Meanwhile, In the Lives of Hundreds of Millions of Asians…

While our social-media feeds and cable-news crawls were inundating us with news of the latest bombing, beheading, armed clash, plane crash, and viral epidemic, this was happening, too:

Rural wages are rising across much of Asia, and in some cases have accelerated since the mid 2000s. And they are doing so fast (and getting faster)… Doubling in China in the last decade, tripling or quadrupling in Vietnam. A bit slower in Bangladesh, but still up by half. This really matters because landless rural people are bottom of the heap (72% of Asia’s extreme poor are rural—some 687m people in 2008), so what they can pick up from their casual labour is a key determinant of poverty, or the lack of it. Steve argues that if the trend continues (and it looks like it will) this spells ‘the end of mass (extreme) poverty in Asia’.

That quote comes from a recent post by Duncan Green for his From Poverty to Power blog. The emphasis is mine. The Steve referenced in the last line is economist Steve Wiggins, co-author with Sharada Keats of a new report, on rural wages in Asia, from which those findings flow.

The good news from this report doesn’t stop in Asia. As Green also summarizes, higher rural wages in many Asian countries are driving up wages from manufacturing and increasing the costs of food production. Those trends should help tilt comparative advantage in food production and low-wage manufacturing toward Africa and lower-income parts of Asia. As that happens, the prospects for similar transformations occurring in those areas should improve, too.

There is no shortage of catastrophes in the world right now, and climate change runs under the whole thing like a fault line that’s started trembling with peak intensity and consequences still unknown. Meanwhile, though, most people in most parts of the world are quietly going about the business of trying to make their own lives a little bit better. And, apparently, many of them are succeeding. We shouldn’t let the incessant flow of bad news obscure our view of the larger system. This report is yet another indication that, at that level, some important things are still trending positive in spite of all the terrible things we more easily observe.

Thoughts on the Power of Civil Resistance

“People power” is a blunt and in some ways soft instrument. Activists engaged in mass protest are usually seeking formal changes in the rules or leadership of organizations to which they do not belong or in which their votes are not counted. Unfortunately for them, there is no clear or direct mechanism for converting the energy of the street into the production of those changes.

Once nonviolent action begins, however, state repression becomes a blunt instrument, too. The varied and often discreet routines states use to prevent challenges from emerging become mostly irrelevant. Instead, states must switch to a repertoire of clumsier and less familiar actions with larger and more immediate consequences.

The awkwardness of this response turns out to be the mechanism that converts people power into change, or at least the possibility for it. States thrive on routines around which they can build bureaucracies and normalize public expectations. Activists who succeed at mobilizing and sustaining mass challenges force the state onto less familiar footing, where those bureaucracies’ routines don’t apply and public expectations are weakly formed. In so doing, activists instill uncertainty in the minds of officials who must respond and of the observers of these interactions.

Responses to that uncertainty don’t always break in favor of the challengers, but they can. Insiders who comfortably played supporting roles before must consider what will happen to them if the challenge succeeds and how they might shape that future in their own favor. Other observers, foreign and domestic, may become newly energized or at least sympathetic, and even small alterations in the behaviors of those individuals can accumulate into large changes in the behavior of the public writ large. Importantly, these responses are more likely to break in favor of the challengers when those challengers manage to sustain nonviolence, even in the face of state repression.

Activists cannot control the reactions catalyzed by this uncertainty, but neither can the state. The result is an opportunity, a roll of the dice that would not have happened in the absence of the public challenge. And, really, that’s the point. That opportunity is not a sufficient condition for deep change, but it is a necessary one, and it almost never arises without a provocation.

How the Umbrella Revolution Could Win

I’m watching Hong Kong’s “umbrella revolution” from afar and wondering how an assemblage of unarmed students and professionals might succeed in wresting change from a dictatorship that has consistently and ruthlessly repressed other challenges to its authority for decades. I have already said that I expect the state to repress again and think it unlikely that China’s Communist regime will bend sharply or break in response to this particular challenge at this particular moment. But unlikely doesn’t mean impossible, and, like many observers, I hope for something better.

How could something better happen? For me, Kurt Schock’s Unarmed Insurrections remains the single most-useful source on this topic. In that 2005 book, Schock compares successful and failed “people power” movements from the late twentieth century to try to identify patterns that distinguish the former from the latter. Schock clearly sympathizes with the nonviolent protesters whose actions he describes, but that sympathy seems to motivate him to make his analysis as rigorous as possible in hopes of learning something that might inform future movements.

