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Testing the drift-diffusion model

Author

Listed:
  • Drew Fudenberg

    (Department of Economics, Massachusetts Institute of Technology, Cambridge, MA 02139)

  • Whitney Newey

    (Department of Economics, Massachusetts Institute of Technology, Cambridge, MA 02139)

  • Philipp Strack

    (Department of Economics, Yale University, New Haven, CT 06520)

  • Tomasz Strzalecki

    (Department of Economics, Harvard University, Cambridge, MA 02138)

Abstract

The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.

Suggested Citation

  • Drew Fudenberg & Whitney Newey & Philipp Strack & Tomasz Strzalecki, 2020. "Testing the drift-diffusion model," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(52), pages 33141-33148, December.
    • Handle: RePEc:nas:journl:v:117:y:2020:p:33141-33148
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    Cited by:

    1. Strittmatter, Anthony & Sunde, Uwe & Zegners, Dainis, 2022. "Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence," Rationality and Competition Discussion Paper Series 317, CRC TRR 190 Rationality and Competition.
    2. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. J r my Boccanfuso, 2022. "Consumption Response Heterogeneity and Dynamics with an Inattention Region," Working Papers wp1172, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Carlos Alós-Ferrer & Michele Garagnani, 2022. "Strength of preference and decisions under risk," Journal of Risk and Uncertainty, Springer, vol. 64(3), pages 309-329, June.
    5. Khai Xiang Chiong & Matthew Shum & Ryan Webb & Richard Chen, 2024. "Combining Choice and Response Time Data: A Drift-Diffusion Model of Mobile Advertisements," Management Science, INFORMS, vol. 70(2), pages 1238-1257, February.

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