One Measure By Which Things Have Recently Gotten Worse

The United Nation’s refugee agency today released its annual report on people displaced by war around the world, and the news is bad:

The number of people forcibly displaced at the end of 2014 had risen to a staggering 59.5 million compared to 51.2 million a year earlier and 37.5 million a decade ago.

The increase represents the biggest leap ever seen in a single year. Moreover, the report said the situation was likely to worsen still further.

The report focuses on raw estimates of displaced persons, but I think it makes more sense to look at this group as a share of world population. The number of people on the planet has increased by more than half a billion in the past decade, so we might expect to see some growth in the number of forcibly displaced persons even if the amount of conflict worldwide had held steady. The chart below plots annual totals from the UNHCR report as a share of mid-year world population, as estimated by the U.S. Census Bureau (here).


The number of observations in this time series is too small to use Bayesian change point detection to estimate the likelihood that the upturn after 2012 marks a change in the underlying data-generating process. I’m not sure we need that kind of firepower, though. After holding more or less steady for at least six years, the share of world population forcibly displaced by war has increased by more than 50 percent in just two years, from about one of every 200 people to 1 of every 133 people. Equally important, reports from field workers indicate that this problem only continues to grow in 2015. I don’t think I would call this upturn a “paradigm change,” as UN High Commissioner for Refugees António Guterres did, but there is little doubt that the problem of displacement by war has worsened significantly since 2012.

In historical terms, just how bad is it? Unfortunately, it’s impossible to say for sure. The time series in the UNHCR report only starts in 2004, and a note warns that methodological changes in 2007 render the data before that year incomparable to the more recent estimates. The UNHCR describes the 2014 figure as “the highest level ever recorded,” and that’s technically true but not very informative when recording started only recently. A longer time series assembled by the Center for Systemic Peace (here) supports the claim that the latest raw estimate is the largest ever, but as a share of world population, it’s probably still a bit lower than the levels seen in the post–Cold War tumult of the early 1990s (see here).

Other relevant data affirm the view that, while clearly worsening, the intensity of armed conflict around the world is not at historically high levels, not even for the past few decades. Here is a plot of annual counts of battle-related deaths (low, high, and best estimates) according to the latest edition of UCDP’s data set on that topic (here), which covers the period 1989–2013. Note that these figures have not been adjusted for changes in world population.

Annual estimates of battle-related deaths worldwide, 1989-2013 (data source: UCDP)

Annual estimates of battle-related deaths worldwide, 1989-2013 (data source: UCDP)

We see similar pattern in the Center for Systemic Peace’s Major Episodes of Political Violence data set (second row here), which covers the whole post-WWII period. For the chart below, I have separately summed the data set’s scalar measure of conflict intensity for two types of conflict, civil and interstate (see the codebook for details). Like the UCDP data, these figures show a local increase in the past few years that nevertheless remains well below the prior peak, which came when the Soviet Union fell apart.

Annual intensity of political violence worldwide, 1946-2014 (data source: CSP)

Annual intensity of political violence worldwide, 1946-2014 (data source: CSP)

And, for longer-term perspective, it always helps to take another look at this one, from an earlier UCDP report:

PRIO battle death trends

I’ll wrap this up by pinning a note in something I see when comparing the shorter-term UCDP estimates to the UNHCR estimates on forcibly displaced persons: adjusting for population, it looks like armed conflicts may be killing fewer but displacing more than they used to. That impression is bolstered by a glance at UCDP data on trends in deaths from “intentional attacks on civilians by governments and formally organized armed groups,” which UCDP calls “one-sided violence” (here).  As the plot below shows, the recent upsurge in warfare has not yet produced a large increase in the incidence of these killings, either. The line is bending upward, but it remains close to historical lows.

Estimated annual deaths from one-sided violence, 1989-2013 (Source: UCDP)

Estimated annual deaths from one-sided violence, 1989-2013 (Source: UCDP)

So, in the tumult of the past few years, it looks like the rate of population displacement has surged while the rate of battle deaths has risen more slowly and the rate of one-sided violence targeting civilians hasn’t risen much at all. If that’s true, then why? Improvements in medical care in conflict zones are probably part of the story, but I wonder if changes in norms and values, and in the international institutions and practices instantiating them, aren’t also shaping these trends. Governments that in the past might have wantonly killed populations they regarded as threats now seem more inclined to press those populations by other means—not always, but more often. Meanwhile, international organizations are readier than ever to assist those groups under pressure by feeding and sheltering them, drawing attention to their miseries, and sometimes even protecting them. The trend may be fragile, and the causality is impossible to untangle with confidence, but it deserves contemplation.

To Realize the QDDR’s Early-Warning Goal, Invest in Data-Making

The U.S. Department of State dropped its second Quadrennial Diplomacy and Development Review, or QDDR, last week (here). Modeled on the Defense Department’s Quadrennial Defense Review, the QDDR lays out the department’s big-picture concerns and objectives so that—in theory—they can guide planning and shape day-to-day decision-making.

The new QDDR establishes four main goals, one of which is to “strengthen our ability to prevent and respond to internal conflict, atrocities, and fragility.” To help do that, the State Department plans to “increase [its] use of early warning analysis to drive early action on fragility and conflict.” Specifically, State says it will:

  1. Improve our use of tools for analyzing, tracking, and forecasting fragility and conflict, leveraging improvements in analytical capabilities;
  2. Provide more timely and accurate assessments to chiefs of mission and senior decision-makers;
  3. Increase use of early warning data and conflict and fragility assessments in our strategic planning and programming;
  4. Ensure that significant early warning shifts trigger senior-level review of the mission’s strategy and, if necessary, adjustments; and
  5. Train and deploy conflict-specific diplomatic expertise to support countries at risk of conflict or atrocities, including conflict negotiation and mediation expertise for use at posts.

Unsurprisingly, that plan sounds great to me. We can’t now and never will be able to predict precisely where and when violent conflict and atrocities will occur, but we can assess risks with enough accuracy and lead time to enable better strategic planning and programming. These forecasts don’t have to be perfect to be earlier, clearer, and more reliable than the traditional practices of deferring to individual country or regional analysts or just reacting to the news.

Of course, quite a bit of well-designed conflict forecasting is already happening, much of it paid for by the U.S. government. To name a few of the relevant efforts: The Political Instability Task Force (PITF) and the Worldwide Integrated Conflict Early Warning System (W-ICEWS) routinely update forecasts of various forms of political crisis for U.S. government customers. IARPA’s Open Source Indicators (OSI) and Aggregative Contingent Estimation (ACE) programs are simultaneously producing forecasts now and discovering ways to make future forecasts even better. Meanwhile, outside the U.S. government, the European Union has recently developed its own Global Conflict Risk Index (GCRI), and the Early Warning Project now assesses risks of mass atrocities in countries worldwide.

That so much thoughtful risk assessment is being done now doesn’t mean it’s a bad idea to start new projects. If there are any iron laws of forecasting hard-to-predict processes like political violence, one of them is that combinations of forecasts from numerous sources should be more accurate than forecasts from a single model or person or framework. Some of the existing projects already do this kind of combining themselves, but combinations of combinations will often be even better.

Still, if I had to channel the intention expressed in this part of the QDDR into a single activity, it would not be the construction of new models, at least not initially. Instead, it would be data-making. Social science is not Newtonian physics, but it’s not astrology, either. Smart people have been studying politics for a long time, and collectively they have developed a fair number of useful ideas about what causes or precedes violent conflict. But, if you can’t track the things those theorists tell you to track, then your forecasts are going to suffer. To improve significantly on the predictive models of political violence we have now, I think we need better inputs most of all.

