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.

Government formation in multiparty systems is of self-evident substantive importance, and the subject of an enormous theoretical literature. Empirical evaluations of models of government formation tend to separate government formation per se, from the distribution of key government payoffs such as cabinet portfolios between members of the government that forms. Models of government formation are necessarily specified ex ante, absent any knowledge of the government that forms. Models of the distribution of cabinet portfolios are typically, though not necessarily, specified ex post, given knowledge of the identity of some government "formateur" or even of the full partisan composition of the eventual cabinet. This disjunction lies at the heart of a notorious contradiction between predictions of the distribution of cabinet portfolios made by canonical models of legislative bargaining, and the robust empirical regularity of proportional portfolio allocations -"Gamson's Law". We resolve this contradiction by specifying and estimating a joint model of cabinet formation and portfolio distribution, which for example predicts ex ante which parties will receive zero portfolios rather than taking this as given ex post.We conclude that canonical models of legislative bargaining do add to our ability to predict government membership, but that portfolio distribution between government members conforms robustly to a proportionality norm … we suggest because portfolio distribution follows the much more difficult process of policy bargaining in the typical government formation process.Cabinet formation and portfolio distribution in European multi-party systems / 2

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.

The rise of social network analyses in the social sciences has allowed empirical work to better account for interdependencies among actors and among their actions. However, this work has been, to a large extent, descriptive: it has treated these actions as exogenous and immutable. In many cases these networks describe actions like alliance formation or trade phenomena that are the outcome variables for programs of social scientific research. In this paper, I attempt to account for both interdependencies and the endogenous nature of networks by incorporating formal theory; helping answer the question of how these networks arise by looking at the incentives of actors to form links with each other. I discuss the appropriate solution concept for a network formation game, and present an algorithm for finding the equilibrium of these networks computationally as well as ways to compare the theoretical networks to observed ones in order to evaluate the fit of the theory. I apply these methods to the study of international cooperation a subject where both the interdependencies and purposive nature of actors must be accounted for. The theoretical network is able to reproduce a number of important observed characteristics. Still, there are more factors that must be accounted for if we want to understand how the network of international cooperation is formed