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Using Deep Learning To Uncover The Relation Between Age and Life Satisfaction

Diskussionspapiere extern

Micha Kaiser, Steffen Otterbach, Alfonso Sousa-Poza

2021,
(Research Square In Review)

Abstract

This study applies a machine learning (ML) approach to around half a million observations from the German Socio-Economic Panel to assess the relation between life satisfaction and age. We show that with our ML-based approach it is possible to isolate the effect of age on life satisfaction across the lifecycle without explicitly parameterizing the complex relationship between age and other covariates – this complex relation is taken into account by a feedforward neural network. Our results show a clear U-shape relation between age and life satisfaction across the lifespan, with a minimum at around 55 years of age.

Themen: Wohlbefinden



Keywords: Learning, Relation, Age and Life, machine learning
Externer Link:
https://www.researchsquare.com/article/rs-943521/v1.pdf
DOI:
https://doi.org/10.21203/rs.3.rs-943521/v1

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