Integrating Theory and Data to Assess Government Policy Options

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Dissident actions including violent protest, terrorist activity, and insurgency demand state responses in order to foster national security. Policy makers and the military are increasingly turning to researchers to help them better formulate and evaluate the interventions they undertake. But the range of potential state interventions is great, comprising anything from diplomatic or informational engagement to economic development or sanctions to military force or police action, and our understanding of how these interventions work is limited. This project will improve our understanding in two ways. First, it will employ cutting-edge Big Data approaches to make predictions as to the likelihoods of different post-intervention outcomes. These predictions will include both the mean effect of the interventions and their variance, allowing policy-makers to use them in cost-benefit calculations. Second, it will provide a novel theoretical undergirding for these predictions. This will greatly enhance the scope of our predictions, in that we will discover how and why the interventions worked as they did, enabling lessons learned to be used in other contexts. In addition to its direct practical and scientific benefit, our research will also show a way forward for theoretically-informed big data applications, and its novel core concepts have the potential for significant uptake across a wide range of fields.We have a limited understanding at present as to both the efficacy of state interventions into substate conflict and the manner in which they function. To improve this understanding we need to develop models of the causal logic by which an intervention designed to work at the individual or community level alters collective dissident mobilization. To make headway on this problem, we tie cutting-edge Big Data approaches to a new type of theoretical model we develop: an integrated theoretical-empirical Bayesian model that assesses the individual-level effects of state interventions and then aggregates individual decisions to predict anti-state action. Our model ties its inputs---individual- and faction-level data---to: (i) theoretical population distributions over incentives to act against the state and (ii) distributions over ensembles of individual-level responses to state interventions. The output of the model is a directly testable probability distribution over predicted anti-state action given a state intervention. As such, the model will produce quantitative predictions on the impact of state interventions that address both the mean effect of the interventions and the variance in their predicted performance. No previous model merges individual-level causal mechanisms, empirical prediction, and direct policy consequences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.