In This Article Forecasting in International Relations

  • Introduction
  • General Overviews
  • Foundations
  • Decision Support Systems
  • Individual Characteristics

International Relations Forecasting in International Relations
Nils W. Metternich, Kristian Skrede Gleditsch
  • LAST MODIFIED: 28 June 2016
  • DOI: 10.1093/obo/9780199743292-0179


Forecasting has always been a central aspiration in the study of international relations. Early efforts to develop a “science of politics” typically emphasized the ability to anticipate future events as a key advantage. The behavioral revolution likewise gave strong prominence to the positivist equivalence between explaining phenomena and the ability to predict. However, despite many programmatic statements, predictions have largely remained aspirations until relatively recently. In this bibliography, we will provide an overview of the existing literature on forecasting, and on applications to conflict in particular. This overview will be organized around differences in specific approaches to forecasting, largely categorized by methods. However, forecasts can differ along a number of dimensions that cut across specific methods. First, there are differences in the scope of forecasts. Some predictions are highly case-specific and predict outcomes for a single unit and particular issue area. Other models have global scope and seek to assess the risks of some general phenomenon across a wide range of units. Between these two extremes there are various more delimited forecasts, focusing on a more confined set such as regions or a group of actors. Second, forecasts differ considerably in their time horizons, from almost daily to long time periods, such as 50–100 years. Third, forecasts differ in the scope of what analysts try to forecast. Many forecasts are limited to predicting the value of the main response variable of interest, based entirely on what is known about predictors ex ante or the currently available information. Other forecasts entail more ambitious efforts to forecast larger systems over long time periods, including the relevant predictors of the response. Finally, one can distinguish between model- and data-driven approaches to forecasting. Highly model-driven forecasts are based on simplified theoretical or mathematical models and a priori assumptions, sometimes devoid of any empirical inputs at all. At the other extreme, we have highly data-driven models, based primarily on fitting to observable data, possibly with few assumptions about structure or functional form. In between these two extremes, there is a wide range of forecasting approaches, combining a priori modeling assumptions with parameters either informed by or calibrated by empirical data. This article concludes with an overview of the interests in forecasts among policy and applied communities, including some of the barriers that have prevented effective communication of research results, pointing to future directions of forecasting conflict. Nils W. Metternich acknowledges support from the Economic and Social Research Council (ES/L011506/1). Kristian Skrede Gleditsch acknowledges support from the European Research Council (313373).

General Overviews

Forecasting in international relations has received new attention because of advances in statistical modeling, data collection, and computational performance. In addition, government funding (see O’Brien 2010) has fostered a huge number of new projects. Schneider, et al. 2011 provides an overview of the latest efforts in the introduction of a special issue that also features a helpful categorization of approaches (see Brandt, et al. 2011). Even more recently, Schrodt, et al. 2013 provides a review of advances in the field. The edited volume Choucri and Robinson 1978 shows that interest in forecasting has a long record in the study of international relations, and Feder 1995 highlights an important interest in forecasting applications in the intelligence community.

  • Brandt, Patrick T., John R. Freeman, and Philip A. Schrodt. “Real-time, Time-series Forecasting of Political Conflict.” Conflict Management and Peace Science 28.1 (2011): 41–64.

    DOI: 10.1177/0738894210388125E-mail Citation »

    Part of the above-mentioned special issue (Schneider, et al. 2011), the authors propose a particular forecasting framework as well as a good overview of existing prediction approaches.

  • Choucri, Nazli, and Thomas W. Robinson. Forecasting in International Relations: Theory, Methods, Problems, Prospects. San Francisco: W.H. Freeman, 1978.

    E-mail Citation »

    An edited volume that collects the state-of-the-art forecasting approaches to international conflict prediction at the time of publication, and also alludes to the limitations of forecasting.

  • Feder, Stanley. “Factions and POLICON: New Ways to Analyze Politics.” In Inside CIA’s Private World: Declassified Articles from the Agency’s Internal Journal, 1955–1992. Edited by H. Bradford Westerfield, 274–292. New Haven, CT: Yale University Press, 1995.

    E-mail Citation »

    This chapter reviews the use of prediction models in the US intelligence community, based on declassified information. Some of models described are based on Bueno de Mesquita’s work.

  • O’Brien, Sean P. “Crisis Early Warning and Decision Support: Contemporary Approaches and Thoughts on Future Research.” International Studies Review 12.1 (2010): 87–104.

    DOI: 10.1111/j.1468-2486.2009.00914.xE-mail Citation »

    This article focuses on introducing the DARPA-funded Integrated Crisis Early Warning System (ICEWS) program, but also covers a lot of issues that arise in current forecasting approaches.

  • Schneider, Gerald, Nils Petter Gleditsch, and Sabine Carey. “Forecasting in International Relations: One Quest, Three Approaches.” Conflict Management and Peace Science 28.1 (2011): 5–14.

    DOI: 10.1177/0738894210388079E-mail Citation »

    This is an introduction to a special issue on forecasting in international relations, but at the same time it highlights current and competing approaches to prediction.

  • Schrodt, Philip A., James Yonamine, and Benjamin E. Bagozzi. “Data-Based Computational Approaches to Forecasting Political Violence.” In Handbook of Computational Approaches to Counterterrorism. Edited by V. S. Subrahmanian, 129–162. New York: Springer, 2013.

    DOI: 10.1007/978-1-4614-5311-6_7E-mail Citation »

    Even though this is published in a terrorism handbook, it focuses on conflict studies more generally.

  • Ward, Michael D., Brian D. Greenhill, and Kristin M. Bakke. “The Perils of Policy by P-Value: Predicting Civil Conflicts.” Journal of Peace Research 47.4 (2010): 363–375.

    DOI: 10.1177/0022343309356491E-mail Citation »

    This article highlights the benefits of out-of-sample predictions, rather than judging statistical findings only on the significance of individual variables. It provides a template for current forecasting approaches.

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