Diag Davenport is a behavioral economist studying technology and social problems that affect inequality in wealth and well-being. He is currently a Presidential Postdoctoral Research Fellow at Princeton. Starting in the fall, he will be an Assistant Professor of Technology Policy, Governance, and Society at UC Berkeley with appointments in the Goldman School of Public Policy and the School of Information.
His primary research shows how cognitive biases, social influences, and economic incentives combine to drive much of how we interact with algorithms and how they impact us. He partners with organizations to build algorithmic tools, studies how people interact with them, and considers how we should govern such tools at scale. He also has a broader interest in extending human capacity by systematically studying ways to improve mentorship, collective decision-making, and mental health.
Diag is a co-recipient of the 2023 Hillel Einhorn New Investigator Award from the Society of Judgment and Decision Making and his work received Honorable Mention for the 2023 Best Paper Award from the Behavioral Science and Policy Association. Diag’s work has been published in top journals such as Proceedings of the National Academy of Sciences and Nature Human Behavior. His work has also been featured in popular press venues such as Bloomberg, Boston Globe, Forbes, and The Telegraph.
Before joining Princeton, Diag earned his PhD in Behavioral Science from the University of Chicago Booth School of Business. Diag also has a master’s in Mathematics and Statistics from Georgetown and bachelor’s degrees in Economics and Management from Penn State. Prior to his PhD studies, Diag advised insurance and manufacturing executives on future litigation risks as a consultant at Bates White and built a variety of data products as a data scientist for the District of Columbia Superintendent of Education, the Federal Reserve Board, and a Covid-thwarted travel startup. During his PhD studies, he also operated an AI consulting firm that advised several startups on data strategy. Those clients have gone on to collectively raise over $20 million.