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Understanding the causes of interstate conflict continues to be a primary goal of the field of international relations. To that end, scholars continue to rely on large datasets of conflict in the international system. This paper introduces the latest iteration in the most widely used dataset on interstate conflicts, the Militarized Interstate Dispute (MID) 4 data. In this paper we first outline the updated data-collection process for the MID4 data. Second, we present some minor changes and clarifications to the coding rules for the MID4 datasets, as well as pointing out how the MID coding procedures affect several notable \"close call\" cases. Third, we introduce updates to the existing MID datasets for the years 2002–2010 and provide descriptive statistics that allow comparisons of the newer MID data to prior versions. We also offer some best practices and point out several ways in which the new MID data can contribute to research in international conflict.

Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted. A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models, which are often generated by the generally applicable bootstrap method. In this work, we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty but that it can be simply calibrated to dramatically improve its accuracy. We demonstrate the effectiveness of this calibration method for both synthetic data and numerous physical datasets from the field of Materials Science and Engineering. The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models.

Much of the data used to measure conflict is extracted from news reports. This is typically accomplished using either expert coders to quantify the relevant information or machine coders to automatically extract data from documents. Although expert coding is costly, it produces quality data. Machine coding is fast and inexpensive, but the data are noisy. To diminish the severity of this tradeoff, we introduce a method for analyzing news documents that uses crowdsourcing, supplemented with computational approaches. The new method is tested on documents about Militarized Interstate Disputes, and its accuracy ranges between about 68 and 76 percent. This is shown to be a considerable improvement over automated coding, and to cost less and be much faster than expert coding.

Dealing with questions of war and peace and understanding the causes of interstate conflict is a primary goal of the field of international relations. In order to study interstate conflict in a rigorous manner, scholars have relied on established rules and procedures for gathering information into coherent data sets. Among those data sets is the Militarized Interstate Dispute (MID) data. In this paper we first outline the data-collection process for the MID3 data. Second, we introduce two new data sets emerging from the project, \"MID-I\" and \"MID-IP.\" Third, we present relatively small changes in coding rules for the new MID3 data and some descriptive statistics. The statistics indicate that the MID3 data are remarkably similar to the MID2.1 version, varying in some minor and predictable ways.

This book presents a general explanation of how states develop their foreign policy. The theory stands in contrast to most approaches--which assume that states want to maximize security--by assuming that states pursue two things, or goods, through their foreign policy: change and maintenance. States, in other words, try both to change aspects of the international status quo that they don't like and maintain those aspects they do like. A state's ability to do so is largely a function of its relative capability, and since national capability is finite, a state must make trade-offs between policies designed to achieve change or maintenance.

How and why do regime type and interstate war affect government spending? We argue that a political leader allocates scarce resources between social and military expenditures as a function of their relative efficiency in securing her political survival. We derive four hypotheses concerning how mobilization for and demobilization from interstate war affects government spending differently in democratic and autocratic regimes. Compared to democracies, autocracies should increase military spending to a greater degree during wartime and decrease military spending to a greater extent following a war. Autocracies also should cut social spending more during an interstate war and increase social spending more during the process of demobilization from war than democracies. Our analyses of all states in the international system from 1950 to 2001 yield support for our hypotheses.

How does increasing military spending affect social spending and economic growth? We argue leaders vary in their preferences over how to pay for military spending and failing to account for interdependence among methods of government finance, government spending, and economic performance limits scholars' ability to identify the consequences of military spending. We use vector autoregressive models to estimate the relationships among military spending, social spending, economic growth, tax revenue, debt, and the money supply in the United States between 1947 and 2007. We find that increasing military spending has a nonlinear effect on economic growth that varies over time and the existence of a guns-versus-butter trade-off is conditional on the relative importance leaders place on protecting the social welfare state, borrowing money, and keeping taxes low when increasing military spending.

Consideration of the effects of domestic politics on international conflict behavior often consists simply of contrasting democracies with non-democracies. One notable exception is work that links economic performance and the use of force. This link has often been addressed through use of a diversionary perspective. In this paper, we argue that more important than the alleged incentive to pursue a rallying effect when times are bad are domestic political and economic factors affecting leaders' constraints, representing some of the costs to pursuing adventurous foreign policies. We examine three sources of constraints on democratic leaders: the willingness of the constituency to support the use of force internationally; the macroeconomic preferences of the party's constituency; and an interaction of those preferences with the state of the economy. We find that in developed democracies, the political orientation of the government is a significant factor affecting the likelihood of international conflict initiation. Specifically, right governments are more likely to initiate interstate disputes; economic conditions have a significant but lesser impact. [PUBLICATION ABSTRACT]

Human exposure to complex, changing, and variably correlated mixtures of environmental chemicals has presented analytical challenges to epidemiologists and human health researchers. There has been a wide variety of recent advances in statistical methods for analyzing mixtures data, with most methods having open-source software for implementation. However, there is no one-size-fits-all method for analyzing mixtures data given the considerable heterogeneity in scientific focus and study design. For example, some methods focus on predicting the overall health effect of a mixture and others seek to disentangle main effects and pairwise interactions. Some methods are only appropriate for cross-sectional designs, while other methods can accommodate longitudinally measured exposures or outcomes. This article focuses on simplifying the task of identifying which methods are most appropriate to a particular study design, data type, and scientific focus. We present an organized workflow for statistical analysis considerations in environmental mixtures data and two example applications implementing the workflow. This systematic strategy builds on epidemiological and statistical principles, considering specific nuances for the mixtures' context. We also present an accompanying methods repository to increase awareness of and inform application of existing methods and new methods as they are developed. We note several methods may be equally appropriate for a specific context. This article does not present a comparison or contrast of methods or recommend one method over another. Rather, the presented workflow can be used to identify a set of methods that are appropriate for a given application. Accordingly, this effort will inform application, educate researchers (e.g., new researchers or trainees), and identify research gaps in statistical methods for environmental mixtures that warrant further development. https://doi.org/10.1289/EHP16791.

Governments can finance the higher military spending associated with interstate war through a combination of cutting nonmilitary spending, imposing higher taxes, borrowing, and adopting an inflationary monetary policy. We argue that the incentives of survival-motivated leaders influence the strategies governments use to fund their war efforts and that regime type conditions the use of some finance strategies. Consistent with our expectations, we find that fighting an interstate war is associated with greater reductions in nonmilitary spending in dictatorships than in democracies and that contemporary democracies and dictatorships have largely avoided financing their wars through tax increases and inflation. We find little support for the argument that democracies finance their interstate wars through greater debt than nondemocracies.