When I say “better” inputs, I have a few things in mind. In some cases, we need to build data sets from scratch. When I was updating my coup forecasts earlier this year, a number of people wondered why I didn’t include measures of civil-military relations, which are obviously relevant to this particular risk. The answer was simple: because global data on that topic don’t exist. If we aren’t measuring it, we can’t use it in our forecasts, and the list of relevant features that falls into this set is surprisingly long.

In other cases, we need to revive them. Social scientists often build “boutique” data sets for specific research projects, run the tests they want to run on them, and then move on to the next project. Sometimes, the tests they or others run suggest that some features captured in those data sets would make useful predictors. Those discoveries are great in principle, but if those data sets aren’t being updated, then applied forecasters can’t use that knowledge. To get better forecasts, we need to invest in picking up where those boutique data sets left off so we can incorporate their insights into our applications.

Finally and in almost all cases, we need to observe things more frequently. Most of the data available now to most conflict forecasters is only updated once each year, often on a several-month delay and sometimes as much as two years later (e.g., data describing 2014 becomes available in 2016). That schedule is fine for basic research, but it is crummy for applied forecasting. If we want to be able to give assessments and warnings that as current as possible to those “chiefs of mission and senior decision-makers” mentioned in the QDDR, then we need to build models with data that are updated as frequently as possible. Daily or weekly are ideal, but monthly updates would suffice in many cases and would mark a huge improvement over the status quo.

As I said at the start, we’re never going to get models that reliably tell us far in advance exactly where and when violent conflicts and mass atrocities will erupt. I am confident, however, that we can assess these risks even more accurately than we do now, but only if we start making more, and better versions, of the data our theories tell us we need.

I’ll end with a final plea to any public servants who might be reading this: if you do invest in developing better inputs, please make the results freely available to the public. When you share your data, you give the crowd a chance to help you spot and fix your mistakes, to experiment with various techniques, and to think about what else you might consider, all at no additional cost to you. What’s not to like about that?

A Note on Trends in Armed Conflict

In a report released earlier this month, the Project for the Study of the 21st Century (PS21) observed that “the body count from the top twenty deadliest wars in 2014 was more than 28% higher than in the previous year.” They counted approximately 163 thousand deaths in 2014, up from 127 thousand in 2013. The report described that increase as “part of a broader multi-year trend” that began in 2007. The project’s executive director, Peter Epps, also appropriately noted that “assessing casualty figures in conflict is notoriously difficult and many of the figures we are looking at here a probably underestimates.”

This is solid work. I do not doubt the existence of the trend it identifies. That said, I would also encourage us to keep it in perspective:

That chart (source) ends in 2005. Uppsala University’s Department of Peace and Conflict (UCDP) hasn’t updated its widely-used data set on battle-related deaths for 2014 yet, but from last year’s edition, we can see the tail end of that longer period, as well as the start of the recent upward trend PS21 identifies. In this chart—R script here—the solid line marks the annual, global sums of their best estimates, and the dotted lines show the sums of the high and low estimates:
Annual, global battle-related deaths, 1989-2013 (source: UCDP)

Annual, global battle-related deaths, 1989-2013 (Data source: UCDP)

If we mentally tack that chart onto the end of the one before it, we can also see that the increase of the past few years has not yet broken the longer spell of relatively low numbers of battle deaths. Not even close. The peak around 2000 in the middle of the nearer chart is a modest bump in the farther one, and the upward trend we’ve seen since 2007 has not yet matched even that local maximum. This chart stops at the end of 2013, but if we used the data assembled by PS21 for the past year to project an increase in 2014, we’d see that we’re still in reasonably familiar territory.

Both of these things can be true. We could be—we are—seeing a short-term increase that does not mark the end of a longer-term ebb. The global economy has grown fantastically since the 1700s, and yet it still suffers serious crises and recessions. The planet has warmed significantly over the past century, but we still see some unusually cool summers and winters.

Lest this sound too sanguine at a time when armed conflict is waxing, let me add two caveats.

First, the picture from the recent past looks decidedly worse if we widen our aperture to include deliberate killings of civilians outside of battle. UCDP keeps a separate data set on that phenomenon—here—which they label “one-sided” violence. If we add the fatalities tallied in that data set to the battle-related ones summarized in the previous plot, here is what we get:

Annual, global battle-related deaths and deaths from one-sided violence, 1989-2013 (Data source: UCDP)

Annual, global battle-related deaths and deaths from one-sided violence, 1989-2013 (Data source: UCDP)

Note the difference in the scale of the y-axis; it is an order of magnitude larger than the one in the previous chart. At this scale, the peaks and valleys in battle-related deaths from the past 25 years get smoothed out, and a single peak—the Rwandan genocide—dominates the landscape. That peak is still much lower than the massifs marking the two World Wars in the first chart, but it is huge nonetheless. Hundreds of thousands of people were killed in a matter of months.

Second, the long persistence of this lower rate does not prove that the risk of violent conflict on the scale of the two World Wars has been reduced permanently. As Bear Braumoeller (here) and Nassim Nicholas Taleb (here; I link reluctantly, because I don’t care for the scornful and condescending tone) have both pointed out, a single war between great powers could end or even reverse this trend, and it is too soon to say with any confidence whether or not the risk of that happening is much lower than it used to be. Like many observers of international relations, I think we need to see how the system processes the (relative) rise of China and declines of Russia and the United States before updating our beliefs about the risk of major wars. As someone who grew up during the Cold War and was morbidly fascinated by the possibility of nuclear conflagration, I think we also need to remember how close we came to nuclear war on some occasions during that long spell, and to ponder how absurdly destructive and terrible that would be.

Strictly speaking, I’m not an academic, but I do a pretty good impersonation of one, so I’ll conclude with a footnote to that second caveat: I did not attribute the idea that the risk of major war is a thing of the past to Steven Pinker, as some do, because as Pinker points out in a written response to Taleb (here), he does not make precisely that claim, and his wider point about a long-term decline in human violence does not depend entirely on an ebb in warfare persisting. It’s hard to see how Pinker’s larger argument could survive a major war between nuclear powers, but then if that happened, who would care one way or another if it had?

Occupy Central and the Rising Risk of New Mass Atrocities in China

This is a cross-post from the blog of the Early Warning Project, which I currently direct. The Early Warning Project concentrates on risks of mass atrocities, but this post also draws on my longstanding interest in democratization and social unrest, so I thought I would share it here as well.

Activists have massed by the thousands in central Hong Kong for the past several days in defiance of repeated attempts to disperse them and menacing words from Beijing. This demonstration and the wider Occupy Central movement from which it draws poses one of the sharpest public challenges to Communist Party authority since the Tiananmen Square uprising 25 years ago. In so doing, it clearly raises the risk of a new mass atrocities in China.

Photo credit: AP via BBC News

Photo credit: AP via BBC News

The demonstrations underway now are really just the latest surge in a wave of activism that began in Hong Kong earlier this year. Under the “one country, two systems” framework to which China committed when it regained sovereignty over the then–UK colony in 1997, Hong Kong is supposed to enjoy a great deal of autonomy over local governance. This summer, however, Beijing issued a white paper affirming the central government’s “comprehensive jurisdiction” over Hong Kong, and it blocked plans for open nominations in local elections due in 2017. Those actions spurred (and were spurred by) an unofficial referendum and a mass pro-democracy rally that eventually ebbed from the streets but left behind a strengthened civic movement.

