Topic: Spatio-temporal aspects of rebel recruitment/mobilization

Thesis timeframe: Autumn 2019 & Spring 2020
Application deadline: 22 May 2019


The CLIMSEC project is currently working on creating early warning prediction models for the outbreak of violent conflict. We are particularly interested in finding out if climate related predictors can improve our ability to do early warning. A major issue is that mobilization processes can happen in different places from where violence breaks out. We need to develop models that can observe high resolution signals and connect these to outcomes further away in space and time. We are investigating whether deep learning can be used to model what we want. The MA student could contribute to this line of research or attempt other solutions to this problem. We are currently creating a comprehensive new dataset (PRIO-GRID v.3) that the MA student can help develop and/or use. The MA student could either work with the global dataset (although restrained in practice to where we find a lot of conflict), or with a particular case, perhaps documenting and tracing spatio-temporal aspects of rebel recruitment.

Relevant literature:

Baudains, Peter (2015) Spatio-temporal modeling of civil violence: Four frameworks for obtaining policy-relevant insights. Phd. Dissertation.

Eck, Kristine (2010) Raising Rebels – Participation and Recruitment in Civil War. Phd. Dissertation.

Gates, Scott (2002) Recruitment and Allegiance – The Microfoundations of Rebellion. Journal of Conflict Resolution, 46(1), 111-130.

Hegre, Håvard; Matthias Basedau; Michael Colaresi; Mihai Croicu; Hanne Fjelde; Lisa Hultman; Naima Mouhleb; Desirée Nilsson; Håvard Mokleiv Nygård; Espen Geelmuyden Rød; Gerald Schneider; Nina von Uexkull & Jonas Vestby (2019) ViEWS: A political Violence Early-Warning System, Journal of Peace Research 56(2).

Vestby, Jonas (2018) Climate, development, and conflict: Learning from the past and mapping uncertainties of the future. PhD thesis, Department of Political Science, University of Oslo, Oslo.


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