Loving Tetlock’s Superforecasting: The Art and Science of Prediction
I was a big fan of Philip Tetlock’s Expert Political Judgment (EPJ). I read it thoroughly; in fact a book review of it was my first academic publication. It was very influential on me.
EPJ is a book that is troubling to many political experts because it basically says that most so-called political expertise is bogus and that what isn’t bogus is fairly limited. It makes this argument with far more meticulous data collection and argumentation than I am able to do justice to here. I found it completely persuasive and inspiring. It wasn’t until I got to Berkeley that I met people who had vivid negative emotional reactions to this work. They seem to mainly have been political experts who do not having their expertise assessed in terms of its predictive power.
Superforecasting: The Art and Science of Prediction (2016) is a much more accessible book that summarizes the main points from EPJ and then discusses the results of Tetlock’s Good Judgment Project, which was his answer to an IARPA challenge in forecasting political events.
Much of the book is an interesting history of the United States Intelligence Community (IC) and the way its attitudes towards political forecasting have evolved. In particular, the shock of the failure of the predictions around Weapons of Mass Destruction that lead to the Iraq War were a direct cause of IARPA’s interest in forecasting and their funding of the Good Judgment Project despite the possibility that the project’s results would be politically challenging. IARPA comes out looking like a very interesting and intellectually honest organization solving real problems for the people of the United States.
Reading this has been timely for me because: (a) I’m now doing what could be broadly construed as “cybersecurity” work, professionally, (b) my funding is coming from U.S. military and intelligence organizations, and (c) the relationship between U.S. intelligence organizations and cybersecurity has been in the news a lot lately in a very politicized way because of the DNC hacking aftermath.
Since so much of Tetlock’s work is really just about applying mathematical statistics to the psychological and sociological problem of developing teams of forecasters, I see the root of it as the same mathematical theory one would use for any scientific inference. Cybersecurity research, to the extent that it uses sound scientific principles (which it must, since it’s all about the interaction between society, scientifically designed technology, and risk), is grounded in these same principles. And at its best the U.S. intelligence community lives up to this logic in its public service.
The needs of the intelligence community with respect to cybersecurity can be summed up in one word: rationality. Tetlock’s work is a wonderful empirical study in rationality that’s a must-read for anybody interested in cybersecurity policy today.