IDEAS home Printed from https://ideas.repec.org/a/cup/apsrev/v98y2004i02p371-378_00.html
   My bibliography  Save this article

Untangling Neural Nets

Author

Listed:
  • DE MARCHI, SCOTT
  • GELPI, CHRISTOPHER
  • GRYNAVISKI, JEFFREY D.

Abstract

Beck, King, and Zeng (2000) offer both a sweeping critique of the quantitative security studies field and a bold new direction for future research. Despite important strengths in their work, we take issue with three aspects of their research: (1) the substance of the logit model they compare to their neural network, (2) the standards they use for assessing forecasts, and (3) the theoretical and model-building implications of the nonparametric approach represented by neural networks. We replicate and extend their analysis by estimating a more complete logit model and comparing it both to a neural network and to a linear discriminant analysis. Our work reveals that neural networks do not perform substantially better than either the logit or the linear discriminant estimators. Given this result, we argue that more traditional approaches should be relied upon due to their enhanced ability to test hypotheses.

Suggested Citation

  • De Marchi, Scott & Gelpi, Christopher & Grynaviski, Jeffrey D., 2004. "Untangling Neural Nets," American Political Science Review, Cambridge University Press, vol. 98(2), pages 371-378, May.
  • Handle: RePEc:cup:apsrev:v:98:y:2004:i:02:p:371-378_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0003055404001200/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Nicholas J Shallcross & Darryl K Ahner, 2020. "Predictive models of world conflict: accounting for regional and conflict-state differences," The Journal of Defense Modeling and Simulation, , vol. 17(3), pages 243-267, July.
    2. Phil Henrickson, 2020. "Predicting the costs of war," The Journal of Defense Modeling and Simulation, , vol. 17(3), pages 285-308, July.
    3. George W Williford & Douglas B Atkinson, 2020. "A Bayesian forecasting model of international conflict," The Journal of Defense Modeling and Simulation, , vol. 17(3), pages 235-242, July.
    4. Christopher Gelpi & Nazli Avdan, 2018. "Democracies at risk? A forecasting analysis of regime type and the risk of terrorist attack," Conflict Management and Peace Science, Peace Science Society (International), vol. 35(1), pages 18-42, January.
    5. Faruk Balli & Hatice Ozer Balli & Mudassar Hasan & Russell Gregory-Allen, 2022. "Geopolitical risk spillovers and its determinants," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(2), pages 463-500, April.

    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:cup:apsrev:v:98:y:2004:i:02:p:371-378_00. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/psr .

    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.