Shahryar Minhas bio photo

Shahryar Minhas

Assistant Professor, Michigan State University

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victimization

  • Networks of Violence and Civilian Targeting During Civil War – with Cassy Dorff & Max Gallop.
    • Increasingly in recent years, scholarship has reached a consensus that armed groups' decision to kill civilians is strategic in nature. However, most examinations of this choice (and its consequences) assume a simple, dyadic environment where the main conflict is between a government and a unified opposition. Yet, we know empirically that civil wars are more complicated than this, often involving three or more actors. We investigate the strategic incentives for civilian victimization in a complex multi-actor conflict environment using an innovative agent based model. We find that, irrespective of the overall intensity of conflict, more dense strategic environments -- where conflict between any two actors is more likely -- lead to a markedly higher tendency to target civilians by all groups. We empirically test this hypothesis in multi-actor civil wars using ACLED to generate conflict specific measures of both intensity and network density. Preliminary empirical analysis is supportive of our findings that a more dense strategic environment is associated with a higher level of violence against the civilian population.




influencenetworks

  • Estimating Influence in Tensors of Conflict – with Peter D. Hoff and Michael D. Ward.
    • Measuring influence and determining what drives it are persistent questions in political science and in network analysis more generally. Herein we focus on the domain of international relations. Our major substantive question is: How can we determine what characteristics make an actor influential? To address the topic of influence, we build on a multilinear tensor regression framework (MLTR) that captures influence relationships using a tensor generalization of a vector autoregression model. Influence relationships in that approach are captured in a pair of n x n matrices and provide measurements of how the network actions of one actor may influence the future actions of another. A limitation of the MLTR and earlier latent space approaches is that there are no direct mechanisms through which to explain why a certain actor is more or less influential than others. Our new framework, social influence regression, provides a way to statistically model the influence of one actor on another as a function of characteristics of the actors. Thus we can move beyond just estimating that an actor influences another to understanding why. To highlight the utility of this approach, we apply it to studying monthly-level conflictual events between countries as measured through the Integrated Crisis Early Warning System (ICEWS) event data project.