Skyler J. Cranmer

Complex Dependencies in the Alliance Network

The multifaceted and strategic interactions inherent in the formation of international military pacts render the alliance decisions of states highly interdependent. Our aim here is to model the network of alliances in such a way as to capture the effects of covariates and account for the complex dependencies inherent in the network. Regression analysis, due to its foundational assumption of conditional independence, cannot be used to analyze alliance decisions specifically and interdependent decisions generally. We demonstrate how alliance decisions are interdependent and define the problems associated with the regression analysis of nonindependent dyads. We then show that alliances can naturally be conceived of as constituting a network, where alliance formation is an inherently interdependent process. We proceed by introducing the exponential random graph model for analyzing interdependence in the alliance network and estimating the effect of covariates on alliances.

Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks

The exponential random graph model (ERGM) is an increasingly popular method for the statistical analysis of networks that can be used to flexibly analyze the processes by which policy actors organize into a network. Often times, interpretation of ERGM results is conducted at the network level, such that effects are related to overall frequencies of network structures (e.g., the number of closed triangles in a network). This limits the utility of the ERGM because there is often interest, particularly in political and policy sciences, in network dynamics at the actor or relationship levels. Micro-level interpretation of the ERGM has been employed in varied applications in sociology and statistics. We present a comprehensive framework for interpretation of the ERGM at all levels of analysis, which casts network formation as block-wise updating of a network. These blocks can represent, for example, each potential link, each dyad, the out- or in-going ties of each actor, or the entire network. We contrast this interpretive framework with the stochastic actor-based model (SABM) of network dynamics. We present the theoretical differences between the ERGM and the SABM and introduce an approach to comparing the models when theory is not sufficiently strong to make the selection a priori. The alternative models we discuss and the interpretation methods we propose are illustrated on previously published data on estuary policy and governance networks.

Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We address this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges have continuous values (bounded or unbounded), thus greatly expanding the scope of networks applied researchers can subject to statistical analysis.

Toward a Network Theory of Alliance Formation

We propose a network-based theory of alliance formation. Our theory implies that, in addition to key state and dyad attributes already established in the literature, the evolution of the alliance network from any given point in time is largely determined by its structure. Specifically, we argue that closed triangles in the alliance network—where i is allied with j is allied with k is allied with i — produce synergy effects in which state-level utility is greater than the sum of its dyadic parts. This idea can be generalized to n-state closure, and, when considered along with factors that make dyadic alliance formation more attractive, such as military prowess and political compatibility, suggests that the network will evolve toward a state of several densely connected clusters of states with star-like groupings of states as an intermediary stage. To evaluate our theory, we use the temporal exponential random graph model and find that the roles of our network effects are robustly supported by the data, whereas the effects of non-network parameters vary substantially between periods of recent history. Our results indicate that network structure plays a greater role in the formation of alliance ties than has been previously understood in the literature.

Reciprocity and the structural determinants of the international sanctions network

Abstract The imposition of international economic sanctions is a strategic and often multilateral phenomenon of states attempting to coerce each other into altering their behavior by means of economic pain. The interlocking connections of states issuing sanctions and being sanctioned creates a network of interdependent relations and, we argue, the structure of dependencies endogenous to the network is a major determinant of the networks formation and persistence. We consider endogenous structures, both theoretically and empirically, with three foci: the tendency to sanction frequently, the tendency to be sanctioned frequently, and, most of all, reciprocity. The empirical support we find for each of these processes adds a new dimension to our existing knowledge of the sanctioning process, casts doubt upon some previous findings, and opens important avenues for future research.