The ongoing demonstrations began with a student boycott of classes a week ago, but they escalated sharply on Friday, when activists began occupying key public spaces in central Hong Kong. Police have made several forceful attempts to disperse or remove the protesters, and official channels have said publicly that Beijing “firmly opposes all illegal activities that could undermine rule of law and jeopardise ‘social tranquility'” in Hong Kong. So far, however, the occupations have proved resilient to those thrusts and threats.

Many observers are now openly wondering how this confrontation will end. For those sympathetic to the protesters, the fear is that Beijing will respond with lethal force, as it did at Tiananmen Square in 1989.

As it happens, the Early Warning Project’s statistical risk assessments do not identify China as a country at relatively high risk of state-led mass killing this year. Partly because of that, we do not currently have a question open on our opinion pool that covers this situation. (Our lone China question focuses on the risk of state-led mass atrocities targeting Uyghurs.)

If we did have a relevant question open on our opinion pool, however, I would be raising my estimate of the risk of a state-led mass killing in response to these developments. I still don’t expect that one will occur, but not because I anticipate that Beijing will concede to the protesters’ demands. Rather, I expect violent repression, but I also doubt that it will cross the 1,000-death threshold we and others use to distinguish episodes of mass killing from smaller-scale and more routine atrocities.

State-led mass killings as we define them usually occur when incumbent rulers perceive potentially existential threats to their authority. Following leading theories on the subject, our statistical analysis concentrates on armed insurgencies and coups as the forms those threats typically take. Authoritarian governments often suppress swelling demonstrations with violence as well, but those crackdowns rarely kill as many as 1,000 nonviolent protesters, who usually disperse long before that threshold is reached. Even the Tiananmen Square massacre probably fell short of this threshold, killing “only” hundreds of activists before achieving the regime’s goal of dispersing the occupation and setting an example that would frighten future dissenters.

Instead, violent state crackdowns usually push countries onto one of three other pathways before they produce more than 1,000 fatalities: 1) they succeed at breaking the uprising and essentially restore the status quo ante (e.g., China in 1989, Uzbekistan in 2005Burma in 2007, and Thailand in 2010); 2) they suppress the nonviolent challenge but, in so doing, help to spawn a violent rebellion that may or may not be met with a mass killing of its own (e.g., Syria since 2011); or 3) they catalyze splits in state security forces or civilian rulers that lead to negotiations, reforms, or regime collapse (e.g., Egypt and Tunisia in 2011). In short, nonviolent uprisings usually lose, transform, or win before the attempts to suppress them amount to what we would call a state-led mass killing.

In Hong Kong right now, the first path—successful repression—appears to be the most likely. Chinese Communist Party leaders have spoken openly in recent years about trying to learn from the mistakes that led to collapse of the Soviet Union, and the mixed signals that were sent to early risers in the USSR—some protests were repressed, but others were allowed to run their course or met with modest concessions—probably rank high on their list of things to avoid. Those Party leaders also know that activists and separatists elsewhere in China are closely watching events in Hong Kong and would probably take encouragement from anything short of a total defeat for Occupy Central. These considerations generate strong incentives to try to quash the current challenge.

In contrast, the second of those three trajectories—a transformation to violent insurgency in response to state repression—seems highly unlikely. Protesters have shown a strong commitment to nonviolence so far and have strategic as well as ideological reasons to continue to do so; after all, the People’s Liberation Army is about as formidable a foe as they come. Brutal state repression might radicalize some survivors and inspire other onlookers, but Hong Kong is a wealthy, urban enclave with minimal access to arms, so a turn toward violent rebellion would face tall structural obstacles.

The third of those trajectories also seems unlikely, albeit somewhat less so than the second. The Communist Party currently faces several profound challenges: a slowing rate of economic growth and widespread concern about a looming financial crisis; an escalating insurgency in Xinjiang; and an epidemic of local protests over pollution, to name just a few. Meanwhile, Xi Jinping’s anti-corruption campaign is creating new fissures within the country’s ruling class, and rumors of dissent within the military have swirled occasionally in the past two years as well. As I discussed in a recent blog post, consolidated single-party regimes like China’s usually weather these kinds of challenges. When they do break down, however, it almost always happens in times like these, when worried insiders start to fight among themselves and form alliances with emboldened popular challengers.

Put those considerations together, and it seems that Beijing is most likely to respond to Occupy Central with a crackdown that could be lethal but probably will not cross the 1,000-death threshold we use to distinguish episodes of mass killing from more routine political violence. It seems less likely but still possible that the prospect or occurrence of such a crackdown will catalyze the kinds of elite splits that could finally produce significant political reform or sustained instability in China. Under none of these circumstances would I expect the challenge in Hong Kong to evolve into an armed rebellion that might produce a new wave of atrocities of its own.

No matter what the immediate outcome, though, it seems increasingly clear that China has entered a period of “thickened history,” as Marc Beissinger calls it, in which national politics will remain more eventful and less certain for some time to come.

Machine learning our way to better early warning on mass atrocities

For the past couple of years, I’ve been helping build a system that uses statistics and expert crowds to assess and track risks of mass atrocities around the world. Recently dubbed the Early Warning Project (EWP), this effort already has a blog up and running (here), and the EWP should finally be able to launch a more extensive public website within the next several weeks.

One of the first things I did for the project, back in 2012, was to develop a set of statistical models that assess risks of onsets of state-led mass killing in countries worldwide, the type of mass atrocities for which we have the most theory and data. Consistent with the idea that the EWP will strive to keep improving on what it does as new data, methods, and ideas become available, that piece of the system has continued to evolve over the ensuing couple of years.

You can find the first two versions of that statistical tool here and here. The latest iteration—recently codified in new-and-improved replication materials—has performed pretty well, correctly identifying the few countries that have seen onsets of state-led mass killing in the past couple of years as relatively high-risk cases before those onsets occurred. It’s not nearly as precise as we’d like—I usually apply the phrase “relatively high-risk” to the Top 30, and we’ll only see one or two events in most years—but that level of imprecision is par for the course when forecasting rare and complex political crises like these.

Of course, a solid performance so far doesn’t mean that we can’t or shouldn’t try to do even better. Last week, I finally got around to applying a couple of widely used machine learning techniques to our data to see how those techniques might perform relative to the set of models we’re using now. Our statistical risk assessments come not from a single model but from a small collection of them—a “multi-model ensemble” in applied forecasting jargon—because these collections of models usually produce more accurate forecasts than any single one can. Our current ensemble mixes two logistic regression models, each representing a different line of thinking about the origins of mass killing, with one machine-learning algorithm—Random Forests—that gets applied to all of the variables used by those theory-specific models. In cross-validation, the Random Forests forecasts handily beat the two logistic regression models, but, as is often the case, the average of the forecasts from all three does even better.

Inspired by the success of Random Forests in our current risk assessments and by the power of machine learning in another project on which I’m working, I decided last week to apply two more machine learning methods to this task: support vector machines (SVM) and the k-nearest neighbors (KNN) algorithm. I won’t explain the two techniques in any detail here; you can find good explanations elsewhere on the internet (see here and here, for example), and, frankly, I don’t understand the methods deeply enough to explain them any better.

What I will happily report is that one of the two techniques, SVM, appears to perform our forecasting task about as well as Random Forests. In five-fold cross-validation, both SVM and Random Forests both produced areas under the ROC curve (a.k.a. AUC scores) in the mid-0.80s. AUC scores range from 0.5 to 1, and a score in the mid-0.80s is pretty good for out-of-sample accuracy on this kind of forecasting problem. What’s more, when I averaged the estimates for each case from SVM and Random Forests, I got AUC scores in the mid– to upper 0.80s. That’s several points better than our current ensemble, which combines Random Forests with those logistic regression models.