Schock’s overarching conclusion is that structure is not destiny—that movement participants can improve their odds of success through the strategies and tactics they choose. In this he echoes the findings of his mentors, who argued in a 1996 book (p. 15) that “movements may be largely born of environmental opportunities, but their fate is heavily shaped by their own actions.” Schock’s theoretical framework is also openly influenced by the pragmatic advocacy of Gene Sharp, but his analysis confirms the basic tenets of that approach.

So, which strategies and tactics improve the odds of movement success? On this, Schock writes (p. 143, emphasis added):

The trajectories of unarmed insurrections are shaped by the extent to which interactions between challengers, the state, and third parties produce shifts in the balance of power. The probability that an unarmed insurrection will tip the balance of power in favor of the challengers is a function of its resilience and leverage. By remaining resilient in the face of repression and effecting the withdrawal of support from or pressure against the state through its dependence relations, the state’s capacity to rule may be diminished, third-party support for the movement may be mobilized, and the coherence of the political or military elite may fracture, that is, the political context may be recast to one more favorable to the challenge.

Resilience refers to the movement’s capacity to keep mobilizing and acting in the face of attempts to repress or disperse it. Leverage refers to the movement’s ability to get constituencies on whose support the regime depends—security forces, local business and political leaders, labor groups, sometimes foreign governments and markets—to support their cause, either directly, through participation or the provision of other resources, or indirectly, through pressure on the regime to reform or concede.

On makes movements resilient, Schock’s analysis points (p. 143) to “decentralized yet coordinated organizational networks, the ability to implement multiple actions from across the three methods of nonviolent action [protest and persuasion, noncooperation, and nonviolent intervention], the ability to implement methods of dispersion as well as methods of concentration, and tactical innovation.”

Schock concludes his study with a list of six lessons that nonviolent challengers might draw from successes of the past about how to improve their own odds of success. Paraphrased and summarized, those six lessons are:

  • Set clear and limited goals. “The goals of movements should be well chosen, clearly defined, and understood by all parties to the conflict. The goals should be compelling and vital to the interests of the challenging group, and they should attract the widest possible support, both within society and externally… Precise goals give direction to the power activated by a movement and inhibit the dispersion of mobilized energies and resources.”
  • Adopt oppositional consciousness and build temporary organizations. “Oppositional consciousness is open-ended, nontotalizing, and respectful of diversity, and it facilitates the mobilization of a broad-based opposition.” Oppositional consciousness also “rejects permanent, centralized organizations and vanguard parties, opting for united front politics, shifting alliances, and temporary organizations that engage in struggles as situations arise.”
  • Engage in multiple channels of resistance. Here, Schock focuses on the value of pairing actions through institutional (e.g., elections) and non-institutional (e.g., street demonstrations) channels. In other words, attack on as many fronts as possible.
  • Employ multiple methods of nonviolent action. “Struggles for political change should not depend on a single event, however momentous, but rather should focus on the process of shifting the balance of political power through a range of mutually supporting actions over time.”
  • Act in multiple spaces and places of resistance. In addition to public rallies and demonstrations, activists can employ methods of non-cooperation (e.g., strikes and boycotts) and try to create “liberated areas” outside the state’s control. (Nowadays, these areas might exist online as well as in physical space.)
  • Communicate. “Communication among the challengers, accurate public knowledge about the movement, and international media coverage all increase the likelihood of success.”

Looking at the umbrella revolution through that lens, I’d say it is doing all of these things already—self-consciously, I would guess—and those actions seem to be having the desired effects of expanding local and international support for their movement and improving its resilience. Just today, the movement reiterated an ambitious but clear and limited set of goals that are positive and broadly appealing. Activists are working cooperatively through an array of organizations. They have built communications networks that are designed to withstand all but the most draconian attempts to shut them down. Participants are using the internet to spread knowledge about their movement, and a bevy of foreign reporters in Hong Kong are amplifying that message. The possible exception comes in the limited range of actions the movement is using. At the moment, the challenge seems to be heavily invested in the occupation of public spaces. That may change, however, as the movement persists or if and when it is confronted with even harsher repression.

