IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1609.08746.html
   My bibliography  Save this paper

When Big Data Fails! Relative success of adaptive agents using coarse-grained information to compete for limited resources

Author

Listed:
  • V. Sasidevan
  • Appilineni Kushal
  • Sitabhra Sinha

Abstract

The recent trend for acquiring big data assumes that possessing quantitatively more and qualitatively finer data necessarily provides an advantage that may be critical in competitive situations. Using a model complex adaptive system where agents compete for a limited resource using information coarse-grained to different levels, we show that agents having access to more and better data can perform worse than others in certain situations. The relation between information asymmetry and individual payoffs is seen to be complex, depending on the composition of the population of competing agents.

Suggested Citation

  • V. Sasidevan & Appilineni Kushal & Sitabhra Sinha, 2016. "When Big Data Fails! Relative success of adaptive agents using coarse-grained information to compete for limited resources," Papers 1609.08746, arXiv.org.
    • Handle: RePEc:arx:papers:1609.08746
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1609.08746
    File Function: Latest version
    Download Restriction: no
    ---><---

    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:arx:papers:1609.08746. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.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.