IDEAS home Printed from https://ideas.repec.org/a/taf/jecmet/v28y2021i1p114-123.html
   My bibliography  Save this article

How-possibly explanations in economics: anything goes?

Author

Listed:
  • Till Grüne-Yanoff
  • Philippe Verreault-Julien

Abstract

The recent literature on economic models has rejected the traditional requirement that their epistemic value necessary depended on them offering actual explanations of phenomena. Contributors to that literature have argued that many models do not aim at providing how-actually explanations, but instead how-possibly explanations. However, how to assess the epistemic value of HPEs remains an open question. We present a programmatic approach to answering it. We first introduce a conceptual framework that distinguishes how-actually explanations from how-possibly explanations and that further differentiates between epistemic and objective how-possibly explanations. Secondly, we show how that framework can be used for methodological appraisal as well as for understanding methodological controversies.

Suggested Citation

  • Till Grüne-Yanoff & Philippe Verreault-Julien, 2021. "How-possibly explanations in economics: anything goes?," Journal of Economic Methodology, Taylor & Francis Journals, vol. 28(1), pages 114-123, January.
  • Handle: RePEc:taf:jecmet:v:28:y:2021:i:1:p:114-123
    DOI: 10.1080/1350178X.2020.1868779
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1350178X.2020.1868779
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1350178X.2020.1868779?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jan Schulz & Daniel M. Mayerhoffer, 2021. "Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms," Journal of Business Economics, Springer, vol. 91(9), pages 1357-1385, November.
    2. Schulz, Jan & Mayerhoffer, Daniel M., 2021. "A network approach to consumption," BERG Working Paper Series 173, Bamberg University, Bamberg Economic Research Group.
    3. Kai Fischbach & Johannes Marx & Tim Weitzel, 2021. "Agent-based modeling in social sciences," Journal of Business Economics, Springer, vol. 91(9), pages 1263-1270, November.

    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:jecmet:v:28:y:2021:i:1:p:114-123. 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/RJEC20 .

    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.