IDEAS home Printed from https://ideas.repec.org/p/war/wpaper/2018-12.html
   My bibliography  Save this paper

Drift-diffusion models: a direct verification

Author

Listed:
  • Michał Krawczyk

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

I conduct a laboratory experiment aimed at verifying the drift-diffusion model. The subjects are shown a sequence of noisy signals of the difference in cash value of two options. Every realization of the signal was costly and the subjects could stop observing it and make their decision at any point. As the cost of the signal, the expected value of each option, the standard deviation of these values and the actual values were systematically varied across 200 rounds, several predictions of the model could be put to a test. In all but one case these predictions were correct.

Suggested Citation

  • Michał Krawczyk, 2018. "Drift-diffusion models: a direct verification," Working Papers 2018-12, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2018-12
    as

    Download full text from publisher

    File URL: https://www.wne.uw.edu.pl/index.php/download_file/4220/
    File Function: First version, 2018
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ian Krajbich & Bastiaan Oud & Ernst Fehr, 2014. "Benefits of Neuroeconomic Modeling: New Policy Interventions and Predictors of Preference," American Economic Review, American Economic Association, vol. 104(5), pages 501-506, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anna Louisa Merkel & Johannes Lohse, 2019. "Is fairness intuitive? An experiment accounting for subjective utility differences under time pressure," Experimental Economics, Springer;Economic Science Association, vol. 22(1), pages 24-50, March.
    2. Susann Fiedler & Adrian Hillenbrand, 2018. "Gain-Loss Framing in Interdependent Choice," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2018_15, Max Planck Institute for Research on Collective Goods.
    3. Hébert, Benjamin & Woodford, Michael, 2023. "Rational inattention when decisions take time," Journal of Economic Theory, Elsevier, vol. 208(C).
    4. Rastislav Rehak, 2022. "Sequential Sampling Beyond Decisions? A Normative Model of Decision Confidence," CERGE-EI Working Papers wp739, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    5. Carlos Alós-Ferrer & Johannes Buckenmaier, 2021. "Cognitive sophistication and deliberation times," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 558-592, June.
    6. Krawczyk, Michał & Sylwestrzak, Marta, 2018. "Exploring the role of deliberation time in non-selfish behavior: The double response method," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 72(C), pages 121-134.
    7. Michalis Drouvelis & Johannes Lohse, 2020. "Cognitive abilities and risk taking: the role of preferences," Discussion Papers 20-02, Department of Economics, University of Birmingham.
    8. Grabiszewski, Konrad & Horenstein, Alex, 2022. "Measuring tree complexity with response times," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).
    9. Jan Hausfeld & Sven Resnjanskij, 2017. "Risky Decisions and the Opportunity Costs of Time," TWI Research Paper Series 108, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    10. Andrew Schotter & Isabel Trevino, 2021. "Is response time predictive of choice? An experimental study of threshold strategies," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 87-117, March.
    11. Benjamin Hébert & Michael Woodford, 2018. "Information Costs and Sequential Information Sampling," NBER Working Papers 25316, National Bureau of Economic Research, Inc.
    12. Ismaël Rafaï & Mira Toumi, 2017. "Pay Attention or Be Paid for Attention? Impact of Incentives on Allocation of Attention," GREDEG Working Papers 2017-11, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    13. Grabiszewski, Konrad & Horenstein, Alex, 2020. "Effort is not a monotonic function of skills: Results from a global mobile experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 634-652.
    14. Edi Karni, 2024. "Irresolute choice behavior," International Journal of Economic Theory, The International Society for Economic Theory, vol. 20(1), pages 70-87, March.
    15. Arkady Konovalov & Ian Krajbich, 2016. "Revealed Indifference: Using Response Times to Infer Preferences," Working Papers 16-01, Ohio State University, Department of Economics.
    16. Parra, Daniel, 2024. "Eliciting dishonesty in online experiments: The observed vs. mind cheating game," Journal of Economic Psychology, Elsevier, vol. 102(C).
    17. David J. Cooper & Ian Krajbich & Charles N. Noussair, 2019. "Choice-Process Data in Experimental Economics," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 1-13, August.
    18. repec:cup:judgdm:v:11:y:2016:i:5:p:460-471 is not listed on IDEAS
    19. Krajbich Ian & Smith Stephanie M., 2015. "Modeling Eye Movements and Response Times in Consumer Choice," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 13(1), pages 55-72, January.
    20. Ambroise Descamps & S´ebastien Massoni & Lionel Page, 2017. "Optimal hesitation, an experiment," QuBE Working Papers 048, QUT Business School.
    21. Carlos Alós-Ferrer & Ernst Fehr & Nick Netzer, 2021. "Time Will Tell: Recovering Preferences When Choices Are Noisy," Journal of Political Economy, University of Chicago Press, vol. 129(6), pages 1828-1877.

    More about this item

    Keywords

    drift-diffusion model; sequential sampling;

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D9 - Microeconomics - - Micro-Based Behavioral Economics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:war:wpaper:2018-12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marcin Bąba (email available below). General contact details of provider: https://edirc.repec.org/data/fesuwpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.