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.