IDEAS home Printed from https://ideas.repec.org/a/nas/journl/v118y2021pe2022685118.html
   My bibliography  Save this article

Retrieval-constrained valuation: Toward prediction of open-ended decisions

Author

Listed:
  • Zhihao Zhang

    (Haas School of Business, University of California, Berkeley, CA 94720; Social Science Matrix, University of California, Berkeley, CA 94720)

  • Shichun Wang

    (Haas School of Business, University of California, Berkeley, CA 94720)

  • Maxwell Good

    (Haas School of Business, University of California, Berkeley, CA 94720; Department of Neurology, University of California, San Francisco, CA 94158; Department of Veterans Affairs Northern California Health Care System, Martinez, CA 94553)

  • Siyana Hristova

    (Haas School of Business, University of California, Berkeley, CA 94720)

  • Andrew S. Kayser

    (Department of Neurology, University of California, San Francisco, CA 94158; Department of Veterans Affairs Northern California Health Care System, Martinez, CA 94553; Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720)

  • Ming Hsu

    (Haas School of Business, University of California, Berkeley, CA 94720; Social Science Matrix, University of California, Berkeley, CA 94720; Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720)

Abstract

Real-world decisions are often open ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here, we take a step toward addressing this issue by developing a neurally inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging, we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together, these results provide a neurally grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.

Suggested Citation

  • Zhihao Zhang & Shichun Wang & Maxwell Good & Siyana Hristova & Andrew S. Kayser & Ming Hsu, 2021. "Retrieval-constrained valuation: Toward prediction of open-ended decisions," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(20), pages 2022685118-, May.
  • Handle: RePEc:nas:journl:v:118:y:2021:p:e2022685118
    as

    Download full text from publisher

    File URL: http://www.pnas.org/content/118/20/e2022685118.full
    Download Restriction: no
    ---><---

    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:nas:journl:v:118:y:2021:p:e2022685118. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Eric Cain (email available below). General contact details of provider: http://www.pnas.org/ .

    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.