By contrast, KNN did quite poorly, hovering close to the 0.5 mark that we would get with randomly generated probabilities. Still, success in one of the two experiments is pretty exciting. We don’t have a lot of forecasts to combine right now, so adding even a single high-quality model to the mix could produce real gains.

Mind you, this wasn’t a push-button operation. For one thing, I had to rework my code to handle missing data in a different way—not because SVM handles missing data differently from Random Forests, but because the functions I was using to implement the techniques do. (N.B. All of this work was done in R. I used ‘ksvm’ from the kernlab package for SVM and ‘knn3’ from the caret package for KNN.) I also got poor results from SVM in my initial implementation, which used the default settings for all of the relevant parameters. It took some iterating to discover that the Laplacian kernel significantly improved the algorithm’s performance, and that tinkering with the other flexible parameters (sigma and C for the Laplacian kernel in ksvm) had no effect or made things worse.

I also suspect that the performance of KNN would improve with more effort. To keep the comparison simple, I gave all three algorithms the same set of features and observations. As it happens, though, Random Forests and SVMs are less prone to over-fitting than KNN, which has a harder time separating the signal from the noise when irrelevant features are included. The feature set I chose probably includes some things that don’t add any predictive power, and their inclusion may be obscuring the patterns that do lie in those data. In the next go-round, I would start the KNN algorithm with the small set of features in whose predictive power I’m most confident, see if that works better, and try expanding from there. I would also experiment with different values of k, which I locked in at 5 for this exercise.

It’s tempting to spin the story of this exercise as a human vs. machine parable in which newfangled software and Big Data outdo models hand-crafted by scholars wedded to overly simple stories about the origins of mass atrocities. It’s tempting, but it would also be wrong on a couple of crucial points.

First, this is still small data. Machine learning refers to a class of analytic methods, not the amount of data involved. Here, I am working with the same country-year data set covering the world from the 1940s to the present that I have used in previous iterations of this exercise. This data set contains fewer than 10,000 observations on scores of variables and takes up about as much space on my hard drive as a Beethoven symphony. In the future, I’d like to experiment with newer and larger data sets at different levels of aggregation, but that’s not what I’m doing now, mostly because those newer and larger data sets still don’t cover enough time and space to be useful in the analysis of such rare events.

Second and more important, theory still pervades this process. Scholars’ beliefs about what causes and presages mass killing have guided my decisions about what variables to include in this analysis and, in many cases, how those variables were originally measured and the fact that data even exist on them at all. Those data-generating and variable-selection processes, and all of the expertise they encapsulate, are essential to these models’ forecasting power. In principle, machine learning could be applied to a much wider set of features, and perhaps we’ll try that some time, too. With events as rare as onsets of state-led mass killing, however, I would not have much confidence that results from a theoretically agnostic search would add real forecasting power and not just result in over-fitting.

In any case, based on these results, I will probably incorporate SVM into the next iteration of the Early Warning Project’s statistical risk assessments. Those are due out early in the spring of 2015, when all of the requisite inputs will have been updated (we hope). I think we’ll also need to think carefully about whether or not to keep those logistic regression models in the mix, and what else we might borrow from the world of machine learning. In the meantime, I’ve enjoyed getting to try out some new techniques on data I know well, where it’s a lot easier to tell if things are going wonky, and it’s encouraging to see that we can continue to get better at this hard task if we keep trying.

A Useful Data Set on Political Violence that Almost No One Is Using

For the past 10 years, the CIA has overtly funded the production of a publicly available data set on certain atrocities around the world that now covers the period from January 1995 to early 2014 and is still updated on a regular basis. If you work in a relevant field but didn’t know that, you’re not alone.

The data set in question is the Political Instability Task Force’s Worldwide Atrocities Dataset, which records information from several international press sources about situations in which five or more civilians are deliberately killed in the context of some wider political conflict. Each record includes information about who did what to whom, where, and when, along with a brief text description of the event, a citation for the source article(s), and, where relevant, comments from the coder. The data are updated monthly, although those updates are posted on a four-month lag (e.g., data from January become available in May).

The decision to limit collection to events involving at least five fatalities was a pragmatic one. As the data set’s codebook notes,

We attempted at one point to lower this threshold to one and the data collection demands proved completely overwhelming, as this involved assessing every murder and ambiguous accidental death reported anywhere in the world in the international media. “Five” has no underlying theoretical justification; it merely provides a threshold above which we can confidently code all of the reported events given our available resources.

For the past three years, the data set has also fudged this rule to include targeted killings that appear to have a political motive, even when only a single victim is killed. So, for example, killings of lawyers, teachers, religious leaders, election workers, and medical personnel are nearly always recorded, and these events are distinguished from ones involving five or more victims by a “Yes” in a field identifying “Targeted Assassinations” under a “Related Tactics” header.

The data set is compiled from stories appearing in a handful of international press sources that are accessed through Factiva. It is a computer-assisted process. A Boolean keyword search is used to locate potentially relevant articles, and then human coders read those stories and make data from the ones that turn out actually to be relevant. From the beginning, the PITF data set has pulled from Reuters, Agence France Press, Associated Press, and the New York Times. Early in the process, BBC World Monitor and CNN were added to the roster, and All Africa was also added a few years ago to improve coverage of that region.

The decision to restrict collection to a relatively small number of sources was also a pragmatic one. Unlike GDELT, for example—the routine production of which is fully automated—the Atrocities Data Set is hand-coded by people reading news stories identified through a keyword search. With people doing the coding, the cost of broadening the search to local and web-based sources is prohibitive. The hope is eventually to automate the process, either as a standalone project or as part of a wider automated event data collection effort. As GDELT shows, though, that’s hard to do well, and that day hasn’t arrived yet.

Computer-assisted coding is far more labor intensive than fully automated coding, but it also carries some advantages. Human coders can still discern better than the best automated coding programs when numerous reports are all referring to the same event, so the PITF data set does a very good job eliminating duplicate records. Also, the “where” part of each record in the PITF data set includes geocoordinates, and its human coders can accurately resolve the location of nearly every event to at least the local administrative area, a task over which fully automated processes sometimes still stumble.

Of course, press reports only capture a fraction of all the atrocities that occur in most conflicts, and journalists writing about hard-to-cover conflicts often describe these situations with stories that summarize episodes of violence (e.g., “Since January, dozens of villagers have been killed…”). The PITF data set tries to accommodate this pattern by recording two distinct kinds of events: 1) incidents, which occur in a single place in short period of time, usually a single day; and 2) campaigns, which involve the same perpetrator and target group but may occur in multiple places over a longer period of time—usually days but sometimes weeks or months.

The inclusion of these campaigns alongside discrete events allows the data set to capture more information, but it also requires careful attention when using the results. Most statistical applications of data sets like this one involve cross-tabulations of events or deaths at a particular level during some period of time—say, countries and months. That’s relatively easy to do with data on discrete events located in specific places and days. Here, though, researchers have to decide ahead of time if and how they are going to blend information about the two event types. There are two basic options: 1) ignore the campaigns and focus exclusively on the incidents, treating that subset of the data set like a more traditional one and ignoring the additional information; or 2) make a convenient assumption about the distribution of the incidents of which campaigns are implicitly composed and apportion them accordingly.

