The Central Intelligence Agency’s armed Remotely Piloted Vehicle-supported counter-insurgency campaign in Pakistan – a mission undermined by unintended consequences?

This paper views America's 'drones-first' counter-insurgency effort in Pakistan through the lens of Merton's theory of the unintended consequences of purposive action. It also references Beck’s Risk Society thesis, America’s Revolution in Military Affairs doctrine, Toft’s theory of isomorphic learning, Langer’s theory of mindfulness, Highly Reliable Organisations theory and the social construction of technology (SCOT) argument. With reference to Merton’s theory, the CIA-directed armed Remotely Piloted Vehicle (RPV) campaign has manifest functions, latent functions and latent dysfunctions. Measured against numbers of suspected insurgents killed, the campaign can be judged a success. Measured against the level of collateral damage or the state of US-Pakistan relations, the campaign can be judged a failure. Values determine the choice of metrics. Because RPV operations eliminate risk to American service personnel, and because this is popular with both US citizens and politicians, collateral damage (the killing of civilians) is not considered a policy-changing dysfunction. However, the latent dysfunctions of America's drones-first policy may be so great as to undermine that policy's intended manifest function – to make a net contribution to the War on Terror. In Vietnam the latent dysfunctions of Westmoreland’s attritional war undermined America’s policy of containment. Vietnam holds a lesson for the Obama administration.Publisher PD

A Bayesian inference approach for determining player abilities in football

We consider the task of determining a football player's ability for a given event type, for example, scoring a goal. We propose an interpretable Bayesian model which is fit using variational inference methods. We implement a Poisson model to capture occurrences of event types, from which we infer player abilities. Our approach also allows the visualisation of differences between players, for a specific ability, through the marginal posterior variational densities. We then use these inferred player abilities to extend the Bayesian hierarchical model of Baio and Blangiardo (2010) which captures a team's scoring rate (the rate at which they score goals). We apply the resulting scheme to the English Premier League, capturing player abilities over the 2013/2014 season, before using output from the hierarchical model to predict whether over or under 2.5 goals will be scored in a given game in the 2014/2015 season. This validates our model as a way of providing insights into team formation and the individual success of sports teams.Comment: 31 pages, 14 figure