Models of Complex Experimentation: Attribute Discovery, Contextual Experimentation, and Experimentation on Causal Graphs

复杂实验模型：属性发现、上下文实验和因果图实验

• 批准号: 1919329
• 负责人: Arjada Bardhi
• 金额: $ 27.8万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919329&HistoricalAwards=false
• 关键词: Models; Complex; Experimentation; Attribute; Discovery

This award funds research in economic theory. The goal to analyze learning and strategic experimentation in complex environments where information is important but is also not always easily discovered. Experimentation is key to innovation and progress in technology, public policy, markets, and science. The growing availability of big data and wide spread use of experimental methods mean that businesses and governments conduct trial or experimental efforts to quickly evaluate everything from marketing plans to government outreach efforts. Yet such data abundance and complexity also leaves room for selective and distorted learning. This research will develop a framework to study such distortions in three complex environments: multi-site evaluation of social programs; small-scale experimentation and policy diffusion in across states in a federal system of government; and dynamic learning of causal relationships.The research seeks to contribute to existing work in optimal attribute learning, causal experimentation, and strategic experimentation with correlated outcomes. The PI will introduce and analyze optimal experimentation in three distinct environments in which the object of experimentation is complex. The first project studies a principal-agent model of experimentation and persuasion through selective discovery of attributes of a multi-attribute object of common interest. Conceptually, the study draws an analogy between optimal attribute sampling and small-scale multi-site evaluation of social programs. Methodologically, it offers a novel theoretical framework based on Gaussian processes. The second project will employ this Gaussian framework to build a dynamic model of experimentation in federal systems. Experimentation performed by individual states within a federal union is a striking example of the interplay between small-scale context-based experimentation and large-scale policy adoption. The analysis seeks to explain empirical patterns of horizontal and vertical policy diffusion. Motivated by the debate on mechanism experiments as an alternative to traditional policy experiments in empirical work, the third project studies strategic experimentation on a causal graph of random variables. The analysis will seek to identify formal conditions under which mechanism experiments dominate policy experiments, by marrying tools of causal graph theory with classic multi-armed bandit problems.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.

该奖项资助经济理论研究。目标是在复杂的环境中分析学习和战略实验，在这些环境中，信息很重要，但也不总是容易发现。实验是技术、公共政策、市场和科学创新和进步的关键。大数据的日益可用性和实验方法的广泛使用意味着企业和政府进行试验或实验工作，以快速评估从营销计划到政府推广工作的一切。然而，如此丰富和复杂的数据也为选择性和扭曲的学习留下了空间。 本研究将建立一个框架，以研究在三个复杂的环境中，这种扭曲：多站点的社会项目评估，小规模的实验和政策扩散，在跨州的联邦政府系统，以及动态学习的因果关系。本研究旨在促进现有的工作，在最佳属性学习，因果实验，并与相关结果的战略实验。PI将介绍和分析在三个不同的环境中，实验对象是复杂的最优实验。第一个项目研究了一个委托代理模型的实验和说服，通过选择性发现的属性的多属性对象的共同利益。从概念上讲，这项研究得出了最佳属性抽样和小规模的多站点评估的社会计划之间的类比。在方法论上，它提供了一个基于高斯过程的新的理论框架。第二个项目将采用这种高斯框架，建立一个动态模型的实验在联邦系统。在联邦联盟内由各州进行的实验是一个引人注目的例子，说明了小规模的基于背景的实验和大规模的政策采纳之间的相互作用。分析试图解释横向和纵向政策扩散的经验模式。第三个课题是基于在实证研究中作为传统政策实验的替代的机制实验的争论，研究随机变量因果图上的战略实验。分析将寻求确定正式条件下，机制实验主导政策实验，通过结婚的工具，因果图理论与经典的多臂土匪problems.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Local Evidence and Diversity in Minipublics

当地证据和小公众的多样性

• DOI: 10.1086/724322
• 发表时间: 2023
• 期刊: Journal of Political Economy
• 影响因子: 8.2
• 作者: Bardhi, Arjada;Bobkova, Nina
• 通讯作者: Bobkova, Nina