For example, if we are trying to count monthly deaths from atrocities at the country level, we could assume that deaths from campaigns are distributed evenly over time and assign equal fractions of those deaths to all months over which they extend. So, a campaign in which 30 people were reportedly killed in Somalia between January and March would add 10 deaths to the monthly totals for that country in each of those three months. Alternatively, we could include all of the deaths from a campaign in the month or year in which it began. Either approach takes advantage of the additional information contained in those campaign records, but there is also a risk of double counting, as some of the events recorded as incidents might be part of the violence summarized in the campaign report.

It is also important to note that this data set does not record information about atrocities in which the United States is either the alleged perpetrator or the target (e.g., 9/11) of an atrocity because of legal restrictions on the activities of the CIA, which funds the data set’s production. This constraint presumably has a bigger impact on some cases, such as Iraq and Afghanistan, than others.

To provide a sense of what the data set contains and to make it easier for other researchers to use it, I wrote an R script that ingests and cross-tabulates the latest iteration of the data in country-month and country-year bins and then plots some of the results. That script is now posted on Github (here).

One way to see how well the data set is capturing the trends we hope it will capture is to compare the figures it produces with ones from data sets in which we already have some confidence. While I was writing this post, Colombian “data enthusiast” Miguel Olaya tweeted a pair of graphs summarizing data on massacres in that country’s long-running civil war. The data behind his graphs come from the Rutas de Conflicto project, an intensive and well-reputed effort to document as many as possible of the massacres that have occurred in Colombia since 1980. Here is a screenshot of Olaya’s graph of the annual death counts from massacres in the Rutas data set since 1995, when the PITF data pick up the story:

Annual Deaths from Massacres in Colombia by Perpetrator (Source: Rutas de Conflicta)

Annual Deaths from Massacres in Colombia by Perpetrator (Source: Rutas de Conflicta)

Now here is a graph of deaths from the incidents in the PITF data set:


Just eyeballing the two charts, the correlation looks pretty good. Both show a sharp increase in the tempo of killing in the mid-1990s; a sustained peak around 2000; a steady decline over the next several years; and a relatively low level of lethality since the mid-2000s. The annual counts from the Rutas data are two or three times larger than the ones from the PITF data during the high-intensity years, but that makes sense when we consider how much deeper of a search that project has conducted. There’s also a dip in the PITF totals in 1999 and 2000 that doesn’t appear in the Rutas data, but the comparisons over the larger span hold up. All things considered, this comparison makes the PITF data look quite good, I think.

Of course, the insurgency in Colombia has garnered better coverage from the international press than conflicts in parts of the world that are even harder to reach or less safe for correspondents than the Colombian highlands. On a couple of recent crises in exceptionally under-covered areas, the PITF data also seems to do a decent job capturing surges in violence, but only when we include campaigns as well as incidents in the counting.

The plots below show monthly death totals from a) incidents only and b) incidents and campaigns combined in the Central African Republic since 1995 and South Sudan since its independence in mid-2011. Here, deaths from campaigns have been assigned to the month in which the campaign reportedly began. In CAR, the data set identifies the upward trend in atrocities through 2013 and into 2014, but the real surge in violence that apparently began in late 2013 is only captured when we include campaigns in the cross-tabulation (the dotted line).

The same holds in South Sudan. There, the incident-level data available so far miss the explosion of civilian killings that began in December 2013 and reportedly continue, but the combination of campaign and incident data appears to capture a larger fraction of it, along with a notable spike in July 2013 related to clashes in Jonglei State.


These examples suggest that the PITF Worldwide Atrocities Dataset is doing a good job at capturing trends over time in lethal violence against civilians, even in some of the hardest-to-cover cases. To my knowledge, though, this data set has not been widely used by researchers interested in atrocities or political violence more broadly. Probably its most prominent use to date was in the Model component of the Tech Challenge for Atrocities Prevention, a 2013 crowdsourced competition funded by USAID and Humanity United. That challenge produced some promising results, but it remains one of the few applications of this data set on a subject for which reliable data are scarce. Here’s hoping this post helps to rectify that.

Disclosure: I was employed by SAIC as research director of PITF from 2001 until 2011. During that time, I helped to develop the initial version of this data set and was involved in decisions to fund its continued production. Since 2011, however, I have not been involved in either the production of the data or decisions about its continued funding. I am part of a group that is trying to secure funding for a follow-on project to the Model part of the Tech Challenge for Atrocities Prevention, but that effort would not necessarily depend on this data set.

Introducing A New Venue for Atrocities Early Warning

Starting today, the bits of this blog on forecasting and monitoring mass atrocities are moving to their proper home, or at least the initial makings of it. Say hi to the (interim) blog of the Early Warning Project.

Since 2012, I have been working as a consultant to the U.S. Holocaust Memorial Museum’s Center for the Prevention of Genocide (CPG) to help build a new global early-warning system for mass atrocities. As usual, that process is taking longer than we had expected. We now have working versions of the project’s two main forecasting streams—statistical risk assessments and a “wisdom of (expert) crowds” system called an opinion pool—and CPG has hired a full-time staffer (hi, Ali) to manage their day-to-day workings. Unfortunately, though, the web site that will present, discuss, and invite discussion of those forecasts is still under construction. Thanks to Dartmouth’s DALI Lab, we’ve got a great prototype, but there’s finishing work to be done, and doing it takes a while.

Well, delays, be damned. We think the content we’re producing is useful now, so we’re not waiting for that site to get finished to start sharing it. Instead, we’re launching this interim blog to go ahead and start doing things like:

When the project’s full-blown web site finally goes up, it will feature a blog, too, and all of the content from this interim venue will migrate there. Until then, if you’re interested in atrocities early warning and prevention—or applied forecasting more generally—please come see what we’re doing, share what you find interesting, and help us think about how to do it even better.

Meanwhile, Dart-Throwing Chimp will keep plugging along on its core themes of democratization, political instability, and forecasting. If you’ve got the interest and the bandwidth, I hope you’ll find time to watch and engage with both channels.

Early Results from a New Atrocities Early Warning System

For the past couple of years, I have been working as a consultant to the U.S. Holocaust Memorial Museum’s Center for the Prevention of Genocide to help build a new early-warning system for mass atrocities around the world. Six months ago, we started running the second of our two major forecasting streams, a “wisdom of (expert) crowds” platform that aggregates probabilistic forecasts from a pool of topical and area experts on potential events of concern. (See this conference paper for more detail.)

The chart below summarizes the output from that platform on most of the questions we’ve asked so far about potential new episodes of mass killing before 2015. For our early-warning system, we define a mass killing as an episode of sustained violence in which at least 1,000 noncombatant civilians from a discrete group are intentionally killed, usually in a period of a year or less. Each line in the chart shows change over time in the daily average of the inputs from all of the participants who choose to make a forecast on that question. In other words, the line is a mathematical summary of the wisdom of our assembled crowd—now numbering nearly 100—on the risk of a mass killing beginning in each case before the end of 2014. Also:

  • Some of the lines (e.g., South Sudan, Iraq, Pakistan) start further to the right than others because we did not ask about those cases when the system launched but instead added them later, as we continue to do.
  • Two lines—Central African Republic and South Sudan—end early because we saw onsets of mass-killing episodes in those countries. The asterisks indicate the dates on which we made those declarations and therefore closed the relevant questions.
  • Most but not all of these questions ask specifically about state-led mass killings, and some focus on specific target groups (e.g., the Rohingya in Burma) or geographic regions (the North Caucasus in Russia) as indicated.
Crowd-Estimated Probabilities of Mass-Killing Onset Before 1 January 2015

Crowd-Estimated Probabilities of Mass-Killing Onset Before 1 January 2015

I look at that chart and conclude that this process is working reasonably well so far. In the six months since we started running this system, the two countries that have seen onsets of mass killing are both ones that our forecasters promptly and consistently put on the high side of 50 percent. Nearly all of the other cases, where mass killings haven’t yet occurred this year, have stuck on the low end of the scale.

