Predictive models, which includes forecasting models, are used to study all types of conflict and political violence, including civil wars, international conflict, terrorism, genocide, and protests. These models are defined as those where the researcher explicitly values predictive performance when building and analyzing the model. This is different from inferential models, where the researcher values the accurate operationalization of a theory, and experimental or quasi-experimental designs where the focus is on the estimation of a causal effect. Researchers employ preditive models to guide policy, to assess the importance of variables, to test and compare theories, and for the development of research methods. In addition to these practical applications, there are more fundamental arguments, rooted in the philosophy of science, as to why these models should be used to advance conflict research. Their use has led to numerous substantive findings. For example, while inferential models largely support the democratic peace hypothesis, predictive models have shown mixed results and have been used to refine the scope of the argument. Among the more robust findings are the presence of nonlinear relationships and the importance of dependencies in all types of conflict data. These findings have implications for how researchers model conflict processes. As predictive models become more common and more integrated into the study of conflict, it is important that researchers understand their underlying components to use them appropriately.
As human tragedies—such as armed conflicts, humanitarian crises, crimes against humanity, and genocide—continue to occur, early warning and conflict prevention are essential comprehensive subjects in any crisis and conflict prevention architecture. Early warning refers to the collection and analysis of information about potential crisis and conflict situations for the purpose of preventing the onset and escalation of such situations, preferably through appropriate preventive response options. Indeed, qualitative approaches to early warning and prevention have produced an impressive list of preventive mechanisms and tools, ranging from non-military—such as political and economic inducements, fact-finding, dialogue, and negotiations—to military ones, such as preventive missions. Meanwhile, a more theoretical and empirically guided approach has made extensive use of quantitative methods to create data-based predictive models for assessing risks of complex humanitarian crises, political instability and state failure, intrastate and ethnopolitical conflicts, and genocide and politicide, as well as other massive human rights violations. There are three types of analysis of risk assessment: the first makes use of structural indicators, the second of sequential models, and the third of inductive methods. However, there are challenges in early warning and conflict prevention posed by the warning-response gap and the issue of “missed opportunities” to prevent. At present, there is no U.N.-wide coordinated early warning system. Nevertheless, several efforts in establishing operational early warning systems on the level of regional and subregional organizations can be identified.