Advanced Search
MyIDEAS: Login to save this paper or follow this series

An R&D Race with Learning and Forgetting

Contents:

Author Info

  • Ulrich Doraszelski
Registered author(s):

    Abstract

    I develop a model of an R&D race in which firms learn and forget. The firm which makes a discovery first is awarded a prize. Firms compete to be the first by investing in R&D. As a by-product of its R&D effort, a firm accumulates knowledge. This knowledge stock is valuable even if success is not immediate. On the other hand, over time a firm's knowledge base depreciates. Unlike traditional models of symmetric R&D races, my model does not inherit the memorylessness property of the exponential distribution. Unlike models of multi-stage races where the stages or experience levels are mere labels, knowledge is productive in my model. I show that learning and forgetting shape firms' equilibrium payoffs and strategies and that many findings of the previous literature are reversed in this more general setting. The resulting patterns of strategic interactions appear to be consistent with both anecdotal evidence and empirical research on R&D races. The model does not in general allow for an analytical solution, and I employ projection methods to solve the partial differential equation that characterizes a firm's value function. Projection methods approximate the value function by a high-order polynomial. Special considerations arise since I need not only a good approximation of the value function but also good approximations of its derivatives to compute the Nash equilibrium in feedback strategies. An accuracy check indicates that the approximations yield a good description of the equilibrium payoffs and strategies. This suggests that projection techniques are promising tools for the analysis of differential games.

    Download Info

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below under "Related research" 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.

    Bibliographic Info

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 43.

    as in new window
    Length:
    Date of creation: 01 Apr 2001
    Date of revision:
    Handle: RePEc:sce:scecf1:43

    Contact details of provider:
    Email:
    Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
    More information through EDIRC

    Related research

    Keywords: dynamic competition; action-reaction; learning and forgetting; R&D;

    Find related papers by JEL classification:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:sce:scecf1:43. 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).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.