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Modeling life outcomes

Modeling life outcomes through foundational machine learning models

How well can we determine ex ante individuals’ life outcomes? Traditional low dimensional models employed in the social sciences prove of limited effectiveness when given such objective. We seek to improve the performance of such models by applying modern machine learning methods on CBS microdata. Neural network models for natural language processing (eg. BERT and GPT-3) have proven to have extraordinary power when trained on large language corpora. Thus by analogy, we believe that models trained on large and rich datasets of individual life courses for millions of individuals could have great explanatory power for a variety of social outcomes that exceeds that of standard social science models.

Participating organisations

Netherlands eScience Center
ODISSEI
Stony Brook University
European University Institute
Social Sciences & Humanities
Social Sciences & Humanities

Team

Flavio Hafner
Flavio Hafner
Lead RSE
Netherlands eScience Center
Rena Bakhshi
Rena Bakhshi
Programme Manager
Netherlands eScience Center
TP
Tanzir Pial
Stony Brook University
DH
Dakota Handzlik
LS
Lucas Sage
European University Institute
SS
Steven Skiena
AvdR
Arnout van de Rijt