"Not So Technical: The Importance of Standard Theories and Empirical Methods in Tech"
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Bio
Dan Silverman, Director of Seller Economics at Amazon, leads a team of scientists who combine economics, machine learning, and data science to improve decisions about seller fees and related policies. The team seeks to incentivize seller behavior to lower costs while improving the integrity of Amazon’s fee systems and the seller experience. Prior to his current role, Dan worked in the central science team for Amazon Stores, and before that he was an academic economist at Arizona State University and the University of Michigan. He is a Research Associate of the National Bureau of Economic Research and former co-editor of the American Economic Journal: Economic Policy. Prior to becoming an economist, Dan led outreach programs for a Community Development Credit Union serving low-income communities in Newark, NJ. He received a B.A. in political science from Williams College, a M.A. in Public Policy from Harvard University, and a Ph.D. in Economics from the University of Pennsylvania.
Presentation Abstract
Applications of science in the tech sector often seem to involve large-scale products: sophisticated combinations of econometrics and machine learning that derive insight from granular data sets and then implement real-time business decision-making. There is also, however, an important role for distinctly less techie science in tech. This presentation illustrates of how standard treatments of externalities, mechanism design, and optimal growth are quantified and applied in the tech sector. These treatments will be familiar to advanced undergraduate students, or to those early in their graduate programs, in economics, computer, and data science. The illustrations will show, especially, some benefits of training in theory for work in a technology company.