I am interested in how humans make decisions, especially within political organizations (such as bureaucracies and states) that deal with ongoing uncertainty and conflicting interests. My earlier work in this area focused on the politics and organizational dynamics of U.S. intelligence agencies, and culminated in my book, The CIA and the Politics of U.S. Intelligence Reform.
My main ongoing research projects address two distinct topics. The first, currently titled Cooked: A Political History of Data, explains how new data collection and analysis technologies have changed politics and society throughout history. The second, How to Make the World More Liberal (with Naazneen Barma and Steve Weber), argues that today's global challenges demand a new way of thinking about how to achieve liberal outcomes, such as human rights, self-determination, and baseline human security.
I also continue to work on intelligence and forecasting organizations, with a particular focus on how secret agencies can be held accountable, and how individual analysts can be assessed in future-oriented work.
See below for more information on each of these projects.
Cooked: A Politics History of Data
This book project considers the social and political effects of innovations in data technologies, from ancient times to the Big Data revolution. The influence of data analytic tools is evident in nearly every aspect of our 21st-century lives, yet their overall effects on our world are poorly understood. I place these developments in their broader context by considering how new approaches to collecting and analyzing data have influenced societies and shifted political power throughout history. (This research has been supported by the Mellon Foundation.)
How to Make the World More Liberal
This co-authored book project (with Naazneen Barma and Steve Weber) puts forward a new strategy to advance liberal values and outcomes on a global scale. First, we develop a pragmatic definition of core liberal objectives based on five values: fairness, protection, competition, innovation, and pluralism. We then propose a practical way to advance liberal outcomes through social contracts grounded in reciprocity and reasonableness – rather than more typical approaches based on rational utility maximization. Finally, we apply the reciprocal model to three contemporary issues that are among the most important emerging problems in 21st century global politics: economic inequality, human displacement, and a lack of shared understandings about truth and knowledge. These cases highlight the need for creative experimentation to achieve liberal outcomes, rather than a continued reliance on outdated institutions and modes of thinking. (This project is under advance contract with Oxford University Press, and has been supported by the Berggruen Institute.)
The first topic grows out of research I have done on principal-agent problems in intelligence oversight. In my current book project, I discuss how the secret nature of intelligence belies traditional approaches to accountability and oversight. More than any other policy domain, I argue, strategic intelligence creates severe information asymmetries between principals and agents, and exhibits a lack of the outside issue publics and media scrutiny that would be necessary for “fire alarm” oversight. In future research, I will consider how different governments and organizations have sought to overcome these problems. In addition to research completed for my book, I have been collecting data for this project during several visits to England, where I have spent weeks in the British archives and conducted more than a dozen interviews with parliamentarians and senior government officials. While I have been primarily focused on qualitative methods for understanding accountability, I have also begun exploring ways to represent and evaluate these information dynamics using formal modeling.
Performance Metrics in Information Organizations
The second area is closely related to the first, but concerns information and accountability at the micro-organizational level. Information-based organizations, in both the public and private sectors, face a difficult task in evaluating the quality of their analysts, particularly those who are asked to make probabilistic predictions. For example, consider when an intelligence analyst forecasts that a terrorist attack is very likely, but the attack does not come: does this reflect poor analysis, the non-occurrence of a high-probability event, or perhaps even a successful preventive response set in motion by the prediction itself? Three challenges set information-based organizations apart from other types of producers and service providers: (1) monitoring problems (it is difficult to measure how hard or well someone is thinking); (2) probabilistic analysis (by definition, the accuracy of probabilistic analysis cannot be measured ex post, since any relevant eventuality is accounted for with probability >0); and (3) constant environmental change (the likelihood of a given event can change quickly, and can even be influenced by the prediction itself, as in the example above). A preliminary survey has found a broad spectrum of performance metrics in these types of organizations. Hedge funds, for example, tend to assess performance exclusively based on outcomes: poor returns mean poor performance, regardless of the quality of the analysis. Many strategic intelligence organizations focus instead on analysts’ behavior, evaluating performance based on tradecraft and proper analytical techniques. Investment banks and management consulting firms often fall somewhere in between. Studying how these different metrics influence analyst performance will be valuable for understanding accountability in a host of public organizations, from strategic intelligence and law enforcement to disaster prevention and response.