More important, this uprising was not born last Friday. The longer arc of this challenge includes a much wider array of methods and spaces, including this summer’s referendum and the marches and actions of political and business elites that accompanied and surrounded them. As Jeff Wasserstrom described in a recent interview with Vox, the Occupy Central movement also connects to a longer history of pro-democracy dissent in Hong Kong under Beijing’s rule and beyond. In other words, this movement is much bigger and more deeply rooted than the occupations we’re witnessing right now, and it has already proved resilient to repeated attempts to quash it.

As Schock and Sharp and many others would argue, those shrewd choices and that resiliency do not ensure success, but they should improve prospects for it. Based on patterns from similar moments around the world in recent decades and the Communist Party of China’s demonstrated intolerance for popular challenges, I continue to anticipate that the ongoing occupations will soon face even harsher attempts to repress them than the relatively modest ones we saw last weekend. Perhaps that won’t happen, though, and if it does, I am optimistic that the larger movement will survive that response and eventually realize its goals, hopefully sooner rather than later.

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.

Why political scientists should predict

Last week, Hans Noel wrote a post for Mischiefs of Faction provocatively titled “Stop trying to predict the future“. I say provocatively because, if I read the post correctly, Noel’s argument deliberately refutes his own headline. Noel wasn’t making a case against forecasting. Rather, he was arguing in favor of forecasting, as long as it’s done in service of social-scientific objectives.

If that’s right, then I largely agree with Noel’s argument and would restate it as follows. Political scientists shouldn’t get sucked into bickering with their colleagues over small differences in forecast accuracy around single events, because those differences will rarely contain enough information for us to learn much from them. Instead, we should take prediction seriously as a means of testing competing theories by doing two things.

First, we should build forecasting models that clearly represent contrasting sets of beliefs about the causes and precursors of the things we’re trying to predict. In Noel’s example, U.S. election forecasts are only scientifically interesting in so far as they come from models that instantiate different beliefs about why Americans vote like they do. If, for example, a model that incorporates information about trends in unemployment consistently produces more accurate forecasts than a very similar model that doesn’t, then we can strengthen our confidence that trends in unemployment shape voter behavior. If all the predictive models use only the same inputs—polls, for example—we don’t leave ourselves much room to learn about theories from them.

In my work for the Early Warning Project, I have tried to follow this principle by organizing our multi-model ensemble around a pair of models that represent overlapping but distinct ideas about the origins of state-led mass killing. One model focuses on the characteristics of the political regimes that might perpetrate this kind of violence, while another focuses on the circumstances in which those regimes might find themselves. These models embody competing claims about why states kill, so a comparison of their predictive accuracy will give us a chance to learn something about the relative explanatory power of those competing claims. Most of the current work on forecasting U.S. elections follows this principle too, by the way, even if that’s not what gets emphasized in media coverage of their work.

Second, we should only really compare the predictive power of those models across multiple events or a longer time span, where we can be more confident that observed differences in accuracy are meaningful. This is basic statistics. The smaller the sample, the less confident we can be that it is representative of the underlying distribution(s) from which it was drawn. If we declare victory or failure in response to just one or a few bits of feedback, we risk “correcting” for an unlikely draw that dimly reflects the processes that really interest us. Instead, we should let the models run for a while before chucking or tweaking them, or at least leave the initial version running while trying out alternatives.

Admittedly, this can be hard to do in practice, especially when the events of interest are rare. All of the applied forecasters I know—myself included—are tinkerers by nature, so it’s difficult for us to find the patience that second step requires. With U.S. elections, forecasters also know that they only get one shot every two or four years, and that most people won’t hear anything about their work beyond a topline summary that reads like a racing form from the horse track. If you’re at all competitive—and anyone doing this work probably is—it’s hard not to respond to that incentive. With the Early Warning Project, I worry about having a salient “miss” early in the system’s lifespan that encourages doubters to dismiss the work before we’ve really had a chance to assess its reliability and value. We can be patient, but if our intended audiences aren’t too, then the system could fail to get the traction it deserves.

Difficult doesn’t mean impossible, however, and I’m optimistic that political scientists will increasingly use forecasting in service of their search for more useful and more powerful theories. Journal articles that take this idea seriously are still rare birds, especially on things other than U.S. elections, but you occasionally spot them (Exhibit A and B). As Drew Linzer tweeted in response to Noel’s post, “Arguing over [predictive] models is arguing over assumptions, which is arguing over theories. This is exactly what [political science] should be doing.”

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.

