Developing political forecasting models not only increases the ability of political scientists to inform public policy decisions, but is also relevant for scientific advancement. This article argues for and demonstrates the utility of creating forecasting models for predicting political conflicts in a diverse range of country settings. Apart from the benefit of making actual predictions, we argue that predictive heuristics are one gold standard of model development in the field of conflict studies. As such, they shed light on an array of important components of the political science literature on conflict dynamics. We develop and present conflict predictions that have been highly accurate for past and subsequent events, exhibiting few false-negative and false-positive categorizations. Our predictions are made at the monthly level for 6-month periods into the future, taking into account the social-spatial context of each individual country. The model has a high degree of accuracy in reproducing historical data measured monthly over the past 10 years and has approximately equal accuracy in making forecasts. Thus, forecasting in political science is increasingly accurate. At the same time, by providing a gold standard that separates model construction from model evaluation, we can defeat observational research designs and use true prediction as a way to evaluate theories. We suggest that progress in the modeling of International Studies Review (2013) 15, 473-490 conflict research depends on the use of prediction as a gold standard of heuristic evaluation.
In this article, we combine a game-theoretic treatment of public goods provision in networks with a statistical network analysis to show that fragmented opposition network structures lead to an increase in conflictual actions. Current literature concentrates on the dyadic relationship between the government and potential challengers. We shift the focus toward exploring how network structures affect the strategic behavior of political actors. We derive and examine testable hypotheses and use latent space analysis to infer actors' positions vis-à-vis each other in the network. Network structure is examined and used to test our hypotheses with data on conflicts in Thailand from 2001 to 2010. We show the influential role of network structure in generating conflictual behavior.
Previous literature has shown a link between violent victimization and pro-sociopolitical behavior. This study asks why victimization is shown to increase the likelihood of political participation in regions of ongoing armed conflict. I argue that previous answers to this question have overlooked a key variable for predicting civilian behavior: individual-level social context. As a step forward in connecting social networks to behavior outcomes, I present the kinship network as a novel measure for proxying an individual’s valuable and private social interactions. Building on previous victimization literature, I suggest that to comprehensively understand the effects of victimization, scholars should account for social context. Specifically, I examine the hypothesis that as kinship ties strengthen, victimization positively influences the likelihood of political participation. To test this argument, I turn to the Mexican criminal conflict. I use original survey data of 1,000 respondents collected in July 2012 from the ongoing drug war in Mexico, and in doing so, I find that kinship plays a key role in motivating political participation during armed conflicts in that survivors of criminal violence with strong ties to kinship networks are the most likely to participate in political groups; these results are robust to state-level fixed effects and are unlikely to be driven by victimization selection bias.
Civil conflicts are complex: multiple warring parties compete for control of territory both against each other and the government. These processes are often dynamic; changing over time and space. In this study, we embrace these complexities through a network based approach. By considering important relational patterns, such as reciprocity and transitivity, and tying them together with existing theoretical developments in the conflict processes literature, we answer the question of 'who fights whom and when' during civil conflict. Further, using the case of Nigeria, we offer novel theoretical insights about how the entrance of a new, aggressive actor can decisively alter the trajectory of conflict. In addition, we show that our approach is better at predicting 'who fights whom and when' in an out-of-sample context than extant approaches.
We forecast Irregular Leadership Changes (ILC)-unexpected leadership changes in contravention of a state's established laws and conventions-for mid-2014 using predictions generated from an innovative ensemble model that is composed of several split-population duration regression models. This approach uses distinct thematic models, combining them into one aggregate forecast developed on the basis of their predictive accuracy and uniqueness. The data are based on 45 ILCs that occurred from
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