IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0250963.html
   My bibliography  Save this article

The implementation of random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries

Author

Listed:
  • Andrew B Whetten
  • John R Stevens
  • Damon Cann

Abstract

Time-to-event analysis is a common occurrence in political science. In recent years, there has been an increased usage of machine learning methods in quantitative political science research. This article advocates for the implementation of machine learning duration models to assist in a sound model selection process. We provide a brief tutorial introduction to the random survival forest (RSF) algorithm and contrast it to a popular predecessor, the Cox proportional hazards model, with emphasis on methodological utility for political science researchers. We implement both methods for simulated time-to-event data and the Power-Sharing Event Dataset (PSED) to assist researchers in evaluating the merits of machine learning duration models. We provide evidence of significantly higher survival probabilities for peace agreements with 3rd party mediated design and implementation. We also detect increased survival probabilities for peace agreements that incorporate territorial power-sharing and avoid multiple rebel party signatories. Further, the RSF, a previously under-used method for analyzing political science time-to event data, provides a novel approach for ranking of peace agreement criteria importance in predicting peace agreement duration. Our findings demonstrate a scenario exhibiting the interpretability and performance of RSF for political science time-to-event data. These findings justify the robust interpretability and competitive performance of the random survival forest algorithm in numerous circumstances, in addition to promoting a diverse, holistic model-selection process for time-to-event political science data.

Suggested Citation

  • Andrew B Whetten & John R Stevens & Damon Cann, 2021. "The implementation of random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.
  • Handle: RePEc:plo:pone00:0250963
    DOI: 10.1371/journal.pone.0250963
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250963
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0250963&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0250963?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. Cranmer, Skyler J. & Desmarais, Bruce A., 2017. "What Can We Learn from Predictive Modeling?," Political Analysis, Cambridge University Press, vol. 25(2), pages 145-166, April.
    2. Jacob M. Montgomery & Santiago Olivella, 2018. "Tree‐Based Models for Political Science Data," American Journal of Political Science, John Wiley & Sons, vol. 62(3), pages 729-744, July.
    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. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    2. Waggoner Philip D. & Kennedy Ryan & Le Hayden & Shiran Myriam, 2019. "Big Data and Trust in Public Policy Automation," Statistics, Politics and Policy, De Gruyter, vol. 10(2), pages 115-136, December.
    3. Robert A. Blair & Nicholas Sambanis, 2020. "Forecasting Civil Wars: Theory and Structure in an Age of “Big Data†and Machine Learning," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(10), pages 1885-1915, November.
    4. Tobias Heinrich & Yoshiharu Kobayashi, 2022. "Evaluating explanations for poverty selectivity in foreign aid," Kyklos, Wiley Blackwell, vol. 75(1), pages 30-47, February.
    5. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    6. Pamp, Oliver & Lebacher, Michael & Thurner, Paul W. & Ziegler, Eva, 2021. "Explaining destinations and volumes of international arms transfers: A novel network Heckman selection model," European Journal of Political Economy, Elsevier, vol. 69(C).
    7. Zhaochen He & John Camobreco & Keith Perkins, 2022. "How he won: Using machine learning to understand Trump’s 2016 victory," Journal of Computational Social Science, Springer, vol. 5(1), pages 905-947, May.
    8. Simon Montfort, 2023. "Key predictors for climate policy support and political mobilization: The role of beliefs and preferences," Papers 2306.10144, arXiv.org.
    9. Dyevre, Arthur & Lampach, Nicolas, 2018. "The origins of regional integration: Untangling the effect of trade on judicial cooperation," International Review of Law and Economics, Elsevier, vol. 56(C), pages 122-133.
    10. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.
    11. Vestby, Jonas & Buhaug, Halvard & von Uexkull, Nina, 2021. "Why do some poor countries see armed conflict while others do not? A dual sector approach," World Development, Elsevier, vol. 138(C).

    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:plo:pone00:0250963. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.