Kantian fractionalization predicts the conflict propensity of the international system

Significance Many studies in international relations have investigated relationships between pairs of countries and the likelihood of conflict, yet none have connected the overall structure of the network of relationships between countries with the total level of international conflict. Here, we blaze a new path in the study of international conflict by introducing a measure of the overall fractionalization in the network of international relationships which we call Kantian fractionalization and demonstrating that this measure has been closely correlated with the number of new international conflicts in the following year. Moreover, we show that jointly democratic pairs of countries contribute negligibly to Kantian fractionalization, casting doubt on one of the most prominent concepts in international relations and policy prescriptions in Washington. Network science has spurred a reexamination of relational phenomena in political science, including the study of international conflict. We introduce a new direction to the study of conflict by showing that the multiplex fractionalization of the international system along three key dimensions is a powerful predictor of the propensity for violent interstate conflict. Even after controlling for well-established conflict indicators, our new measure contributes more to model fit for interstate conflict than all of the previously established measures combined. Moreover, joint democracy plays little, if any, role in predicting system stability, thus challenging perhaps the major empirical finding of the international relations literature. Lastly, the temporal variability of our measure with conflict is consistent with a causal relationship. Our results have real-world policy implications as changes in our fractionalization measure substantially aid the prediction of conflict up to 10 years into the future, allowing it to serve as an early warning sign of international instability.

Forecasting the locational dynamics of transnational terrorism: a network analytic approach

AbstractEfforts to combat and prevent transnational terrorism rely, to a great extent, on the effective allocation of security resources. Critical to the success of this allocation process is the identification of the likely geopolitical sources and targets of terrorism. We construct the network of transnational terrorist attacks, in which source (sender) and target (receiver) countries share a directed edge, and we evaluate a network analytic approach to forecasting the geopolitical sources and targets of terrorism. We integrate a deterministic, similarity-based, link prediction framework into a probabilistic modeling approach in order to develop an edge-forecasting method. Using a database of over 12,000 transnational terrorist attacks occurring between 1968 and 2002, we show that probabilistic link prediction is not only capable of accurate forecasting during a terrorist campaign, but is a promising approach to forecasting the onset of terrorist hostilities between a source and a target.

The heritability of foreign policy preferences.

Attitudes towards foreign policy have typically been explained by ideological and demographic factors. We approach this study from a different perspective and ex amine the extent to which foreign policy preferences correspond to genetic variation. Using data from the Minnesota Twin Family Study, we show that a moderate share of individual differences in the degree to which ones foreign policy preferences are hawkish or dovish can be attributed to genetic variation. We also show, based on a bivariate twin model, that foreign policy preferences share a common genetic source of variation with political ideology. This result presents the possibility that ideology may be the causal pathway through which genes affect foreign policy preferences.

Stochastic Weighted Graphs: Flexible Model Specification and Simulation

In most domains of network analysis researchers consider networks that arise in nature with weighted edges. Such networks are routinely dichotomized in the interest of using available methods for statistical inference with networks. The generalized exponential random graph model (GERGM) is a recently proposed method used to simulate and model the edges of a weighted graph. The GERGM specifies a joint distribution for an exponential family of graphs with continuous-valued edge weights. However, current estimation algorithms for the GERGM only allow inference on a restricted family of model specifications. To address this issue, we develop a Metropolis -- Hastings method that can be used to estimate any GERGM specification, thereby significantly extending the family of weighted graphs that can be modeled with the GERGM. We show that new flexible model specifications are capable of avoiding likelihood degeneracy and efficiently capturing network structure in applications where such models were not previously available. We demonstrate the utility of this new class of GERGMs through application to two real network data sets, and we further assess the effectiveness of our proposed methodology by simulating non-degenerate model specifications from the well-studied two-stars model. A working R version of the GERGM code is available in the supplement and will be incorporated in the gergm CRAN package.

Demography, Democracy and Disputes: The Search for the Elusive Relationship Between Population Growth and International Conflict

We examine the propensity of states to initiate international conflict conditioned on four primary explanatory variables: (1) changes in population over varying lags, (2) democratic status of the state, (3) the power status of the state, and (4) changes in the states level of energy consumption. We hypothesize that the responsiveness of a government to the needs of its citizens is sufficiently important that the effect of population growth cannot be properly examined independently of democracy and that major powers tend to become involved in disputes for a much wider set of reasons than minor powers. Thus, we expect to find the strongest effect of population change on conflict initiation in democratic minor powers. We also expect that decreases in energy consumption concurrent with increases in population will lead to conflict initiation. A series of negative binomial regressions over 20 yearly time lags lends robust support to our expectations.