Researchers have long theorized about the processes through which family background and childhood experiences shape life outcomes. However, statistical models that use data on family background and childhood experiences to predict life outcomes often have poor predictive performance. In this talk, we present results from three interrelated studies of the predictability of life outcomes: a scientific mass collaboration involving hundreds of participants, a high-throughput study using hundreds of machine learning pipelines to predict hundreds of life outcomes, and a qualitative study involving in-depth interviews with 40 families. Collectively these studies help to assess and understand the limits of predictability of life outcomes, which has implications for social science theory and for algorithmic decision-making in high-stakes settings.
Matt Salganik is a professor of sociology who has pioneered uses of data and digital technologies in social research. He was appointed interim director of Princeton University’s Center for Information Technology Policy on July 1, 2019, and then director of CITP for a two-year term beginning July 1, 2020.
Matt is affiliated with several other Princeton’s interdisciplinary research centers, including: the Office for Population Research, the Center for Health and Wellbeing, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. He is the author of Bit by Bit: Social Research in the Digital Age.