IDEAS home Printed from https://ideas.repec.org/p/iim/iimawp/14427.html
   My bibliography  Save this paper

Learning of Utilitarian Decision Model through Preferences

Author

Listed:
  • Aggarwal, Manish

Abstract

Understanding and predicting the decision making behaviour of individuals is a subject of interest for marketers, strategists, economists and the computer scientists alike. We develop an aproach to learn a decision maker (DM)’s behavioral process by combining recent possibilistic discrete choice models with the emerging machine learning methods. The proposed approach considers the utility values derived by a DM from each of the attribute values (information source values). We take the training information in the form of the exemplary multi-attribute preferences, and the decision model is specified in terms of two vectors that are unique to a DM. The experimental results on a set of 10 benchmark datasets suggest that our approach is both intuitively appealing and competitive to state-of-the-art methods in terms of the prediction accuracy.

Suggested Citation

  • Aggarwal, Manish, 2016. "Learning of Utilitarian Decision Model through Preferences," IIMA Working Papers WP2016-02-12, Indian Institute of Management Ahmedabad, Research and Publication Department.
  • Handle: RePEc:iim:iimawp:14427
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:iim:iimawp:14427. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/eciimin.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.