CAREER: Behavioral Analytics and Field Experiments in Sustainable Innovation Policies

数字平台上的大数据分析加速了创业活动，并支持经济中创新相关技能的发展。然而，这些数据往往表现出一种悖论，即在拥有、管理和控制从海量数据中获得情报的人（例如，拥有移动应用程序数据库的公司）与传播这些信息的人（例如，用户或社区）之间存在距离。对于公共领域的开放数据，可以通过直接让社区参与进来，并培训当地的学生和专业人员，使他们能够解释和翻译数据，以回答对社区有意义的问题，从而缩小这一距离。CAREER项目利用能源系统和交通领域的社会创新，运用大数据分析和实验科学来推进城市和非城市地区资源保护政策激励机制的设计。建筑和交通是重要的经济部门；因此，本研究呼吁人类和机器智能来推进大规模公民数据与行为策略相结合的研究，以增加资源节约，同时提供有关基础设施和局部影响的近实时汇总情报。这项研究为一套基于信息的政策干预创造了必要的数据基础设施。这包括应用人工智能和机器学习预测与因果推理方法的联系。需要人工处理数月的非结构化数据将被快速解释和分类，而存在于不同信息系统中的结构化数据可以被链接和激活，以实现策略分析。此外，该职业计划将行为研究、教学和计算工具的传播作为一种手段，以扩大历史上代表性不足的社区对分析政策过程的参与。这项研究将是数据科学和政策的交叉，将采用新的方法来评估、参与和传播开放数据，以解决关键公民基础设施和自然资源保护的资源效率和行为变化问题。它不仅与公民合作伙伴合作，而且与K-12学生以及本科生和研究生合作。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。

The analysis of big data in digital platforms accelerates entrepreneurial activity and supports the development of innovation related skills in the economy. However, these data often exhibit a paradox in that there is distance between those who own, curate, and control intelligence gleamed from massive data (e.g., companies owning mobile application databases) and those who broadcast it (e.g., users or communities). For open data in the public domain, this distance can be narrowed by directly involving communities, and training local cohorts of students and professionals to be able to interpret and translate data to answer questions of significance to the community. Using social innovations in energy systems and transportation, this CAREER project deploys big data analytics and experimental science to advance the design of incentives for resource conservation policies in both urban and non-urban areas. Buildings and transportation are important sectors of the economy; therefore, this research calls on human and machine intelligence to advance research on how large-scale civic data in combination with behavioral strategies can be used to increase resource conservation, while providing aggregate intelligence about infrastructure and localized impacts in near real time. This research creates the necessary data infrastructure for a suite of information-based policy interventions. This includes linkages in the application of artificial intelligence and machine learning prediction with methods of causal inference. Unstructured data that would take months of human processing will be interpreted and classified rapidly, while structured data that lives in disparate information systems can be linked and activated to enable policy analysis. Furthermore, this CAREER program integrates behavioral research, teaching, and the dissemination of computational tools as a means to broaden participation in analytic policy processes among historically under-represented communities. The research, will lies at the intersection of data science and policy, will deploy novel approaches to evaluate, engage, and disseminate open data to problems of resource efficiency and behavior changes for critical civic infrastructure and natural resource protection. It engages not only with civic partners, but also with K-12 students as well as undergraduate and graduate students.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.