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Articles

Forecasting conflict in Africa with automated machine learning systems

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References

  • Beck, N., G. King, and L. Zeng. 2000. “Improving Quantitative Studies of International Conflict: A Conjecture.” American Political Science Review 94 (1): 21–35.
  • Bell, C., C. Besaw, and M. Frank. 2021. “The Rulers, Elections, and Irregular Governance (REIGN) Dataset.” One Earth Future. Access 11 June 2020. https://oefdatascience.github.io/REIGN.github.io/.
  • Bergstra, J., and Y. Bengio. 2012. “Random Search for Hyper-parameter Optimization.” The Journal of Machine Learning Research 13 (1): 281–305.
  • Blair, R. A., and N. Sambanis. 2020. “Forecasting Civil Wars: Theory and Structure in an Age of ‘Big Data’ and Machine Learning.” Journal of Conflict Resolution 64 (10): 1885–915. doi:10.1177/0022002720918923
  • Blake, J. 2020. “Preventing the Next Boko Haram in Northern Mozambique.” Accessed 2020 Dec 14. https://www.cfr.org/blog/preventing-next-boko-haram-northern-mozambique
  • Boehmke, F. J., O. Chyzh, and C. G. Thies. 2016. “Addressing Endogeneity in Actor-specific Network Measures.” Political Science Research and Methods 4 (1): 123–49. doi:10.1017/psrm.2015.34
  • Bowlsby, D., E. Chenoweth, C. Hendrix, and J. D. Moyer. 2020. “The Future Is a Moving Target: Predicting Political Instability.” British Journal of Political Science 50 (4): 1405–17. doi:10.1017/S0007123418000443
  • Brandt, P. T., J. R. Freeman, and P. A. Schrodt. 2014. “Evaluating Forecasts of Political Conflict Dynamics.” International Journal of Forecasting 30 (4): 944–62. doi:10.1016/j.ijforecast.2014.03.014
  • Campbell, J. 2020. “The Military-First Approach in Northern Mozambique Is Bound to Fail.” Accessed 2020 Dec 14. https://www.cfr.org/blog/military-first-approach-northern-mozambique-bound-fail
  • Carter, D. B., and C. S. Signorino. 2010. “Back to the Future: Modeling Time Dependence in Binary Data.” Political Analysis 18 (3): 271–92. doi:10.1093/pan/mpq013
  • Chenoweth, E., and J. Ulfelder. 2017. “Can Structural Conditions Explain the Onset of Nonviolent Uprisings?” Journal of Conflict Resolution 61 (2): 298–324. doi:10.1177/0022002715576574
  • Colaresi, M., and Z. Mahmood. 2017. “Do the Robot: Lessons from Machine Learning to Improve Conflict Forecasting.” Journal of Peace Research 54 (2): 193–214. doi:10.1177/0022343316682065
  • Coppedge, M., J. Gerring, C. H. Knutsen, S. I. Lindberg, and J. Teorell. 2021. “V-Dem [Country–year/country–date] Dataset V11.1.” Varieties of Democracy Project. doi: 10.23696/vdemds21.
  • Cranmer, S. J., and B. A. Desmarais. 2017. “What Can We Learn from Predictive Modeling?” Political Analysis 25 (2): 145–66. doi:10.1017/pan.2017.3
  • D’Orazio, V. 2020. “Conflict Forecasting and Prediction.” Oxford Research Encyclopedia of International Studies doi: 10.1093/acrefore/9780190846626.013.514.
  • D3M. 2021. “Data-Driven Discovery of Models.” https://datadrivendiscovery.org/
  • Dorff, C., M. Gallop, and S. Minhas. 2020. “Networks of Violence: Predicting Conflict in Nigeria.” The Journal of Politics 82 (2): 476–93. doi:10.1086/706459
  • Drori, I., Y. Krishnamurthy, R. Rampin, R. D. P. Lourenco, J. P. Ono, K. Cho, C. Silva, and J. Freire. 2018. “AlphaD3M: Machine Learning Pipeline Synthesis.” In Proceedings of the ICML Workshop on Automatic Machine Learning Stockholm, Sweden, pp. 1–8.
