1. Mike Ward and six colleagues recently posted a new working paper on “the next generation of crisis prediction.” The paper echoes themes that Mike and Nils Metternich sounded in a recent Foreign Policy piece responding to one I wrote a few days earlier, about the challenges of forecasting rare political events around the world. Here’s a snippet from the paper’s intro:
We argue that conflict research in political science can be improved by more, not less, attention to predictions. The increasing availability of disaggregated data and advanced estimation techniques are making forecasts of conflict more accurate and precise. In addition, we argue that forecasting helps to prevent overfitting, and can be used both to validate models, and inform policy makers.
I agree with everything the authors say about the scientific value and policy relevance of forecasting, and I think the modeling they’re doing on civil wars is really good. There were two things I especially appreciated about the new paper.
First, their modeling is really ambitious. In contrast to most recent statistical work on civil wars, they don’t limit their analysis to conflict onset, termination, or duration, and they don’t use country-years as their unit of observation. Instead, they look at country-months, and they try to tackle the more intuitive but also more difficult problem of predicting where civil wars will be occurring, whether or not one is already ongoing.
This version of the problem is harder because the factors that affect the risk of conflict onset might not be the same ones that affect the risk of conflict continuation. Even when they are, those factors might not affect the two risks in inverse ways. As a result, it’s hard to specify a single model that can reliably anticipate continuity in, and changes from, both forms of the status quo (conflict or no conflict).
The difficulty of this problem is evident in the out-of-sample accuracy of the model these authors have developed. The performance statistics are excellent on the whole, but that’s mostly because the model is accurately forecasting that whatever is happening in one month will continue to happen in the next. Not surprisingly, the model’s ability to anticipate transitions is apparently weaker. Of the five civil-war onsets that occurred in the test set, only two “arguably…rise to probability levels that are heuristic,” as the authors put it.
I emailed Mike to ask about this issue, and he said they were working on it:
Although the paper doesn’t go into it, in a separate part of this effort we actually do have separate models for onset and continuation, and they do reasonably well. We are at work on terminations, and developing a new methodology that predicts onsets, duration, and continuation in a single (complicated!) model. But that is down the line a bit.
Second and even more exciting to me, the authors close the paper with real, honest-to-goodness forecasts. Using the most recent data available when the paper was written, the authors generate predicted probabilities of civil war for the next six months: October 2012 through March 2013. That’s the first time I’ve seen that done in an academic paper about something other than an election, and I hope it sets a precedent that others will follow.
2. Over at Red (team) Analysis, Helene Lavoix appropriately pats The Economist on the back for publicly evaluating the accuracy of the predictions they made in their “World in 2012″ issue. You can read the Economist‘s own rack-up here, but I want to highlight one of the points Helene raised in her discussion of it. Toward the end of her post, in a section called “Black swans or biases?”, she quotes this bit from the Economist:
As ever, we failed at big events that came out of the blue. We did not foresee the LIBOR scandal, for example, or the Bo Xilai affair in China or Hurricane Sandy.
As Helene argues, though, it’s not self evident that these events were really so surprising—in their specifics, yes, but not in the more general sense of the possibility of events like these occurring sometime this year. On Sandy, for example, she notes that
Any attention paid to climate change, to the statistics and documents produced by Munich-re…or Allianz, for example, to say nothing about the host of related scientific studies, show that extreme weather events have become a reality and we are to expect more of them and more often, including in the so-called rich countries.
This discussion underscores the importance of being clear about what kind of forecasting we’re trying to do, and why. Sometimes the specifics will matter a great deal. In other cases, though, we may have reason to be more concerned with risks of a more general kind, and we may need to broaden our lens accordingly. Or, as Helene writes,
The methodological problem we are facing here is as follows: Are we trying to predict discrete events (hard but not impossible, however with some constraints and limitations according to cases) or are we trying to foresee dynamics, possibilities? The answer to this question will depend upon the type of actions that should follow from the anticipation, as predictions or foresight are not done in a vacuum but to allow for the best handling of change.
3. Last but by no means least, Edge.org has just posted an interview with psychologist Phil Tetlock about his groundbreaking and ongoing research on how people forecast, how accurate (or not) their forecasts are, and whether or not we can learn to do this task better. [Disclosure: I am one of hundreds of subjects in Phil’s contribution to the IARPA tournament, the Good Judgment Project.] On the subject of learning, the conventional wisdom is pessimistic, so I was very interested to read this bit (emphasis added):
Is world politics like a poker game? This is what, in a sense, we are exploring in the IARPA forecasting tournament. You can make a good case that history is different and it poses unique challenges. This is an empirical question of whether people can learn to become better at these types of tasks. We now have a significant amount of evidence on this, and the evidence is that people can learn to become better [forecasters]. It’s a slow process. It requires a lot of hard work, but some of our forecasters have really risen to the challenge in a remarkable way and are generating forecasts that are far more accurate than I would have ever supposed possible from past research in this area.
And bonus alert: the interview is introduced by Daniel Kahneman, Nobel laureate and author of one of my favorite books from the past few years, Thinking, Fast and Slow.