Suchergebnisse

Scholars of civil wars have long argued that non-state actors can use selective punishment to reduce collaboration with state adversaries. However, there is little systematic evidence confirming this claim, nor investigation into the mechanisms at play. In this paper, we provide such evidence from the drone war in Pakistan. Militants in Pakistan's tribal areas engaged in a brutal counterespionage campaign with the aim of reducing collaboration with the United States. Our analysis combines a novel dataset of collaborator killings with data on drone strike outcomes. We find that strikes killed half as many militant leaders and fighters following collaborator killings and that this suppressive effect likely works by deterring spying in the future. Beyond providing an empirical confirmation of the selective punishment hypothesis, our paper suggests an unacknowledged vulnerability of the drone program to reprisals against local allies and collaborators that limits its effectiveness as a long-term tool of counterterrorism.

High rates of missing perpetrator information in political violence data pose a serious challenge for studies into militant group behavior and the microdynamics of conflict more generally. In this article we introduce multiple imputation (MI) as the best available method for minimizing the impact of missing perpetrator information on quantitative analyses of political violence, a method that can easily be incorporated into most quantitative research designs. MI will produce unbiased attributions when the reasons for missingness are known and can be controlled for using observed variables, rendering responsibility for unclaimed attacks, "missing at random" (MAR) – which we show is a reasonable assumption in the case of political violence based on current theory of militant group claiming. We lay out the logics and steps of MI, identify variables and data sources, and demonstrate that MI produced better results in the case of the Pakistani Taliban's response to drone strikes.

Scholars of civil wars have long argued that non-state actors can use selective punishment to reduce collaboration with state adversaries. However, there is little systematic evidence confirming this claim, nor investigation into the mechanisms at play. In this paper, we provide such evidence from the drone war in Pakistan. Militants in Pakistan's tribal areas engaged in a brutal counterespionage campaign with the aim of reducing collaboration with the United States. Our analysis combines a novel dataset of collaborator killings with data on drone strike outcomes. We find that strikes killed half as many militant leaders and fighters following collaborator killings and that this suppressive effect likely works by deterring spying in the future. Beyond providing an empirical confirmation of the selective punishment hypothesis, our paper suggests an unacknowledged vulnerability of the drone program to reprisals against local allies and collaborators that limits its effectiveness as a long-term tool of counterterrorism.

High rates of missing perpetrator information in political violence data pose a serious challenge for studies into militant group behavior and the microdynamics of conflict more generally. In this article we introduce multiple imputation (MI) as the best available method for minimizing the impact of missing perpetrator information on quantitative analyses of political violence, a method that can easily be incorporated into most quantitative research designs. MI will produce unbiased attributions when the reasons for missingness are known and can be controlled for using observed variables, rendering responsibility for unclaimed attacks, "missing at random" (MAR) – which we show is a reasonable assumption in the case of political violence based on current theory of militant group claiming. We lay out the logics and steps of MI, identify variables and data sources, and demonstrate that MI produced better results in the case of the Pakistani Taliban's response to drone strikes.

Abstract
Purpose
Police officers in the USA are often put in harm's way when responding to calls for service. This paper provides a demonstration of concept for how machine learning procedures combined with conformal prediction inference can be properly used to forecast the amount of risk associated with each dispatch. Accurate forecasts of risk can help improve officer safety.

Methods
The unit of analysis is each of 1928 911 calls involving weapons offenses. Using data from the calls and other information, we develop a machine learning algorithm to forecast the risk that responding officers will face. Uncertainty in those forecasts is captured by nested conformal prediction sets.

Results
For approximately a quarter of a holdout sample of 100 calls, a forecast of high risk was correct with the odds of at least 3 to 1. For approximately another quarter of the holdout sample, a forecast of low risk was correct with an odds of at least 3 to 1. For remaining cases, insufficiently reliable forecasts were identified. A result of "can't tell" is an appropriate assessment when the data are deficient.

Conclusions
Compared to current practice at the study site, we are able to forecast with a useful level of accuracy the risk for police officers responding to calls for service. With better data, such forecasts could be substantially improved. We provide examples.