In his recent Washington Post article “America Needs a Council of International Strategy”, David Laitin accurately makes the case for “better analysis of data, trends, and context…” to help policy makers within the government make more informed international policy decisions. He recommends the creation of a “team of strategic analysts capable of using the statistical models of the forecasters…” so that policy makers can explore global events and related policy options. As someone who helped build exactly that kind of analytic team within the government–and then saw it eliminated–I learned plenty of lessons about obstacles to implementation. Instead of creating yet another analytic organization, we should focus on refining current analytic institutions within the Intelligence Community and Department of Defense (which I’ll refer to as the Community) to fill the gap Dr. Laitin identifies.
As Dr. Laitin rightly notes, there does not exist (to my knowledge) a place within the Community that contains a group of government civilian scholars focused on quantitative modeling to inform strategic level decisions. While there are pockets of these capabilities, they are small and disparate. One reason for this gap is a bias against quantitative social science analyses. This can be partially traced to the false promise of many quantitative models that proved to either be incongruent with the operational pace of the Community or were simply based on faulty or obsolete theory and data. These models often contained proprietary black-box modeling techniques, and thus were impossible to fully comprehend. Because of this, quantitative analyses that truly accommodate academia’s technical rigor as well as the Community’s expedited analytic pace continue to be met with skepticism. I still recall a task in which our team responded to a question from the highest levels of military leadership. Our quantitatively-derived findings – which at the time were counterintuitive but have since been proven out – were never included in the final presentation to leadership. Quite simply, domain expertise trumped quantitative analyses then, and it still does today.
Second, there is a bias in the Community for tactical, real-time analysis over longer-term strategic thinking. This is partly due to an incentive structure that focuses on inputs into daily briefs and quick-turn responses in lieu of longer-term, strategic analyses. This is not a surprise given real-world demand for real-time responses. However, in my experience talking to various levels of leadership within the Community, there is also demand for strategic insights. In fact, as you move up the leadership chain, these kinds of analyses become ever more important to help inform global resource allocation, effects assessments, and planning.
Academia is equally culpable for the gap Dr. Laitin identifies. First, there remains a faulty assumption that scholarship can only be rigorous or policy relevant, and not both. This was evident at this year’s American Political Science Association (APSA) Annual Meeting. To provide just one thematic example, cyber analyses across the soft/hard power spectrum were practically non-existent among the over one thousand panels. Academic leaders, just like their government counterparts, similarly need to adapt the discipline for the challenges of the modern era.
Finally, there needs to be greater academic support outside of the beltway for rigorous, policy relevant research and career tracks. Given the academic job market over the last decade, academic leadership should also encourage, not deter, graduate students from pursuing non-academic positions, including in the government analytic community.
I adamantly agree with Dr. Laitin’s acknowledgement that policy makers require greater access to independent, quantitatively driven and probabilistic policy alternatives. However, the best way to fill this gap is from the inside, not from the creation of yet another new organization. Let’s complement and refine the extant analytic institutions such that they too can conduct and make relevant the quantitatively-derived strategic analyses Dr. Laitin describes.