Some Suggested Readings for Political Forecasters

A few people have recently asked me to recommend readings on political forecasting for people who aren’t already immersed in the subject. Since the question keeps coming up, I thought I’d answer with a blog post. Here, in no particular order, are books (and one article) I’d suggest to anyone interested in the subject.

Thinking, Fast and Slow, by Daniel Kahneman. A really engaging read on how we think, with special attention to cognitive biases and heuristics. I think forecasters should read it in hopes of finding ways to mitigate the effects of these biases on their own work, and of getting better at spotting them in the thinking of others.

Numbers Rule Your World, by Kaiser Fung. Even if you aren’t going to use statistical models to forecast, it helps to think statistically, and Fung’s book is the most engaging treatment of that topic that I’ve read so far.

The Signal and the Noise, by Nate Silver. A guided tour of how forecasters in a variety of fields do their work, with some useful general lessons on the value of updating and being an omnivorous consumer of relevant information.

The Theory that Would Not Die, by Sharon Bertsch McGrayne. A history of Bayesian statistics in the real world, including successful applications to some really hard prediction problems, like the risk of accidents with atomic bombs and nuclear power plants.

The Black Swan, by Nicholas Nassim Taleb. If you can get past the derisive tone—and I’ll admit, I initially found that hard to do—this book does a great job explaining why we should be humble about our ability to anticipate rare events in complex systems, and how forgetting that fact can hurt us badly.

Expert Political Judgment: How Good Is It? How Can We Know?, by Philip Tetlock. The definitive study to date on the limits of expertise in political forecasting and the cognitive styles that help some experts do a bit better than others.

Counterfactual Thought Experiments in World Politics, edited by Philip Tetlock and Aaron Belkin. The introductory chapter is the crucial one. It’s ostensibly about the importance of careful counterfactual reasoning to learning from history, but it applies just as well to thinking about plausible futures, an important skill for forecasting.

The Foundation Trilogy, by Isaac Asimov. A great fictional exploration of the Modernist notion of social control through predictive science. These books were written half a century ago, and it’s been more than 25 years since I read them, but they’re probably more relevant than ever, what with all the talk of Big Data and the Quantified Self and such.

The Perils of Policy by P-Value: Predicting Civil Conflicts,” by Michael Ward, Brian Greenhill, and Kristin Bakke. This one’s really for practicing social scientists, but still. The point is that the statistical models we typically construct for hypothesis testing often won’t be very useful for forecasting, so proceed with caution when switching between tasks. (The fact that they often aren’t very good for hypothesis testing, either, is another matter. On that and many other things, see Phil Schrodt’s “Seven Deadly Sins of Contemporary Quantitative Political Analysis.“)

I’m sure I’ve missed a lot of good stuff and would love to hear more suggestions from readers.

And just to be absolutely clear: I don’t make any money if you click through to those books or buy them or anything like that. The closest thing I have to a material interest in this list are ongoing professional collaborations with three of the authors listed here: Phil Tetlock, Phil Schrodt, and Mike Ward.

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.

It’s Not Just The Math

This week, statistics-driven political forecasting won a big slab of public vindication after the U.S. election predictions of an array of number-crunching analysts turned out to be remarkably accurate. As John Sides said over at the Monkey Cage, “2012 was the Moneyball election.” The accuracy of these forecasts, some of them made many months before Election Day,

…shows us that we can use systematic data—economic data, polling data—to separate momentum from no-mentum, to dispense with the gaseous emanations of pundits’ “guts,” and ultimately to forecast the winner.  The means and methods of political science, social science, and statistics, including polls, are not perfect, and Nate Silver is not our “algorithmic overlord” (a point I don’t think he would disagree with). But 2012 has showed how useful and necessary these tools are for understanding how politics and elections work.

Now I’ve got a short piece up at Foreign Policy explaining why I think statistical forecasts of world politics aren’t at the same level and probably won’t be very soon. I hope you’ll read the whole thing over there, but the short version is: it’s the data. If U.S. electoral politics is a data hothouse, most of international politics is a data desert. Statistical models make very powerful forecasting tools, but they can’t run on thin air, and the density and quality of the data available for political forecasting drops off precipitously as you move away from U.S. elections.

Seriously: you don’t have to travel far in the data landscape to start running into trouble. In a piece posted yesterday, Stephen Tall asks rhetorically why there isn’t a British Nate Silver and then explains that it’s because “we [in the U.K.] don’t have the necessary quality of polls.” And that’s the U.K., for crying out loud. Now imagine how things look in, say, Ghana or Sierra Leone, both of which are holding their own national elections this month.

Of course, difficult does not mean impossible. I’m a bit worried, actually, that some readers of that Foreign Policy piece will hear me saying that most political forecasting is still stuck in the Dark Ages, when that’s really not what I meant. I think we actually do pretty well with statistical forecasting on many interesting problems in spite of the dearth of data, as evidenced by the predictive efforts of colleagues like Mike Ward and Phil Schrodt and some of the work I’ve posted here on things like coups and popular uprisings.

I’m also optimistic that the global spread of digital connectivity and associated developments in information-processing hardware and software are going to help fill some of those data gaps in ways that will substantially improve our ability to forecast many political events. I haven’t seen any big successes along those lines yet, but the changes in the enabling technologies are pretty radical, so it’s plausible that the gains in data quality and forecasting power will happen in big leaps, too.

