Forecasting Round-Up No. 3

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 overfi tting, 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.

N.B. In case you’re wondering, you can find Forecasting Round-Up Nos. 1 and 2 here and here.

Forecasting Round-Up No. 2

N.B. This is the second in an occasional series of posts I’m expecting to do on forecasting miscellany. You can find the first one here.

1. Over at Bad Hessian a few days ago, Trey Causey asked, “Where are the predictions in sociology?” After observing how the accuracy of some well-publicized forecasts of this year’s U.S. elections has produced “growing public recognition that quantitative forecasting models can produce valid results,” Trey wonders:

If the success of these models in forecasting the election results is seen as a victory for social science, why don’t sociologists emphasize the value of prediction and forecasting more? As far as I can tell, political scientists are outpacing sociologists in this area.

I gather that Trey intended his post to stimulate discussion among sociologists about the value of forecasting as an element of theory-building, and I’m all for that. As a political scientist, though, I found myself focusing on the comparison Trey drew between the two disciplines, and that got me thinking again about the state of forecasting in political science. On that topic, I had two brief thoughts.

First, my simple answer to why forecasting is getting more attention from political scientists that it used to is: money! In the past 20 years, arms of the U.S. government dealing with defense and intelligence seem to have taken a keener interest in using tools of social science to try to anticipate various calamities around the world. The research program I used to help manage, the Political Instability Task Force (PITF), got its start in the mid-1990s for that reason, and it’s still alive and kicking. PITF draws from several disciplines, but there’s no question that it’s dominated by political scientists, in large part because the events it tries to forecast—civil wars, mass killings, state collapses, and such—are traditionally the purview of political science.

I don’t have hard data to back this up, but I get the sense that the number and size of government contracts funding similar work has grown substantially since the mid-1990s, especially in the past several years. Things like the Department of Defense’s Minerva Initiative; IARPA’s ACE Program; the ICEWS program that started under DARPA and is now funded by the Office of Naval Research; and Homeland Security’s START consortium come to mind. Like PITF, all of these programs are interdisciplinary by design, but many of the topics they cover have their theoretical centers of gravity in political science.

In other words, through programs like these, the U.S. government is now spending millions of dollars each year to generate forecasts of things political scientists like to think about. Some of that money goes to private-sector contractors, but some of it is also flowing to research centers at universities. I don’t think any political scientists are getting rich off these contracts, but I gather there are bureaucratic and career incentives (as well as intellectual ones) that make the contracts rewarding to pursue. If that’s right, it’s not hard to understand why we’d be seeing more forecasting come out of political science than we used to.

My second reaction to Trey’s question is to point out that there actually isn’t a whole lot of forecasting happening in political science, either. That might seem like it contradicts the first, but it really doesn’t. The fact is that forecasting has long been pooh-poohed in academic social sciences, and even if that’s changing at the margins in some corners of the discipline, it’s still a peripheral endeavor.

The best evidence I have for this assertion is the brief history of the American Political Science Association’s Political Forecasting Group. To my knowledge—which comes from my participation in the group since its establishment—the Political Forecasting Group was only formed several years ago, and its membership is still too small to bump it up to the “organized section” status that groups representing more established subfields enjoy. What’s more, almost all of the panels the group has sponsored so far have focused on forecasts of U.S. elections. That’s partly because those papers are popular draws in election years, but it’s also because the group’s leadership has had a really hard time finding enough scholars doing forecasting on other topics to assemble panels.

If the discipline’s flagship association in one of the countries most culturally disposed to doing this kind of work has trouble cobbling together occasional panels on forecasts of things other than elections, then I think it’s fair to say that forecasting still isn’t a mainstream pursuit in political science, either.

2. Speaking of U.S. election forecasting, Drew Linzer recently blogged a clinic in how statistical forecasts should be evaluated. Via his web site, Votamatic, Drew:

1) began publishing forecasts about the 2012 elections well in advance of Election Day (so there couldn’t be any post hoc hemming and hawing about what his forecasts really were);

2) described in detail how his forecasting model works;

3) laid out a set of criteria he would use to judge those forecasts after the election; and then

4) walked us through his evaluations soon after the results were (mostly) in.

Oh, and in case you’re wondering: Drew’s model performed very well, thank you.

3. But you know what worked a little better than Drew’s election-forecasting model, and pretty much everyone else’s, too? An average of the forecasts from several of them. As it happens, this pattern is pretty robust. A well-designed statistical model is great for forecasting, but an average of forecasts from a number of them is usually going to be even better. Just ask the weather guys.

4. Finally, for those of you—like me—who want to keep holding pundits’ feet to the fire long after the election’s over, rejoice that Pundit Tracker is now up and running, and they even have a stream devoted specifically to politics. Among other things, they’ve got John McLaughlin on the record predicting that Hillary Clinton will win the presidency in 2016, and that President Obama will not nominate Susan Rice to be Secretary of State. McLaughlin’s hit rate so far is a rather mediocre 49 percent (18 of 37 graded calls correct), so make of those predictions what you will.

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