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

该奖项资助经济理论方面的研究。目标是分析复杂环境中的学习和战略实验，其中信息很重要，但并不总是容易发现。实验是技术、公共政策、市场和科学创新和进步的关键。越来越多的大数据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外展工作的一切。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。本研究将开发一个框架来研究三种复杂环境中的这种扭曲：社会项目的多地点评估；在联邦政府体制下的各州进行小规模试验和政策推广；以及因果关系的动态学习。本研究旨在为最优属性学习、因果实验和具有相关结果的策略实验的现有工作做出贡献。PI将介绍和分析三种不同环境下的最佳实验，其中实验对象是复杂的。第一个项目通过选择性地发现共同利益的多属性对象的属性，研究实验和说服的委托代理模型。从概念上讲，该研究将最优属性抽样与社会项目的小规模多站点评估进行了类比。在方法上，它提供了一个新的基于高斯过程的理论框架。第二个项目将采用高斯框架在联邦系统中建立一个动态实验模型。由联邦联盟内的个别州进行的实验是小规模基于环境的实验与大规模政策采用之间相互作用的一个显著例子。该分析试图解释横向和纵向政策扩散的经验模式。受实证工作中机制实验作为传统政策实验替代方案的争论的推动，第三个项目研究随机变量因果图上的战略实验。分析将通过将因果图理论的工具与经典的多武装强盗问题结合起来，寻求确定机制实验主导政策实验的正式条件。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。

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.