I’m also gratified to see that the system is already generating the kind of dynamic output we’d hoped it would, even with fewer than 100 forecasters in the pool. In the past several weeks, the forecasts for both Burma and Iraq have risen sharply, apparently in response to shifts in relevant policies in the former and the escalation of the civil war in the latter. Meanwhile, the forecast for Uighurs in China has risen steadily over the year as a separatist rebellion in Xinjiang Province has escalated and, with it, concerns about a harsh government response. These inflection points and trends can help identify changes in risk that warrant attention from organizations and individuals concerned about preventing or mitigating these potential atrocities.

Finally, I’m also intrigued to see that our opinion pool seems to be sorting cases into a few clusters that could be construed as distinct tiers of concern. Here’s what I have in mind:

  • Above the 50-percent threshold are the high risk cases, where forecasters assess that mass killing is likely to occur during the specified time frame.  These cases won’t necessarily be surprising. Some observers had been warning on the risk of mass atrocities in CAR and South Sudan for months before those episodes began, and the plight of the Rohingya in Burma has been a focal point for many advocacy groups in the past year. Even in supposedly “obvious” cases, however, this system can help by providing a sharper estimate of that risk and giving a sense of how it is trending over time. In the case of Burma, for example, it is the separation that has happened in the last several weeks that tells the story of a switch from possible to likely and thus adds a degree of urgency to that warning.
  • A little farther down the y-axis are the moderate risk cases—ones that probably won’t suffer mass killing during the period in question but could more readily tip in that direction. In the chart above, Iraq, Sudan, Pakistan, Bangladesh, and Burundi all land in this tier, although Iraq now appears to be sliding into the high risk group.
  • Clustered toward the bottom are the low risk cases where the forecasters seem fairly confident that mass killing will not occur in the near future. In the chart above, Russia, Afghanistan, and Ethiopia are the cases that land firmly in this set. China (Uighurs) remains closer to them than the moderate risk tier, but it appears to be creeping toward the moderate-risk group. We are also running a question about the risk of state-led mass killing in Rwanda before 2015, and it currently lands in this tier, with a forecast of 14 percent.

The system that generates the data behind this chart is password protected, but the point of our project is to make these kinds of forecasts freely available to the global public. We are currently building the web site that will display the forecasts from this opinion pool in real time to all comers and hope to have it ready this fall.

In the meantime, if you think you have relevant knowledge or expertise—maybe you study or work on this topic, or maybe you live or work in parts of the world where risks tend to be higher—and are interested in volunteering as a forecaster, please send an email to us at

Alarmed By Iraq

Iraq’s long-running civil war has spread and intensified again over the past year, and the government’s fight against a swelling Sunni insurgency now threatens to devolve into the sort of indiscriminate reprisals that could produce a new episode of state-led mass killing there.

The idea that Iraq could suffer a new wave of mass atrocities at the hands of state security forces or sectarian militias collaborating with them is not far fetched. According to statistical risk assessments produced for our atrocities early-warning project (here), Iraq is one of the 10 countries worldwide most susceptible to an onset of state-led mass killing, bracketed by places like Syria, Sudan, and the Central African Republic where large-scale atrocities and even genocide are already underway.

Of course, Iraq is already suffering mass atrocities of its own at the hands of insurgent groups who routinely kill large numbers of civilians in indiscriminate attacks, every one of which would stun American or European publics if it happened there. According to the widely respected Iraq Body Count project, the pace of civilian killings in Iraq accelerated sharply in July 2013 after a several-year lull of sorts in which “only” a few hundred civilians were dying from violence each month. Since the middle of last year, the civilian toll has averaged more than 1,000 fatalities per month. That’s well off the pace of 2006-2007, the peak period of civilian casualties under Coalition occupation, but it’s still an astonishing level of violence.

Monthly Counts of Civilian Deaths from Violence in Iraq (Source: Iraq Body Count)

Monthly Counts of Civilian Deaths from Violence in Iraq (Source: Iraq Body Count)

What seems to be increasing now is the risk of additional atrocities perpetrated by the very government that is supposed to be securing civilians against those kinds of attacks. A Sunni insurgency is gaining steam, and the government, in turn, is ratcheting up its efforts to quash the growing threat to its power in worrisome ways. A recent Reuters story summarized the current situation:

In Buhriz and other villages and towns encircling the capital, a pitched battle is underway between the emboldened Islamic State of Iraq and the Levant, the extremist Sunni group that has led a brutal insurgency around Baghdad for more than a year, and Iraqi security forces, who in recent months have employed Shi’ite militias as shock troops.

And this anecdote from the same Reuters story shows how that battle is sometimes playing out:

The Sunni militants who seized the riverside town of Buhriz late last month stayed for several hours. The next morning, after the Sunnis had left, Iraqi security forces and dozens of Shi’ite militia fighters arrived and marched from home to home in search of insurgents and sympathizers in this rural community, dotted by date palms and orange groves.

According to accounts by Shi’ite tribal leaders, two eyewitnesses and politicians, what happened next was brutal.

“There were men in civilian clothes on motorcycles shouting ‘Ali is on your side’,” one man said, referring to a key figure in Shi’ite tradition. “People started fleeing their homes, leaving behind the elders and young men and those who refused to leave. The militias then stormed the houses. They pulled out the young men and summarily executed them.”

Sadly, this escalatory spiral of indiscriminate violence is not uncommon in civil wars. Ben Valentino, a collaborator of mine in the development of this atrocities early-warning project, has written extensively on this topic (see especially here , here, and here). As Ben explained to me via email,

The relationship between counter-insurgency and mass violence against civilians is one of the most well-established findings in the social science literature on political violence. Not all counter-insurgency campaigns lead to mass killing, but when insurgent groups become large and effective enough to seriously threaten the government’s hold on power and when the rebels draw predominantly on local civilians for support, the risks of mass killing are very high. Usually, large-scale violence against civilians is neither the first nor the only tactic that governments use to defeat insurgencies. They may try to focus operations primarily against armed insurgents, or even offer positive incentives to civilians who collaborate with the government. But when less violent methods fail, the temptation to target civilians in the effort to defeat the rebels increases.

Right now, it’s hard to see what’s going to halt or reverse this trend in Iraq. “Things can get much worse from where we are, and more than likely they will,” Daniel Serwer told IRIN News for a story on Iraq’s escalating conflict (here). Other observers quoted in the same story seemed to think that conflict fatigue would keep the conflict from ballooning further, but that hope is hard to square with the escalation of violence that has already occurred over the past year and the fact that Iraq’s civil war never really ended.

In theory, elections are supposed to be a brake on this process, giving rival factions opportunities to compete for power and influence state policy in nonviolent ways. In practice, this often isn’t the case. Instead, Iraq appears to be following the more conventional path in which election winners focus on consolidating their own power instead of governing well, and excluded factions seek other means to advance their interests. Here’s part of how the New York Times set the scene for this week’s elections, which incumbent prime minister Nouri al-Maliki’s coalition is apparently struggling to win:

American intelligence assessments have found that Mr. Maliki’s re-election could increase sectarian tensions and even raise the odds of a civil war, citing his accumulation of power, his failure to compromise with other Iraqi factions—Sunni or Kurd—and his military failures against Islamic extremists. On his watch, Iraq’s American-trained military has been accused by rights groups of serious abuses as it cracks down on militants and opponents of Mr. Maliki’s government, including torture, indiscriminate roundups of Sunnis and demands of bribes to release detainees.

