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On the functional form of temporal discounting: An optimized adaptive test

Author

Listed:
  • Daniel R. Cavagnaro

    (California State University Fullerton)

  • Gabriel J. Aranovich

    (University of California, San Francisco)

  • Samuel M. McClure

    (Arizona State University)

  • Mark A. Pitt

    (The Ohio State University)

  • Jay I. Myung

    (The Ohio State University)

Abstract

The tendency to discount the value of future rewards has become one of the best-studied constructs in the behavioral sciences. Although hyperbolic discounting remains the dominant quantitative characterization of this phenomenon, a variety of models have been proposed and consensus around the one that most accurately describes behavior has been elusive. To help bring some clarity to this issue, we propose an Adaptive Design Optimization (ADO) method for fitting and comparing models of temporal discounting. We then conduct an ADO experiment aimed at discriminating among six popular models of temporal discounting. Rather than supporting a single underlying model, our results show that each model is inadequate in some way to describe the full range of behavior exhibited across subjects. The precision of results provided by ADO further identify specific properties of models, such as accommodating both increasing and decreasing impatience, that are mandatory to describe temporal discounting broadly.

Suggested Citation

  • Daniel R. Cavagnaro & Gabriel J. Aranovich & Samuel M. McClure & Mark A. Pitt & Jay I. Myung, 2016. "On the functional form of temporal discounting: An optimized adaptive test," Journal of Risk and Uncertainty, Springer, vol. 52(3), pages 233-254, June.
    • Handle: RePEc:kap:jrisku:v:52:y:2016:i:3:d:10.1007_s11166-016-9242-y
    DOI: 10.1007/s11166-016-9242-y
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    Cited by:

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    2. Nina Anchugina, 2017. "One-Switch Discount Functions," Papers 1702.02254, arXiv.org.
    3. Jonathan Chapman & Erik Snowberg & Stephanie Wang & Colin Camerer, 2018. "Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)," NBER Working Papers 25072, National Bureau of Economic Research, Inc.
    4. Cathleen Johnson & Aurélien Baillon & Han Bleichrodt & Zhihua Li & Dennie Dolder & Peter P. Wakker, 2021. "Prince: An improved method for measuring incentivized preferences," Journal of Risk and Uncertainty, Springer, vol. 62(1), pages 1-28, February.
    5. Lisheng He & Pantelis P. Analytis & Sudeep Bhatia, 2022. "The Wisdom of Model Crowds," Management Science, INFORMS, vol. 68(5), pages 3635-3659, May.
    6. Sangil Lee & Chris M. Glaze & Eric T. Bradlow & Joseph W. Kable, 2020. "Flexible Utility Function Approximation via Cubic Bezier Splines," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 716-737, September.
    7. Yang Wang & Frank A. Sloan, 2018. "Present bias and health," Journal of Risk and Uncertainty, Springer, vol. 57(2), pages 177-198, October.
    8. David Scrogin, 2023. "Estimating risk and time preferences over public lotteries: Findings from the field and stream," Journal of Risk and Uncertainty, Springer, vol. 67(1), pages 73-106, August.

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