IDEAS home Printed from https://ideas.repec.org/a/taf/gmasxx/v44y2020i2p65-89.html
   My bibliography  Save this article

Something out of nothing: a Bayesian learning computational model for the social construction of value

Author

Listed:
  • Lynette A. Shaw

Abstract

This article develops a formalism for the social construction of value. Using a model based on Bayesian agents, it demonstrates how “something” arises out of “nothing” via the emergence of durable value conventions and shows how the developed framework can be used to investigate socially constructed valuations under a variety of circumstances. The resulting analysis clarifies why assumptions that collectives will converge upon the “intrinsic” (i.e., non-socially originating) value of an object (e.g., market efficiency) may not hold for mixed social and non-social valuation regimes, explains the dependency of socially constructed valuations on early accidents, demonstrates the effects of confident actors on constructed values, and identifies the production of time-dependent ratcheting effects from the interaction of bubbles with value conventions.

Suggested Citation

  • Lynette A. Shaw, 2020. "Something out of nothing: a Bayesian learning computational model for the social construction of value," The Journal of Mathematical Sociology, Taylor & Francis Journals, vol. 44(2), pages 65-89, April.
    • Handle: RePEc:taf:gmasxx:v:44:y:2020:i:2:p:65-89
    DOI: 10.1080/0022250X.2019.1652173
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0022250X.2019.1652173
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0022250X.2019.1652173?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    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:taf:gmasxx:v:44:y:2020:i:2:p:65-89. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/gmas .

    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.