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OFAI-TR-2000-30 ( 118kB PDF file)

Timing, Sequencing, and Quantum of Life Course Events: a Machine Learning Approach

Francesco C. Billari, Johannes Fürnkranz, Alexia Prskawetz

In this methodological paper we discuss and apply machine learning techniques, a core research area in the artificial intelligence literature, to analyse simultaneously timing, sequencing, and quantum of life course events from a comparative perspective. We outline the need for techniques which allow the adoption of a holistic approach to the analysis of life courses, illustrating the specific case of the transition to adulthood. We briefly introduce machine learning algorithms to build decision trees and rule sets and then apply such algorithms to delineate the key features which distinguish Austrian and Italian pathways to adulthood, using Fertility and Family Survey data. The key role of sequencing and synchronisation between events emerges clearly from the methodology used.

Keywords: life course, event history, data mining, machine learning, transition to adulthood

Citation: Billari F., Fürnkranz J., Prskawetz A.: Timing, Sequencing, and Quantum of Life Course Events: a Machine Learning Approach. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2000-30, 2000. Also appeared as Working Paper WP 2000-10, Max-Planck Institute for Demographic Research, Rostock, Germany.