IDEAS home Printed from https://ideas.repec.org/p/sce/scecf4/221.html
   My bibliography  Save this paper

An agent-based model of directed advertising on a social network

Author

Listed:
  • C. Castaldi
  • F. Alkemade

Abstract

Network economics holds the view that individual actions and, in turn aggregate outcomes, are mainly determined by the interaction structure between heterogeneous economic agents. In this paper we study the diffusion of an innovation over a social network. More specifically, we study whether firms that receive only aggregate sales data can learn strategies to increase the size and the speed of the diffusion of their innovation over a network consisting of consumers. In order to do so the firm has to take into account both the characteristics of individual consumers and the topology of the social network. We use evolutionary agent-based experiments to simulate the learning behaviour of the firm and to study the diffusion dynamics. We find that firms can learn directed advertising strategies that take into account both the topology of the social consumer network and the characteristics of the consumer. These learned strategies lead to an increase in both the size and the speed of the innovation diffusion.

Suggested Citation

  • C. Castaldi & F. Alkemade, 2004. "An agent-based model of directed advertising on a social network," Computing in Economics and Finance 2004 221, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:221
    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

    Keywords

    social networks; innovation diffusion; agent-based economics;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:sce:scecf4:221. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/sceeeea.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.