International relations is the most predictively oriented subfield of political science…Yet even in the other empirical subfields, the positivist notion that everything must ultimately be reducible to (knowable) universal laws displays its hold in excrescences such as quadrennial attempts to derive formulae for predicting the next presidential election outcome, usually on the basis of ‘‘real’’ (economic) factors. Even if one follows Milton Friedman (1953) in insisting that the factors expressed by such formulae are not supposed to be actually causing electoral outcomes, but are merely variables that (for some unknown reason) allow us to make good behavioral predictions, in practice one usually wants to know what is actually causing the behavior, and it is all too easy to assume that whatever is causing it—since it seems to be responsible for a behavioral regularity—must be some universal human disposition.
That’s from a 2012 paper by Jeffrey Friedman on Robert Jervis’ 1997 System Effects and the “problem of prediction.” I actually enjoyed the paper on the whole, but this passage encapsulates what drives me nuts about what many people—including many social “scientists”—think it means to try to make forecasts about politics.
Contrary to the assertions of some haters, political scientists almost never make explicit forecasts about the things they study—at least not in print or out loud. Some of that reticence presumably results from the fact that there’s no clear professional benefit to making predictions, and there is some professional risk in doing so and then being wrong.
Some of that reticence, though, also seems to flow from this silly but apparently widely-held idea that the very act of forecasting implies that the forecaster accepts the strict positivist premise that “everything must ultimately be reducible to (knowable) universal laws.” To that, I say…
Probability is a mathematical representation of uncertainty, and a probabilistic forecast explicitly acknowledges that we don’t know for sure what’s going to happen. Instead, it’s an educated guess—or, in Bayesian terms, an informed belief.
Forecasters generally use evidence from the past to educate those guesses, but that act of empiricism in itself does not imply that we presume there are universal laws driving political processes lurking beneath that history. Instead, it’s really just a practical solution to the problem of wanting better information—sometimes to help us plan for the future, and sometimes to try to adjudicate between different ideas about the forces shaping those processes now and in the past.
Empiricism is a practical solution because it works—not perfectly, of course, but, for many problems of interest, a lot better than casting bones or reading entrails or consulting oracles. The handful of forecasters I know all embrace the premises that their efforts are only approximations, and that the world can always change in ways that will render the models we find helpful today less helpful in the future. In the meantime, though, we figure we can nibble away at our ignorance by making structured guesses about that future and seeing which ones turn out to be more reliable than the others. Physicists still aren’t entirely sure how planes manage to fly, but millions of us make a prediction every day that the plane we’re about to board is somehow going to manage that feat. We don’t need to be certain of the underlying law to find that prediction useful.
Finally, I can’t resist: there’s real irony in Freidman’s choice of examples of misguided forecasting projects. To have called efforts to predict the outcome of U.S. presidential elections “excrescences” in the year those excrescences had a kind of popular coming out, well, that’s just unfortunate. I guess Friedman didn’t see that one coming.