Because Iraq ranked so high in our last statistical risk assessments, we posted a question about it a few months ago on our “wisdom of (expert) crowds” forecasting system. Our pool of forecasters is still relatively small—89 as I write this—but the ones who have weighed in on this topic so far have put it in what I see as a middle tier of concern, where the risk is seen as substantial but not imminent or inevitable. Since January, the pool’s estimated probability of an onset of state-led mass killing in Iraq in 2014 has hovered around 20 percent, alongside countries like Pakistan (23 percent), Bangladesh (20 percent), and Burundi (19 percent) but well behind South Sudan (above 80 percent since December) and Myanmar (43 percent for the risk of a mass killing targeting the Rohingya in particular).

Notably, though, the estimate for Iraq has ticked up a few notches in the past few days to 27 percent as forecasters (including me) have read and discussed some of the pre-election reports mentioned here. I think we are on to something that deserves more scrutiny than it appears to be getting.

The Rwanda Enigma

For analysts and advocates trying to assess risks of future mass atrocities in hopes of preventing them, Rwanda presents an unusual puzzle. Most of the time, specialists in this field readily agree on which countries are especially susceptible to genocide or mass killing, either because those countries are either already experiencing large-scale civil conflict or because they are widely considered susceptible to it. Meanwhile, countries that sustain long episodes of peace and steadily grow their economies are generally presumed to have reduced their risk and eventually to have escaped this trap for good.

Contemporary Rwanda is puzzling because it provokes a polarized reaction. Many observers laud Rwanda as one of Africa’s greatest developmental successes, but others warn that it remains dangerously prone to mass atrocities. In a recent essay for African Arguments on how the Rwandan genocide changed the world, Omar McDoom nicely encapsulates this unusual duality:

What has changed inside Rwanda itself since the genocide? The country has enjoyed a remarkable period of social stability. There has not been a serious incident of ethnic violence in Rwanda for nearly two decades. Donors have praised the country’s astonishing development.  Economic growth has averaged over 6% per year, poverty and inequality have declined, child and maternal mortality have improved, and primary education is now universal and free. Rwanda has shown, in defiance of expectations, that an African state can deliver security, public services, and rising prosperity.

Yet, politically, there is some troubling continuity with pre-genocide Rwanda. Power remains concentrated in the hands of a small, powerful ethnic elite led by a charismatic individual with authoritarian tendencies. In form, current president Paul Kagame and his ruling party, the RPF, the heroes who ended the genocide, appear to exercise power in a manner similar to former president Juvenal Habyarimana and his ruling MRND party, the actors closely-tied to those who planned the slaughter. The genocide is testament to what unconstrained power over Rwanda’s unusually efficient state machinery can enable.

That duality also emerges from a comparison of two recent quantitative rankings. On the one hand, The World Bank now ranks Rwanda 32nd on the latest edition of its “ease of doing business” index—not 32nd in Africa, but 32nd of 189 countries worldwide. On the other hand, statistical assessments of the risk of an onset of state-led mass killing identify Rwanda as one of the 25 countries worldwide currently most vulnerable to this kind of catastrophe.

How can both of these things be true? To answer that question, we need to have a clearer sense of where that statistical risk assessment comes from. The number that ranks Rwanda among the 25 countries most susceptible to state-led mass killing is actually an average of forecasts from three models representing a few different ideas about the origins of mass atrocities, all applied to publicly available data from widely used sources.

  • Drawing on work by Barbara Harff and the Political Instability Task Force, the first model emphasizes features of countries’ national politics that hint at a predilection to commit genocide or “politicide,” especially in the context of political instability. Key risk factors in Harff’s model include authoritarian rule, the political salience of elite ethnicity, evidence of an exclusionary elite ideology, and international isolation as measured by trade openness.
  • The second model takes a more instrumental view of mass killing. It uses statistical forecasts of future coup attempts and new civil wars as proxy measures of things that could either spur incumbent rulers to lash out against threats to their power or usher in an insecure new regime that might do the same.
  • The third model is really not a model but a machine-learning process called Random Forests applied to the risk factors identified by the other two. The resulting algorithm is an amalgamation of theory and induction that takes experts’ beliefs about the origins of mass killing as its jumping-off point but also leaves more room for inductive discovery of contingent effects.

All of these models are estimated from historical data that compares cases where state-led mass killings occurred to ones where they didn’t. In essence, we look to the past to identify patterns that will help us spot cases at high risk of mass killing now and in the future. To get our single-best risk assessment—the number that puts Rwanda in the top (or bottom) 25 worldwide—we simply average the forecasts from these three models. We prefer the average to a single model’s output because we know from work in many fields—including meteorology and elections forecasting—that this “ensemble” approach generally produces more accurate assessments than we could expect to get from any one model alone. By combining forecasts, we learn from all three perspectives and hedge against the biases of any one of them.

Rwanda lands in the top 25 worldwide because all three models identify it as a relatively high-risk case. It ranks 15th on the PITF/Harff model, 28th on the “elite threat” model, and 30th on the Random Forest. The PITF/Harff model sees a relatively low risk in Rwanda of the kinds of political instability that typically trigger onsets of genocide or politicide, but it also pegs Rwanda as the kind of regime most likely to resort to mass atrocities if instability were to occur—namely, an autocracy in which elites’ ethnicity is politically salient in a country with a recent history of genocide. Rwanda also scores fairly high on the “elite threat” model because, according to our models of these things, it is at relatively high risk of a new insurgency and moderate risk of a coup attempt. Finally, the Random Forest sees a very low probability of mass killing onset in Rwanda but still pegs it as a riskier case than most.

Our identification of Rwanda as a relatively high-risk case is echoed by some, but not all, of the other occasional global assessments of countries’ susceptibility to mass atrocities. In her own applications of her genocide/politicide model for the task of early warning, Barbara Harff pegged Rwanda as one of the world’s riskiest cases in 2011 but not in 2013. Similarly, the last update of Genocide Watch’s Countries at Risk Report, in 2012, lists Rwanda as one of more than a dozen countries at stage five of seven on the path to genocide, putting it among the 35 countries worldwide at greatest risk. By contrast, the Global Centre for the Responsibility to Protect has not identified Rwanda as a situation of concern in any of its R2P Monitor reports to date, and the Sentinel Project for Genocide Prevention does not list Rwanda among its situations of concern, either. Meanwhile, recent reporting on Rwanda from Human Rights Watch has focused mostly on the pursuit of justice for the 1994 genocide and other kinds of human-rights violations in contemporary Rwanda.

To see what our own pool of experts makes of our statistical risk assessment and to track changes in their views over time, we plan to add a question to our “wisdom of (expert) crowds” forecasting system asking about the prospect of a new state-led mass killing in Rwanda before 2015. If one does not happen, as we hope and expect will be the case, we plan to re-launch the question at the start of next year and will continue to do so as long as our statistical models keep identifying it as a case of concern.

In the meantime, I thought it would be useful to ask a few country experts what they make of this assessment and how a return to mass killing in Rwanda might come about. Some were reluctant to speak on the record, and understandably so. The present government of Rwanda has a history of intimidating individuals it perceives as its critics. As Michaela Wrong describes in a recent piece for Foreign Policy,

A U.S. State Department spokesperson said in mid-January, “We are troubled by the succession of what appear to be politically motivated murders of prominent Rwandan exiles. President Kagame’s recent statements about, quote, ‘consequences’ for those who betray Rwanda are of deep concern to us.”

