Active preference learning to aid public decisions

当个人需要在选项之间做出选择时，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，例如价格或每加仑英里数，然后选择最能满足他们偏好的选项。当有许多选择，描述这些选择的复杂方法，以及当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对可用选项的风险、成本和收益的简单描述，帮助个人制定此类决策问题。然而，许多重要的决策涉及多个决策者，例如一个家庭购买汽车，一群朋友选择看电影，甚至是公众成员为他们的城市，州或国家选择未来的能源政策。在这项研究中，我们推广的个人决策援助的公共决策援助，帮助群体的异质决策者达成共识，使用个人和群体的选择信息。为了做到这一点，我们结合联合收割机的方法，从积极的偏好学习与使用社会福利功能，映射个人到群体的偏好。我们的两个公共决策辅助工具：1）通过向决策者提出最少数量的问题来精确地学习偏好，从而学习个人偏好; 2）有效地学习群体社会福利函数; 3）根据学习到的个人和群体偏好向群体提出建议。使用这种方法，我们的目标是回答三个研究问题：1）什么选择规则的个人和团体使用的能源和环境政策？2)什么样的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？该研究通过将行为决策研究，决策分析，主动机器学习和技术经济分析的交叉点的理论和模型相结合，推进了决策的基础知识。该项目将研究公共决策援助方法的概念，方法和经验基础，以帮助利益相关者群体就公共政策达成共识。为了做到这一点，该项目结合了主动偏好学习方法，选择信息量最大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到群体偏好的映射。三个目标推动这项研究。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，使用模拟和先验数据来测试该架构。目标2扩展了具有同质度1惩罚的架构，以从群体选择行为中学习群体社会福利函数，使用模拟来测试神经网络对试点研究中建立的社会福利函数先验的影响。目标3在两种情况下收集新数据。第一个测试最好的个人和团体的主动偏好学习方法在美国联邦能源政策的在线随机实验。第二个项目使用现场实验来帮助智利监管机构优先进行环境检查。研究结果扩大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中达成群体共识。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.