Drew Fudenberg, Whitney K. Newey, Philipp Strack, Tomasz Strzalecki
arXiv 16 Aug 2019 · Econometrics · publishedProceedings of the National Academy of Sciences (2020)
arXiv:1908.05824 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Fudenberg, Strack, and Strzalecki (2018) Speed, accuracy, and the optimal timing of choices self | 0.737 | 5 | 2 | 60% |
| 2 | Baldassi, Cerreia-Vioglio, Maccheroni, and Marinacci (2018) An axiomatization of the Drift Diffusion Model and its extension to multi-alternative choice | 0.644 | 2 | 2 | 100% |
| 3 | Milosavljevic, Malmaud, Huth, Koch, and Rangel (2010) The drift diffusion model can account for value-based choice response times under high and low time pressure | 0.644 | 2 | 2 | 100% |
| 4 | Drugowitsch, Moreno-Bote, Churchland, Shadlen, and Pouget (2012) The cost of accumulating evidence in perceptual decision making | 0.585 | 3 | 1 | 100% |
| 5 | Newey (1994) The asymptotic variance of semiparametric estimators self | 0.511 | 2 | 2 | 50% |
| 6 | Aczél (1966) Lectures on functional equations and their applications\/, vol. 19 | 0.405 | 1 | 1 | 100% |
| 7 | Alós-Ferrer, Fehr, and Netzer (2018) Time will tell: recovering preferences when choices are noisy | 0.405 | 1 | 1 | 100% |
| 8 | Busemeyer and Townsend (1993) Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment | 0.405 | 1 | 1 | 100% |
| 9 | Busemeyer and Townsend (1992) Fundamental derivations from decision field theory | 0.405 | 1 | 1 | 100% |
| 10 | Che and Mierendorff (2016) Optimal Sequential Decision with Limited Attention | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 39 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Decision Conflict, Power Logit, and the Deferral Outside Option | 0.405 | 1 | 1 |