IDEAS home Printed from https://ideas.repec.org/a/taf/ginixx/v48y2022i4p521-554.html
   My bibliography  Save this article

Lessons from an escalation prediction competition

Author

Listed:
  • Håvard Hegre
  • Paola Vesco
  • Michael Colaresi

Abstract

Recent research on the forecasting of violence has mostly focused on predicting the presence or absence of conflict in a given location, while much less attention has been paid to predicting changes in violence. We organized a prediction competition to forecast changes in state-based violence both for the true future and for a test partition. We received contributions from 15 international teams. The models leverage new insight on the targeted problem, insisting on methodological advances, new data and features, and innovative frameworks which contribute to the research frontiers from various perspectives. This article introduces the competition, presents the main innovations fostered by the teams and discusses ways to further expand and improve upon this wisdom of the crowd. We show that an optimal modeling approach builds on a good number of the presented contributions and new evaluation metrics are needed to capture substantial models’ improvements and reward unique insights.La investigación reciente sobre la previsión de la violencia se ha centrado principalmente en predecir la presencia o ausencia de conflictos en un determinado lugar, mientras que se ha prestado mucha menos atención a predecir los cambios en la violencia. Organizamos una competencia de predicción para predecir los cambios en la violencia estatal tanto para el futuro cierto como para una división del análisis. Recibimos aportes de quince equipos internacionales. Los modelos aprovechan las nuevas ideas sobre el problema específico insistiendo en los avances metodológicos, los nuevos datos y características, así como en los marcos innovadores que contribuyen a las fronteras de la investigación desde diversas perspectivas. Este artículo presenta la competencia y las principales innovaciones que los equipos fomentan, y analiza maneras de expandirse y mejorar aún más a partir de esta sabiduría del público. Mostramos que un enfoque de modelación óptimo se crea a partir de un buen número de aportes presentados y que se necesitan nuevas métricas de evaluación para capturar las mejoras considerables de los modelos y para premiar las ideas únicas.Les recherches récentes sur la prévision de la violence se sont principalement concentrées sur la prédiction de la présence ou de l’absence de conflit dans un lieu donné, alors que beaucoup moins d’attention a été accordée à la prédiction des évolutions de la violence. Nous avons organisé un concours de prédictions dont l’objectif était de prévoir les évolutions de la violence étatique à la fois pour le futur réel et pour une partition test. Nous avons reçu des contributions de 15 équipes internationales. Les modèles concernés tirent profit de nouveaux renseignements sur le problème ciblé en insistant sur les progrès méthodologiques, sur de nouvelles données et caractéristiques et sur des cadres innovants contribuant à élargir les frontières des recherches de divers points de vue. Cet article présente le concours et les principales innovations proposées par les équipes et aborde les moyens d’étendre et d’améliorer cette sagesse de la foule. Nous montrons qu’une approche optimale de la modélisation repose sur bon nombre des contributions présentées et que de nouvelles métriques d’évaluation sont nécessaires pour saisir les améliorations substantielles des modèles et récompenser les idées uniques.

Suggested Citation

  • Håvard Hegre & Paola Vesco & Michael Colaresi, 2022. "Lessons from an escalation prediction competition," International Interactions, Taylor & Francis Journals, vol. 48(4), pages 521-554, July.
  • Handle: RePEc:taf:ginixx:v:48:y:2022:i:4:p:521-554
    DOI: 10.1080/03050629.2022.2070745
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03050629.2022.2070745
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03050629.2022.2070745?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Mueller, H. & Rauh, C. & Seimon, B., 2024. "Introducing a Global Dataset on Conflict Forecasts and News Topics," Janeway Institute Working Papers 2402, Faculty of Economics, University of Cambridge.
    2. Racek, Daniel & Thurner, Paul & Kauermann, Goeran, 2024. "Integrating Spatio-temporal Diffusion into Statistical Forecasting Models of Armed Conflict via Non-parametric Smoothing," OSF Preprints q59dr, Center for Open Science.
    3. Rød, Espen Geelmuyden & Gåsste, Tim & Hegre, Håvard, 2024. "A review and comparison of conflict early warning systems," International Journal of Forecasting, Elsevier, vol. 40(1), pages 96-112.

    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:taf:ginixx:v:48:y:2022:i:4:p:521-554. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GINI20 .

    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.