In this conversation with Yakov Feygin and Nick Vincent, we focus on how data and other assets get their value; compare data policy to the industrial policy of the depression era; and much more.
The backstory to this episode is a lengthy research collaboration focused on how the value of data gets captured. With that in mind, how to design a tax that would fairly redistribute it. You can see the collaboration results at Datadividends.org -- a proposal for a simple, eminently implementable tax that would go to the heart of the economic distortion caused by the data economy. In this conversation with Yakov Feygin and Nick Vincent, we focus on how data and other assets get their value; compare data policy to the industrial policy of the depression era; and much more.
Yakov Feygin is responsible for developing the research plan, projects, initiatives, and partnerships for the Future of Capitalism program at the Berggruen Institute. Before joining the Berggruen Institute, Yakov was a fellow in History and Policy at the Harvard University Kennedy School of Government and managing editor of The Private Debt Project. Yakov holds a Ph.D. in History with a focus on economic history from the University of Pennsylvania. His forthcoming book, Building a Ruin: The International and Domestic Politics of Economic Reform in the Soviet Union, will be published by Harvard University Press. He has taught courses in international political economy, money and banking, and business history and held fellowships from the Institute for New Economic Thinking, The Fulbright-Hays Doctoral Dissertation Research Abroad Program, Harvard University, and the University of Pennsylvania.
Nick Vincent is a Ph.D. student in Northwestern University's Technology and Social Behavior program and is part of the People, Space, and Algorithms Research Group. His broad research interests include human-computer interaction, human-centered machine learning, and social computing. His research focuses on studying the relationships between human-generated data and computing technologies to mitigate the negative impacts of these technologies. His work relates to concepts such as "data dignity," "data as labor," "data leverage," and "data dividends."