IDEAS home Printed from https://ideas.repec.org/a/sae/jocore/v65y2021i7-8p1427-1453.html
   My bibliography  Save this article

Is Theory Useful for Conflict Prediction? A Response to Beger, Morgan, and Ward

Author

Listed:
  • Robert A. Blair
  • Nicholas Sambanis

Abstract

Beger, Morgan, and Ward (BM&W) call into question the results of our article on forecasting civil wars. They claim that our theoretically-informed model of conflict escalation under-performs more mechanical, inductive alternatives. This claim is false. BM&W’s critiques are misguided or inconsequential, and their conclusions hinge on a minor technical question regarding receiver operating characteristic (ROC) curves: should the curves be smoothed, or should empirical curves be used? BM&W assert that empirical curves should be used and all of their conclusions depend on this subjective modeling choice. We extend our original analysis to show that our theoretically-informed model performs as well as or better than more atheoretical alternatives across a range of performance metrics and robustness specifications. As in our original article, we conclude by encouraging conflict forecasters to treat the value added of theory not as an assumption, but rather as a hypothesis to test.

Suggested Citation

  • Robert A. Blair & Nicholas Sambanis, 2021. "Is Theory Useful for Conflict Prediction? A Response to Beger, Morgan, and Ward," Journal of Conflict Resolution, Peace Science Society (International), vol. 65(7-8), pages 1427-1453, August.
    • Handle: RePEc:sae:jocore:v:65:y:2021:i:7-8:p:1427-1453
    DOI: 10.1177/00220027211026748
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00220027211026748
    Download Restriction: no
    File URL: https://libkey.io/10.1177/00220027211026748?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Andreas Beger & Richard K. Morgan & Michael D. Ward, 2021. "Reassessing the Role of Theory and Machine Learning in Forecasting Civil Conflict," Journal of Conflict Resolution, Peace Science Society (International), vol. 65(7-8), pages 1405-1426, August.
    3. Nils W. Metternich & Cassy Dorff & Max Gallop & Simon Weschle & Michael D. Ward, 2013. "Antigovernment Networks in Civil Conflicts: How Network Structures Affect Conflictual Behavior," American Journal of Political Science, John Wiley & Sons, vol. 57(4), pages 892-911, October.
    4. Chiba, Daina & Metternich, Nils W. & Ward, Michael D., 2015. "Every Story Has a Beginning, Middle, and an End (But Not Always in That Order): Predicting Duration Dynamics in a Unified Framework," Political Science Research and Methods, Cambridge University Press, vol. 3(3), pages 515-541, September.
    5. Beger, Andreas & Dorff, Cassy L. & Ward, Michael D., 2016. "Irregular leadership changes in 2014: Forecasts using ensemble, split-population duration models," International Journal of Forecasting, Elsevier, vol. 32(1), pages 98-111.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jun Xiang, 2017. "Dyadic Effects, Relevance, and the Empirical Assessment of the Kantian Peace," International Interactions, Taylor & Francis Journals, vol. 43(2), pages 248-271, March.
    2. Emily Kalah Gade & Michael Gabbay & Mohammed M. Hafez & Zane Kelly, 2019. "Networks of Cooperation: Rebel Alliances in Fragmented Civil Wars," Journal of Conflict Resolution, Peace Science Society (International), vol. 63(9), pages 2071-2097, October.
    3. Matija Kovacic & Claudio Zoli, 2021. "Ethnic distribution, effective power and conflict," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 57(2), pages 257-299, August.
    4. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    5. Jie-Huei Wang & Cheng-Yu Liu & You-Ruei Min & Zih-Han Wu & Po-Lin Hou, 2024. "Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data," Mathematics, MDPI, vol. 12(14), pages 1-24, July.
    6. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    7. João Chang Junior & Fábio Binuesa & Luiz Fernando Caneo & Aida Luiza Ribeiro Turquetto & Elisandra Cristina Trevisan Calvo Arita & Aline Cristina Barbosa & Alfredo Manoel da Silva Fernandes & Evelinda, 2020. "Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.
    8. Kikuta,Kyosuke & Hanayama,Manaho, 2023. "Does the Nobel Peace Prize Improve Women’s Rights? Prize and Praise in International Relations," IDE Discussion Papers 903, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    9. Arthur De Sá Ferreira & Ney Meziat-Filho & Ana Paula Antunes Ferreira, 2021. "Double threshold receiver operating characteristic plot for three-modal continuous predictors," Computational Statistics, Springer, vol. 36(3), pages 2231-2245, September.
    10. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    11. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    12. Masabho P Milali & Samson S Kiware & Nicodem J Govella & Fredros Okumu & Naveen Bansal & Serdar Bozdag & Jacques D Charlwood & Marta F Maia & Sheila B Ogoma & Floyd E Dowell & George F Corliss & Maggy, 2020. "An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    13. Daniel R Jeske, 2018. "Metrics Used When Evaluating the Performance of Statistical Classifiers," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(1), pages 7-9, August.
    14. Juliet Chebet Moso & Stéphane Cormier & Cyril de Runz & Hacène Fouchal & John Mwangi Wandeto, 2021. "Anomaly Detection on Data Streams for Smart Agriculture," Agriculture, MDPI, vol. 11(11), pages 1-17, November.
    15. Samuel Bazzi & Robert A. Blair & Christopher Blattman & Oeindrila Dube & Matthew Gudgeon & Richard Peck, 2022. "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 764-779, October.
    16. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    17. Tzu-Hsuan Lin & Jehn-Ruey Jiang, 2021. "Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    18. Mieke Deschepper & Willem Waegeman & Dirk Vogelaers & Kristof Eeckloo, 2020. "Using structured pathology data to predict hospital-wide mortality at admission," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-11, June.
    19. Alfred Krzywicki & David Muchlinski & Benjamin E. Goldsmith & Arcot Sowmya, 2022. "From academia to policy makers: a methodology for real-time forecasting of infrequent events," Journal of Computational Social Science, Springer, vol. 5(2), pages 1489-1510, November.
    20. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.

    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:sae:jocore:v:65:y:2021:i:7-8:p:1427-1453. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc 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 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: SAGE Publications (email available below). General contact details of provider: http://pss.la.psu.edu/ .

    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.