No, Pope Francis, this is not World War Three

In the homily to a mass given this morning in Italy, at a monument to 100,000 soldiers killed in World War I, Pope Francis said:

War is madness… Even today, after the second failure of another world war, perhaps one can speak of a third war, one fought piecemeal, with crimes, massacres, destruction.

There are a lot of awful things happening around the world, and I appreciate the pope’s advocacy for peace, but this comparison goes too far. Take a look at this chart of battle deaths from armed conflict around the world from 1900 to 2005, from a study by the Peace Research Institute of Oslo:

The chart doesn’t include the past decade, but we don’t need all the numbers in one place to see what a stretch this comparison is. Take Syria’s civil war, which has probably killed more than 150,000 (source) and perhaps as many as 300,000 or more people over the past three years, for an annual death rate of 50,000–100,000. That is a horrifying toll, but it is vastly lower than the annual rates in the several millions that occurred during the World Wars. Put another way, World War II was like 40 to 80 Syrian civil wars at once.

The many other wars of the present do not substantially close this gap. The civil war in Ukraine has killed approximately 3,000 so far (source). More than 2,000 people have died in the fighting associated with Israel’s Operation Protective Edge in Gaza this year (source). The resurgent civil war in Iraq dwarfs them both but still remains well below the intensity of the (interconnected) war next door (source). There are more than 20 other armed conflicts ongoing around the world, but most of them are much less lethal than the ones in Syria and Iraq, and their cumulative toll does not even begin to approach the ones that occurred in the World Wars (source).

I sympathize with the Pope’s intentions, but I don’t think that hyperbole is the best way to realize them. Of course, Pope Francis is not alone; we’ve been hearing a lot of this lately. I wonder if violence on the scale of the World Wars now lies so far outside of our lived experience that we simply cannot fathom it. Beyond some level of disorder, things simply become terrible, and all terrible things are alike. I also worry that the fear this apparent availability cascade is producing will drive other governments to react in ways that only make things worse.

The era of democratization is not over

In the latest issue of the Journal of Democracy, (PDF), Marc Plattner makes the provocative claim that “the era of democratic transitions is over, and should now become the province of the historians.” By that, he seems to mean that we should not expect new waves of democratization similar in form and scale to the ones that have occurred before. I think Plattner is wrong, in part because he has defined “wave” too broadly. If we tighten up that concept a bit, I think we can see at least a few possibilities for new waves in the not-too-distant future, and thus an extension of the now–long-running era of democratization.

In his essay, Plattner implicitly adopts the definition of waves of democratization described by Samuel Huntington on p. 15 of his influential 1991 book:

A wave of democratization is a group of transitions from nondemocratic to democratic regimes that occur within a specified period of time and that significantly outnumber transitions in the opposite direction during that period of time.

Much of what’s been written and said about waves of democratization since that book was published accepts those terms and the three waves Huntington identifies when he applies them to the historical evidence: one in Europe from the 1820s to the 1920s; another and wider one in Europe, Latin America, and Asia from the 1940s to the early 1960s; and a third and so-far final one that began in Portugal in 1974, has been global in scope, and now appears to have stalled or ended.

I find Huntington’s definition and resulting periodization wanting because they focus on the what and don’t pay enough attention to the why. A large number of transitions might occur around the same time because they share common underlying causes; because they cause and reinforce each other; or as a matter of chance, when independent events just happen to cluster. The third possibility is not scientifically interesting (cf. the Texas sharpshooter fallacy). More relevant here, though, I think the first two become banal if we let the time lag or chain of causality stretch too far. We inhabit a global system; at some level, everything causes, and is caused by, everything else. For the wave idea to be scientifically useful, we have to restrict its use to clusters of transitions that share common, temporally proximate causes and/or directly cause and reinforce each other.

By that definition, I think we can make out at least five and maybe more such waves since the early 1900s, not the three or maybe four we usually hear about.

First, as Plattner  (p. 9) points out, what Huntington describes as the “first, long” wave really includes two distinct clusters: 1) the “dozen or so European and European-settler countries that already had succeeded in establishing a fair degree of freedom and rule of law, and then moved into the democratic column by gradually extending the suffrage”; and 2) “countries that became democratic after World War I, many of them new nations born from the midst of the European empires defeated and destroyed during the war.”

