Laboratory

Population Dynamics and Sustainable Well-Being

At a Glance Projects Publications Team

Project

Forecasting Individual-Level Mortality

Ugofilippo Basellini, Emilio Zagheni, Monica Alexander (University of Toronto, Canada), Luca Badolato (The Ohio State University, Columbus, USA), Ari Gabriel Decter-Frain (Cornell University, Ithaca, USA), Nicolas Irons (MPIDR / University of Washington, Seattle, USA), Maria Laura Miranda (MPIDR / Federal University of Minas Gerais, Belo Horizonte, Brazil), Erin Walk (MPIDR / Massachusetts Institute of Technology, Cambridge, USA), Elnura Zhalieva (Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, United Arab Emirates)

In this project, we provide novel insights into the predictability of individual lifespans, employing both classic statistical models and emerging machine-learning approaches. We focus on prediction accuracy and inequalities in predictability across socioeconomic groups.  Detailed Description

Prediction accuracy of individual-level lifespan for different models by gender, race and ethnicity, and education

Integrated Brier Score and Mean Area Under the Curve by gender (panel a.), race and ethnicity (panel b.), and education (panel c.). Prediction accuracy across models is worse for Men, non-Hispanic Blacks and low-educated individuals as compared to other socioeconomic groups. © © Badolato et al. (2023)

Publications

Badolato, L.; Decter-Frain, A. G.; Irons, N.; Miranda, M. L.; Walk, E.; Zhalieva, E.; Alexander, M. J.; Basellini, U.; Zagheni, E.:

The limits of predicting individual-level longevity

MPIDR Working Paper WP-2023-008. (2023)