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Exploring intertemporal decision-making dynamics through functional data analysis: investigating variations in different discount function's dimensions

Author

Listed:
  • Roberta Martino

    (University of Campania Luigi Vanvitelli)

  • Annamaria Porreca

    (G. D’Annunzio University of Chieti-Pescara)

  • Viviana Ventre

    (University of Campania Luigi Vanvitelli)

  • Fabrizio Maturo

    (Universitas Mercatorum)

Abstract

Intertemporal preferences are closely related to essential aspects of an individual’s emotional and cognitive domains. Discount functions are used to quantify these preferences, which can help us understand conditions such as addiction, depression, and Attention Deficit Hyperactivity Disorder (ADHD). However, traditional parametric models are limited when dealing with intertemporal preferences, mainly when behavioural biases are involved. This study exploits Functional Data Analysis (FDA) to investigate the properties of discount functions in intertemporal choices comparing people suffering from the Hikikomori pathology (a condition that involves social withdrawal) and normal people. Notably, the goal of this research is to look for statistically significant differences in the dynamics of intertemporal decision-making according to different gravity of the Hikikomori condition through the magnified FDA lens on different functional dimensions; the distinctive curves of discount functions categorised by Hikikomori scores prompted a more profound investigation via the so-called augmented functional analysis of variance. The original curves and their derivatives, and the discount rates and their first derivatives provide the different functional dimensions explored. This original approach of analyzing differences between subgroups according to decision-making behaviours is exciting from a methodological and practical perspective.

Suggested Citation

  • Roberta Martino & Annamaria Porreca & Viviana Ventre & Fabrizio Maturo, 2025. "Exploring intertemporal decision-making dynamics through functional data analysis: investigating variations in different discount function's dimensions," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(3), pages 2171-2196, June.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:3:d:10.1007_s11135-024-01869-y
    DOI: 10.1007/s11135-024-01869-y
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    References listed on IDEAS

    as
    1. Li-Lin Rao & Shu Li, 2011. "New paradoxes in intertemporal choice," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(2), pages 122-129, February.
    2. repec:cup:judgdm:v:6:y:2011:i:2:p:122-129 is not listed on IDEAS
    3. Fabrizio Maturo & Rosanna Verde, 2023. "Supervised classification of curves via a combined use of functional data analysis and tree-based methods," Computational Statistics, Springer, vol. 38(1), pages 419-459, March.
    4. Fabrizio Maturo & Rosanna Verde, 2024. "Combining unsupervised and supervised learning techniques for enhancing the performance of functional data classifiers," Computational Statistics, Springer, vol. 39(1), pages 239-270, February.
    5. Ventre, Viviana & Martino, Roberta & Cruz Rambaud, Salvador & Maturo, Fabrizio & Porreca, Annamaria, 2024. "An original approach to anomalies in intertemporal choices through functional data analysis: Theory and application for the study of Hikikomori syndrome," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    6. Rao, Li-Lin & Li, Shu, 2011. "New paradoxes in intertemporal choice," Judgment and Decision Making, Cambridge University Press, vol. 6(2), pages 122-129, February.
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