It is a pattern that suggests the Rwandan government may have come to see the violent silencing of critics—irrespective of geographical location and host country—as a beleaguered country’s prerogative.

Despite these constraints, the impression I get from talking to some experts and reading the work of others is that our risk assessment strikes nearly all of them as plausible. None said that he or she expects an episode of state-led mass killing to begin soon in Rwanda. Consistent with the thinking behind our statistical models, though, many seem to believe that another mass killing could occur in Rwanda, and if one did, it would almost certainly come in reaction to some other rupture in that country’s political stability.

Filip Reyntjens, a professor at the University of Antwerpen who wrote a book on Rwandan politics since the 1994 genocide, was both the most forthright and the most pessimistic in his assessment. Via email, he described Rwanda as

A volcano waiting to erupt. Nearly all field research during the last 15 years points at pervasive structural violence that may, as we know, become physical, acute violence following a trigger. I don’t know what that trigger will be, but I think a palace revolution or a coup d’etat is the most likely scenario. That may create a situation difficult to control.

In a recent essay for Juncture that was adapted for the Huffington Post (here), Phil Clark sounds more optimistic than Reyntjens, but he is not entirely sanguine, either. Clark sees the structure and culture of the country’s ruling party, the Rwandan Patriotic Front (RPF), as the seminal feature of Rwandan politics since the genocide and describes it as a double-edged sword. On the one hand, the RPF’s cohesiveness and dedication to purpose has enabled it, with help from an international community with a guilty conscience, to make “enormous” developmental gains. On the other hand,

The RPF’s desire for internal cohesion has made it suspicious of critical voices within and outside of the party—a feature compounded by Rwanda’s fraught experience of multi-party democracy in the early 1990s, which saw the rise of ethnically driven extremist parties and helped to create an environment conducive to genocide. The RPF’s singular focus on rebuilding the nation and facilitating the return of refugees means it has often viewed dissent as an unaffordable distraction. The disastrous dalliance with multipartyism before the genocide has only added to the deep suspicion of policy based on the open contestation of ideas.

Looking ahead, Clark wonders what happens when that intolerance for dissent bumps up against popular frustrations, as it probably will at some point:

For the moment, there are few signs of large-scale popular discontent with the closed political space. However, any substantial decline in socio-economic conditions in the countryside will challenge this. The RPF’s gamble appears to be that the population will tolerate a lack of national political contestation provided domestic stability and basic living standards are maintained. For now, the RPF seems to have rightly judged the popular mood but that situation may not hold.

Journalist Kris Berwouts portrays similarly ambiguous terrain in a recent piece for the Dutch magazine Mo that also appeared on the blog African Arguments (here). Berwouts quotes David Himbara, a former Rwandan regime insider who left the country in 2010 and has vocally criticized the Kagame government ever since, as telling him that “all society has vanished from Rwanda, mistrust is complete. It has turned Rwanda into a time bomb.” But Berwouts juxtaposes that dire assessment with the cautiously optimistic view of Belgian journalist Marc Hoogsteyns, who has worked in the region for years and has family ties by marriage to its Tutsi community. According to Hoogsteyns,

Rwanda is a beautiful country with many strengths and opportunities, but at the same time it is some kind of African version of Brave New World. People are afraid to talk. But they live more comfortably and safely than ever before, they enjoy high quality education and health care. They are very happy with that. The Tutsi community stands almost entirely behind Kagame and also most Hutu can live with it. They obviously don’t like the fact that they do not count on the political scene, but they can do what they want in all other spheres of live. They can study and do business etcetera. They can deal with the level of repression, because they know that countries such as Burundi, Congo or Kenya are not the slightest bit more democratic. Honestly, if we would have known twenty years ago, just after the genocide, that Rwanda would achieve this in two decades, we would have signed for it immediately.

As people of a certain age in places like Sarajevo or Bamako might testify, though, stability is a funny thing. It’s there until it isn’t, and when it goes, it sometimes goes quickly. In this sense, the political crises that sometimes produce mass killings are more like earthquakes than elections. We can spot the vulnerable structures fairly accurately, but we’re still not very good at anticipating the timing and dynamics of ruptures in them.

In the spirit of that last point, it’s important to acknowledge that the statistical assessment of Rwanda’s risk to mass killing is a blunt piece of information. Although it does specifically indicate a susceptibility to atrocities perpetrated by state security forces or groups acting at their behest, it does not necessarily implicate the RPF as the likely perpetrators. The qualitative assessments discussed above suggest that some experts find that scenario plausible, but it isn’t the only one consistent with our statistical finding. A new regime brought to power by coup or revolution could also become the agent of a new wave of mass atrocities in Rwanda, and the statistical forecast would be just as accurate.

Egypt’s recent past offers a case in point. Our statistical assessments of susceptibility to state-led mass killing in early 2013 identified Egypt as a relatively high-risk case, like Rwanda now. At the time, Mohammed Morsi was president, and one plausible interpretation of that risk assessment might have centered on the threat the Muslim Brotherhood’s supporters posed to Egypt’s Coptic Christians. Fast forward to July 2013, and the mass killing we ended up seeing in Egypt came at the hands of an army and police who snatched power away from Morsi and the Brotherhood and then proceeded to kill hundreds of their unarmed sympathizers. That outcome doesn’t imply that Coptic Christians weren’t at grave risk before the coup, but it should remind us to consider a variety of ways these systemic risks might become manifest.

Still, after conversations with a convenience sample of regional experts, I am left with the impression that the risk our statistical models identify of a new state-led mass killing in Rwanda is real, and that it is possible to imagine the ruling RPF as the agents of such violence.

No one seems to expect the regime to engage in mass violence without provocation, but the possibility of a new Hutu insurgency, and the state’s likely reaction to it, emerged from those conversations as perhaps the most likely scenario. According to some of the experts with whom I spoke, many Rwandan Hutus are growing increasingly frustrated with the RPF regime, and some radical elements of the Hutu diaspora appear to be looking for ways to take up that mantle. The presence of an insurgency is the single most-powerful predictor of state-led mass killing, and it does not seem far fetched to imagine the RPF regime using “scorched earth” tactics in response to the threat or occurrence of attacks on its soldiers and Tutsi citizens. After all, this is the same regime whose soldiers pursued Hutu refugees into Zaire in the mid-1990s and, according to a 2010 U.N. report, participated in the killings of tens of thousands of civilians in war crimes that were arguably genocidal.

Last but not least, we can observe that Rwanda has suffered episodes of mass killing roughly once per generation since independence—in the early 1960s, in 1974, and again in the early 1990s, culminating in the genocide of 1994 and the reprisal killings that followed. History certainly isn’t destiny, but our statistical models confirm that in the case of mass atrocities, it often rhymes.

It saddens me to write this piece about a country that just marked the twentieth anniversary of one of the most lethal genocides since the Holocaust, but the point of our statistical modeling is to see what the data say that our mental models and emotional assessments might overlook. A reprisal of mass killing in Rwanda would be horribly tragic. As Free Africa Foundation president George Ayittey wrote in a recent letter of the Wall Street Journal, however, “The real tragedy of Rwanda is that Mr. Kagame is so consumed by the 1994 genocide that, in his attempt to prevent another one, he is creating the very conditions that led to it.”


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