  • Gelpi, C., and N. Avdan. 2018. “Democracies at Risk? A Forecasting Analysis of Regime Type and the Risk of Terrorist Attack.” Conflict Management and Peace Science 35 (1): 18–42. doi:10.1177/0738894215608998
  • Gleditsch, K. S., and M. D. Ward. 2013. “Forecasting Is Difficult, Especially about the Future: Using Contentious Issues to Forecast Interstate Disputes.” Journal of Peace Research 50 (1): 17–31. doi:10.1177/0022343312449033
  • Goldsmith, B. E., and C. Butcher. 2018. “Genocide Forecasting: Past Accuracy and New Forecasts to 2020.” Journal of Genocide Research 20 (1): 90–107. doi:10.1080/14623528.2017.1379631
  • Goldstone, J. A., R. H. Bates, D. L. Epstein, T. R. Gurr, M. B. Lustik, M. G. Marshall, J. Ulfelder, and M. Woodward. 2010. “A Global Model for Forecasting Political Instability.” American Journal of Political Science 54 (1): 190–208. doi:10.1111/j.1540-5907.2009.00426.x
  • H2O.ai. 2017. June. “H2O AutoML.” H2O version 3.30.0.1.
  • Harff, B. 2003. “No Lessons Learned from the Holocaust? Assessing Risks of Genocide and Political Mass Murder since 1955.” American Political Science Review 97 (1): 57–73. doi:10.1017/S0003055403000522
  • Harwood, A. 2020. “After Lake Chad Offensive, April One of Deadliest Months in Boko Haram Conflict.” Council on Foreign Relations. Accessed 2020 Dec 14. https://www.cfr.org/blog/after-lake-chad-offensive-april-one-deadliest-months-boko-haram-conflict
  • Hastie, T., R. Tibshirani, and J. Friedman. 2011. The Elements of Statistical Learning. 2nd ed. New York, Springer.
  • Hegre, H., C. Bell, M. Colaresi, M. Croicu, F. Hoyles, R. Jansen, M. R. Leis, A. Lindqvist-mcgowan, D. Randahl, and E. G. Rød. 2021. “ViEWS2020: Revising and Evaluating the ViEWS Political Violence Early-Warning System.” Journal of Peace Research 58 (3): 599–611. doi:10.1177/0022343320962157.
  • Hegre, H., J. Karlsen, H. M. Nygård, H. Strand, and H. Urdal. 2013. “Predicting Armed Conflict, 2010–2050.” International Studies Quarterly 57 (2): 250–70. doi:10.1111/isqu.12007
  • Hegre, H., M. Allansson, M. Basedau, M. Colaresi, M. Croicu, H. Fjelde, F. Hoyles, L. Hultman, S. Högbladh, and R. Jansen. 2019. “ViEWS: A Political Violence Early-warning System.” Journal of Peace Research 56 (2): 155–74. doi:10.1177/0022343319823860.
  • Hegre, H., P. Vesco, and M. Colaresi. 2022. “Lessons from an Escalation Prediction Competition.” International Interactions 48 (4).
  • Hill, D. W., and Z. M. Jones. 2014. “An Empirical Evaluation of Explanations for State Repression.” American Political Science Review 108 (3): 661–87. doi:10.1017/S0003055414000306
  • Hirose, K., K. Imai, and J. Lyall. 2017. “Can Civilian Attitudes Predict Insurgent Violence? Ideology and Insurgent Tactical Choice in Civil War.” Journal of Peace Research 54 (1): 47–63. doi:10.1177/0022343316675909
  • Hutter, F., L. Kotthoff, and J. Vanschoren. 2019. Automated Machine Learning: Methods, Systems, Challenges. Cham, Switzerland: Springer Nature.
  • Hyndman, R. J., and G. Athanasopoulos. 2018. Forecasting: Principles and Practice. Melbourne, Australia: OTexts.
