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HDSI Seminar – Emily Aiken – Empirically understanding the relative value of prediction in allocation
March 2 @ 2:00 pm - 3:00 pm
Speaker: Emily Aiken
Date & Time: Monday March 2nd, 2:00pm
Location: HDSI Multipurpose Room 123
Talk Title:
Empirically understanding the relative value of prediction in allocation
Abstract:
Public institutions increasingly use prediction to allocate scarce resources. From a design perspective, better predictions compete with other investments, such as expanding capacity or improving treatment quality. Here, the big question is not how to solve a specific allocation problem, but rather which problem to solve. In this talk, I will introduce an empirical toolkit
Public institutions increasingly use prediction to allocate scarce resources. From a design perspective, better predictions compete with other investments, such as expanding capacity or improving treatment quality. Here, the big question is not how to solve a specific allocation problem, but rather which problem to solve. In this talk, I will introduce an empirical toolkit
to help planners form principled answers to this question and quantify the bottom-line welfare impact of investments in prediction versus other policy levers such as expanding capacity and improving treatment quality. I will then apply the framework to two real-world case studies on predictive methods for allocating humanitarian aid in Bangladesh and Ethiopia. Related papers: this and this.
Speaker bio:
Emily is an assistant professor jointly appointed in HDSI and the School of Global Policy and Strategy. Her research interests are at the intersection of data science and development economics, with a focus on analyzing large digital traces to inform the design of social protection and humanitarian aid programs. Her work centers on the implementation and evaluation of data-driven allocation systems for aid delivery in low-income countries.
Prior to joining UC San Diego, Emily was a postdoctoral scholar at Carnegie Mellon University Africa. She received her PhD from the UC Berkeley School of Information, and holds an MS in computer science from UC Berkeley and a BA in computer science from Harvard University.