Meanwhile, while we wait for those leaps to happen, there are some alternatives to statistical models that can help fill some of the gaps. Based partly on my own experiences and partly on my read of relevant evidence (see here, here, and here for a few tidbits), I’m now convinced that prediction markets and other carefully designed systems for aggregating judgments can produce solid forecasts. These tools are most useful in situations where the outcome isn’t highly predictable but relevant information is available to those who dig for it. They’re somewhat less useful for forecasting the outcomes of decision processes that are idiosyncratic and opaque, like North Korean government or even the U.S. Supreme Court. There’s no reason to let the perfect be the enemy of the good, but we should use these tools with full awareness of their limitations as well as their strengths.

More generally, though, I remain convinced that, when trying to forecast political events around the world, there’s a complexity problem we will never overcome no matter how many terabytes of data we produce and consume, how fast our processors run, and how sophisticated our methods become. Many of the events that observers of international politics care about are what Nassim Nicholas Taleb calls “gray swans”—”rare and consequential, but somewhat predictable, particularly to those who are prepared for them and have the tools to understand them.”

These events are hard to foresee because they bubble up from a complex adaptive system that’s constantly evolving underfoot. The patterns we think we discern in one time and place can’t always be generalized to others, and the farther into the future we try to peer, the thinner those strands get stretched. Events like these “are somewhat tractable scientifically,” as Taleb puts it, but we should never expect to predict their arrival the way we can foresee the outcomes of more orderly processes like U.S. elections.

In Defense of Political Science and Forecasting

Under the headline “Political Scientists Are Lousy Forecasters,” today’s New York Times includes an op-ed by Jacqueline Stevens that takes a big, sloppy swipe at most of the field. The money line:

It’s an open secret in my discipline: in terms of accurate political predictions (the field’s benchmark for what counts as science), my colleagues have failed spectacularly and wasted colossal amounts of time and money.

As she sees it, this poor track record is an inevitability. Referencing the National Science Foundation‘s history of funding research in which she sees little value, Stevens writes:

Government can—and should—assist political scientists, especially those who use history and theory to explain shifting political contexts, challenge our intuitions and help us see beyond daily newspaper headlines. Research aimed at political prediction is doomed to fail. At least if the idea is to predict more accurately than a dart-throwing chimp.

I don’t have much time to write today, so I was glad to see this morning that Henry Farrell has already penned a careful rebuttal that mirrors my own reactions. On the topic of predictions in particular, Farrell writes:

The claim here—that “accurate political prediction” is the “field’s benchmark for what counts as science” is quite wrong. There really isn’t much work at all by political scientists that aspires to predict what will happen in the future…It is reasonable to say that the majority position in political science is a kind of soft positivism, which focuses on the search for law-like generalizations. But that is neither a universal benchmark (I, for one, don’t buy into it), nor indeed, the same thing as accurate prediction, except where strong covering laws (of the kind that few political scientists think are generically possible) can be found.

To Farrell’s excellent rebuttals, I would add a couple of things.

First and most important, there’s a strong case to be made that political scientists don’t engage in enough forecasting and really ought to do more of it. Contrary to Stevens’ assertion in that NYT op-ed, most political scientists eschew forecasting, seeing description and explanation as the goals of their research instead. As Phil Schrodt argues in “Seven Deadly Sins of Quantitative Political Science” (PDF), however, to the extent that we see our discipline as a form of science, political scientists ought to engage in forecasting, because prediction is an essential part of the scientific method.

Explanation in the absence of prediction is not somehow scienti cally superior to predictive analysis, it isn’t scienti c at all! It is, instead, “pre-scientific.”

In a paper on predicting civil conflicts, Mike Ward, Brian Greenhill, and Kristin Bakke help to explain why:

Scholars need to make and evaluate predictions in order to improve our models. We have to be willing to make predictions explicitly – and plausibly be wrong, even appear foolish – because our policy prescriptions need to be undertaken with results that are drawn from robust models that have a better chance of being correct. The whole point of estimating risk models is to be able to apply them to specific cases. You wouldn’t expect your physician to tell you that all those cancer risk factors from smoking don’t actually apply to you. Predictive heuristics provide a useful, possibly necessary, strategy that may help scholars and policymakers guard against erroneous recommendations.

Second, I think Stevens actually gets the historical record wrong. It drives me crazy when I see people take the conventional wisdom about a topic—say, the possibility of the USSR’s collapse, or a wave of popular uprisings in Middle East and North Africa—and turn it into a blanket statement that “no one predicted X.” Most of the time, we don’t really know what most people would have predicted, because they weren’t asked to make predictions. The absence of a positive assertion that X will happen is not the same thing as a forecast that X will not happen. In fact, in at least one of the cases Stevens discusses—the USSR’s collapse—we know that some observers did forecast its eventual collapse, albeit usually without much specificity about the timing of that event.

More generally, I think it’s fair to say that, on just about any topic, there will be a distribution of forecasts—from high to low, impossible to inevitable, and so on. Often, that distribution will have a clear central tendency, as did expectations about the survival of authoritarian regimes in the USSR or the Arab world, but that central tendency should not be confused with a consensus. Instead, this divergence of expectations is precisely where the most valuable information will be found. Eventually, some of those predictions will prove correct while others will not, and, as Phil and Mike and co. remind us, that variation in performance tells us something very useful about the power of the explanatory models—quantitative, qualitative, it doesn’t really matter—from which they were derived.

PS. For smart rebuttals to other aspects of Steven’s jeremiad, see Erik Voeten’s post at the Monkey Cage and Steve Saideman’s rejoinder at Saideman’s Semi-Spew.

PPS. Stevens provides some context for her op-ed on her own blog, here. (I would have added this link sooner, but I’ve just seen it myself.)

PPPS. For some terrific ruminations on uncertainty, statistics, and scientific knowledge, see this latecomer response from Anton Strezhnev.

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