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

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European Journal of Population / Revue européenne de Démographie Aims and scope Submit manuscript

Abstract

In this paper we discuss and apply machine learning techniques, using ideas from 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 life course analysis, 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 synchronization between events emerges clearly from the analysis.

Résumé

Dans cet article, nous appuyant sur des éléments centraux de la recherche en intelligence artificielle, nous appliquons les techniques d’apprentissage par machine pour analyser simultanément le calendrier, la séquence et l’intensité des événements du cycle de vie d’un point de vue comparatif. Nous soulignons le besoin de techniques qui permettent une approche holistique de l’analyse du cycle de vie, en illustrant ici le cas particulier de la transition vers l’âge adulte. Nous présentons brièvement les algorithmes d’apprentissage qui permettent de construire des arbres décisionnels et des ensembles de règles et appliquons ces algorithmes pour identifier, à partir des données des enquêtes Fécondité et Famille, les principaux traits qui distinguent les chemins de transition vers la vie adulte des Autrichiens et des Italiens. L’analyse fait ressortir le rôle clé joué par la séquence et la synchronisation des événements.

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Authors and Affiliations

  1. Institute of Quantitative Methods, Università Bocconi and IGIER, Viale Isonzo 25, I-20135, Milan, Italy

    Francesco C. Billari

  2. Department of Computer Science, Knowledge Engineering Group, TU Darmstadt, Hochschulstrasse 10, D-64289, Darmstadt, Germany

    Johannes Fürnkranz

  3. Vienna Institute of Demography, Prinz Eugen Strasse 8–10, A 1040, Wien, Austria

    Alexia Prskawetz

Authors
  1. Francesco C. Billari
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  2. Johannes Fürnkranz
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  3. Alexia Prskawetz
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Correspondence to Francesco C. Billari.

Additional information

Billari F.C., Fürnkranz J., et Prskawetz A., 2006. Calendrier, séquence et intensitédes événements du cycle de vie : une application des techniques d’apprentissage par machine. Revue Européenne de Démographie, 22: 37–65

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Billari, F.C., Fürnkranz, J. & Prskawetz, A. Timing, Sequencing, and Quantum of Life Course Events: A Machine Learning Approach. Eur J Population 22, 37–65 (2006). https://doi.org/10.1007/s10680-005-5549-0

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  • DOI: https://doi.org/10.1007/s10680-005-5549-0

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