NSF Award SES-1919329, ``Models of Complex Experimentation: Attribute Discovery, Contextual Experimentation, and Experimentation on Causal Graphs''
AWARD ABSTRACT
This award funds research in economic theory. The goal is to analyze learning and strategic experimentation in complex environments where information is critical but also challenging to acquire. Experimentation is key to innovation and progress in technology, public policy, markets, and science. The growing availability of big data and widespread use of experimental methods enable businesses and governments to conduct trials and other experimental efforts to quickly evaluate novel ideas, from marketing plans to government outreach efforts. Yet such data abundance and complexity also leave 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 across states in a federal union; 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.