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Testing the Drift-Diffusion Model

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  • Drew Fudenberg
  • Whitney K. Newey
  • Philipp Strack
  • Tomasz Strzalecki

Abstract

The drift diffusion model (DDM) is a model of sequential sampling with diffusion (Brownian) signals, where the decision maker accumulates evidence until the process hits a stopping boundary, and then stops and chooses the alternative that corresponds to that boundary. This model has been widely used in psychology, neuroeconomics, and neuroscience to explain the observed patterns of choice and response times in a range of binary choice decision problems. This paper provides a statistical test for DDM's with general boundaries. We first prove a characterization theorem: we find a condition on choice probabilities that is satisfied if and only if the choice probabilities are generated by some DDM. Moreover, we show that the drift and the boundary are uniquely identified. We then use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic.

Suggested Citation

  • Drew Fudenberg & Whitney K. Newey & Philipp Strack & Tomasz Strzalecki, 2019. "Testing the Drift-Diffusion Model," Papers 1908.05824, arXiv.org.
    • Handle: RePEc:arx:papers:1908.05824
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    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.

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