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.