  • James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. An Introduction to Statistical Learning. Vol. 112. New York, NY: Springer.
  • Jansen, R., H. Hegre, M. Colaresi, and F. Hoyles. 2020. Benchmark Models for the ViEWS Prediction Competition. Uppsala, Sweden: ViEWS.
  • Jones, Z. M., and Y. Lupu. 2018. “Is There More Violence in the Middle?” American Journal of Political Science 62 (3): 652–67.
  • King, G., and L. Zeng. 2001. “Improving Forecasts of State Failure.” World Politics 53 (4): 623–58. doi:10.1353/wp.2001.0018
  • Lagazio, M., and T. Marwala. 2006. “Assessing Different Bayesian Neural Network Models for Militarized Interstate Dispute: Outcomes and Variable Influences.” Social Science Computer Review 24 (1): 119–31. doi:10.1177/0894439305281512
  • Lindholm, A., J. Hendriks, A. Wills, and T. B. Schön. 2022. “Predicting political violence using a state-space model.” International Interactions 48 (4).
  • Minhas, S., P. D. Hoff, and M. D. Ward. 2016. “A New Approach to Analyzing Coevolving Longitudinal Networks in International Relations.” Journal of Peace Research 53 (3): 491–505. doi:10.1177/0022343316630783
  • Ono, J. P., S. Castelo, R. Lopez, E. Bertini, J. Freire, and C. Silva. 2020. “PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines.” IEEE Transactions on Visualization and Computer Graphics 27 (2): 390–400. doi:10.1109/TVCG.2020.3030361
  • Rost, N., G. Schneider, and J. Kleibl. 2009. “A Global Risk Assessment Model for Civil Wars.” Social Science Research 38 (4): 921–33. doi:10.1016/j.ssresearch.2009.06.007
  • Sandler, T. and W. Enders. 2007. “Applying Analytical Methods to Study Terrorism.” International Studies Perspectives 8(3): 287–302.
  • Schutte, S., and N. B. Weidmann. 2011. “Diffusion Patterns of Violence in Civil Wars.” Political Geography 30 (3): 143–52. doi:10.1016/j.polgeo.2011.03.005
  • Sundberg, R., and E. Melander. 2013. “Introducing the UCDP Georeferenced Event Dataset.” Journal of Peace Research 50 (4): 523–32. doi:10.1177/0022343313484347
  • Tollefsen, A. F., H. Strand, and H. Buhaug. 2012. “PRIO-GRID: A Unified Spatial Data Structure.” Journal of Peace Research 49 (2): 363–74. doi:10.1177/0022343311431287
  • Van der Laan, M. J., E. C. Polley, and A. E. Hubbard. 2007. “Super Learner.” Statistical Applications in Genetics and Molecular Biology 6 (1). doi:10.2202/1544-6115.1309.
  • Vesco, P., H. Hegre, M. Colaresi, R. B. Jansen, A. Lo, G. Reisch, and N. B. Weidmann. 2022. “United They Stand: Findings from an Escalation Prediction Competition.” International Interactions 48 (4). doi:10.1080/03050629.2022.2029856
  • Ward, M. D., B. D. Greenhill, and K. M. Bakke. 2010. “The Perils of Policy by P-value: Predicting Civil Conflicts.” Journal of Peace Research 47 (4): 363–75. doi:10.1177/0022343309356491
  • Ward, M. D., R. M. Siverson, and X. Cao. 2007. “Disputes, Democracies, and Dependencies: A Reexamination of the Kantian Peace.” American Journal of Political Science 51 (3): 583–601. doi:10.1111/j.1540-5907.2007.00269.x
  • Ward, M. D. 2016. “Can We Predict Politics? Toward What End?” Journal of Global Security Studies 1 (1): 80–91. doi:10.1093/jogss/ogv002
  • Weidmann, N. B., and M. D. Ward. 2010. “Predicting Conflict in Space and Time.” Journal of Conflict Resolution 54 (6): 883–901. doi:10.1177/0022002710371669