welfare function priors established in pilot research. Aim 3 collects new data in two contexts. The first tests the best individual and group active preference learning approaches in an online randomized experiment for US federal energy policy. The second uses a field experiment to help Chilean regulators prioritize environmental inspections. The results expand scientific understanding of the capability and efficiency of methods for learning individual and group preferences, and help practitioners use the most effective methods for reaching group consensus in energy and environmental public policy.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.

当个人需要在选项之间进行选择时，例如是否购买电动汽车或混合动力汽车，他们必须首先描述这些选项的特征，例如价格或每加仑的英里数，然后选择最能满足他们偏好的选项。当有很多选择、描述这些选择的复杂方式以及个人不确定如何在它们之间进行权衡时，这可能是一项艰巨的任务。决策辅助工具通过提供可用选项的风险、成本和收益的简单描述来帮助个人制定此类决策问题。然而，许多重要决策涉及多个决策者，例如一个家庭购买一辆汽车、一群朋友选择观看一部电影，甚至是公众选择其城市、州或国家能源政策的未来。在这项研究中，我们将个人决策辅助推广为公共决策辅助，帮助异质决策者群体使用有关个人和群体选择的信息达成共识。为此，我们将主动偏好学习的方法与将个人偏好映射到群体偏好的社会福利函数相结合。我们的两个公共决策辅助工具1）通过向决策者询问最少数量的问题来精确学习偏好来学习个人偏好，2）有效地学习群体社会福利函数，然后3）根据学习到的个人和群体向群体提出建议群体偏好。使用这种方法，我们旨在回答三个研究问题：1）个人和团体在能源和环境政策中使用什么选择规则？ 2）什么主动学习方法可以最好地估计这些选择规则？ 3）社会偏好的异质性在多大程度上影响群体共识？该研究通过结合行为决策研究、决策分析、主动机器学习和技术经济分析的理论和模型，推进决策的基础知识。该项目对公共决策援助方法的概念、方法和实证基础进行研究，以帮助利益相关者群体就公共政策达成共识。为此，该项目将主动偏好学习方法与社会福利优化相结合，主动偏好学习方法选择信息最丰富的选择集来学习偏好，社会福利优化根据群体行为学习从个人偏好到群体偏好的映射。推进这项研究的三个目标。 Aim 1 开发了一种新颖的孪生神经网络架构，可以主动学习决策者在许多不同类型的行为选择规则中的个人偏好，并使用模拟和先前数据根据强大的基准测试该架构。目标 2 通过同质 1 度惩罚扩展该架构，从群体选择行为中学习群体社会福利函数，并使用模拟根据试点研究中建立的社会福利函数先验来测试神经网络。目标 3 在两种情况下收集新数据。第一个在美国联邦能源政策的在线随机实验中测试了最佳的个人和团体主动偏好学习方法。第二个项目利用现场实验来帮助智利监管机构优先考虑环境检查。研究结果拓展了对学习个人和群体偏好的方法的能力和效率的科学理解，并帮助从业者使用最有效的方法在能源和环境公共政策方面达成群体共识。该奖项反映了 NSF 的法定使命，并被认为是值得的。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来提供支持。