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Rationally inattentive intertemporal choice

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  • Samuel J. Gershman

    (Harvard University
    Harvard University
    Minds and Machines)

  • Rahul Bhui

    (Harvard University
    Harvard University)

Abstract

Discounting of future rewards is traditionally interpreted as evidence for an intrinsic preference in favor of sooner rewards. However, temporal discounting can also arise from internal uncertainty in value representations of future events, if one assumes that noisy mental simulations of the future are rationally combined with prior beliefs. Here, we further develop this idea by considering how simulation noise may be adaptively modulated by task demands, based on principles of rational inattention. We show how the optimal allocation of mental effort can give rise to the magnitude effect in intertemporal choice. In a re-analysis of two prior data sets, and in another experiment, we reveal several behavioral signatures of this theoretical account, tying choice stochasticity to the magnitude effect. We conclude that some aspects of temporal discounting may result from a cognitively plausible adaptive response to the costs of information processing.

Suggested Citation

  • Samuel J. Gershman & Rahul Bhui, 2020. "Rationally inattentive intertemporal choice," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16852-y
    DOI: 10.1038/s41467-020-16852-y
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    Cited by:

    1. Benjamin Enke & Thomas Graeber & Ryan Oprea & Thomas W. Graeber, 2023. "Complexity and Hyperbolic Discounting," CESifo Working Paper Series 10861, CESifo.
    2. Cruz Rambaud, Salvador & Ortiz Fernández, Piedad & Parra Oller, Isabel María, 2023. "A systematic review of the main anomalies in intertemporal choice," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 104(C).
    3. Benjamin Enke & Thomas Graeber & Ryan Oprea, 2023. "Complexity and Time," CESifo Working Paper Series 10327, CESifo.
    4. Jian-Qiao Zhu & Haijiang Yan & Thomas L. Griffiths, 2024. "Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice," Papers 2405.19313, arXiv.org.

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