Abstract

Policies and potential interventions are often expensive to run and difficult to change once put in place. As a result, policy-relevant experimentation in the social sciences . Machine Learning provides a useful paradigm for social planners or experimenters to more intelligently choosing among potential interventions to maximize impact or learning. I develop a model of reinforced learning for social experiments and policy design, demonstrating optimal search paths over relevant policy space. I suggest two areas of potential application.