The second (or now third?) wave grew out of World War II. Even though this wave was relatively short, it also included a few distinct sub-clusters: countries defeated in that war, countries born of decolonization, and a number of Latin American cases. This wave is more coherent, in that all of these sub-clusters were at least partially nudged along by the war’s dynamics and outcomes. It wouldn’t be unreasonable to split the so-called second wave into two clusters (war losers and newly independent states) and a clump of coincidences (Latin America), but there are enough direct linkages across those sets to see meaning in a larger wave, too.

As for the so-called third wave, I’m with Mike McFaul (here) and others who see at least two separate clusters in there. The wave of democratization that swept southern Europe and Latin America in the 1970s and early 1980s is temporally and causally distinct from the spate of transitions associated with the USSR’s reform and disintegration, so it makes no sense to talk of a coherent era spanning the past 40 years. Less clear is where to put the many democratic transitions—some successful, many others aborted or short lived—that occurred in Africa as Communist rule collapsed. Based partly on Robert Bates’ analysis (here), I am comfortable grouping them with the post-Communist cases. Trends in the global economy and the disappearance of the USSR as a patron state directly affected many of these countries, and political and social linkages within and across these regional sets also helped to make democratization contagious once it started.

So, based on that definition and its application, I think it’s fair to say that we have seen at least five waves of democratization in the past two centuries, and perhaps as many as six or seven.

Given that definition, I think it’s also easier to see possibilities for new waves, or “clusters” if we want to make clearer the distinction from conventional usage. Of course, the probability of any new waves is partially diminished by the success of the earlier ones. Nearly two-thirds of the world’s countries now have regimes that most observers would call democratic, so the pool of potential democratizers is substantially diminished. As Plattner puts it (p. 14), “The ‘low-hanging fruit’ has been picked.” Still, if we look for groups of authoritarian regimes that share enough political, economic, social, and cultural connections to allow common causes and contagion to kick in, then I think we can find some sets in which this dynamic could clearly happen again. I see three in particular.

The first and most obvious is in the Middle East and North Africa, the region that has proved most resistant to democratization to date. In fact, I think we already saw—or, arguably, are still seeing—the next wave of democratization in the form of the Arab Spring and its aftermath. So far, that cluster of popular uprisings and state collapses has only produced one persistently democratic state (Tunisia), but it has also produced a democratic interlude in Egypt; a series of competitively elected (albeit ineffective) governments in Libya; a nonviolent transfer of power between elected governments in Iraq; ongoing (albeit not particularly liberal) revolutions in Syria and Yemen; and sustained, liberal challenges to authoritarian rule in Bahrain, Kuwait, and, perhaps, Saudi Arabia. In other words, a lot of countries are involved, and it ain’t over yet. Most of the Soviet successor states never really made it all the way to democracy, but we still think of them as an important cluster of attempts at democratization. I think the Arab Spring fits the same mold.

Beyond that, though, I also see the possibility of a wave of regime breakdowns and attempts at democracy in Asia brought on by economic or political instability in China. Many of the autocracies that remain in that region—and there are many—depend directly or indirectly on Chinese patronage and trade, so any significant disruption in China’s political economy would send shock waves through their systems as well. I happen to think that systemic instability will probably hit China in the next few years (see here, here, and here), but the timing is less relevant here than the possibility of this turbulence, and thus of the wider wave of democratization it could help to produce.

Last and probably least in its scope and impact, I think we can also imagine a similar cluster occurring in Eurasia in response to instability in Russia. The number of countries enmeshed in this network is smaller, but the average strength of their ties is probably similar.

I won’t hazard guesses now about the timing and outcome of the latter two possibilities beyond what I’ve already written about China’s increasing fragility. As the Arab Spring has shown, even when we can spot the stresses, it’s very hard to anticipate when they’ll overwhelm the sources of negative feedback and what form the new equilibrium will take. What I hope I have already done, though, is to demonstrate that, contra Plattner, there’s plenty of room left in the system for fresh waves of democratization. In fact, I think we even have a pretty good sense of where and how those waves are most likely to come.

2014 NFL Football Season Predictions

Professional (American) football season starts tonight when the Green Bay Packers visit last year’s champs, the Seattle Seahawks, for a Thursday-night opener thing that still seems weird to me. (SUNDAY, people. Pro football is played on Sunday.) So, who’s likely to win?

With the final preseason scores from our pairwise wiki survey in hand, we can generate a prediction for that game, along with all 255 other regular-season contests on the 2014 schedule. As I described in a recent post, this wiki survey offers a novel way to crowdsource the problem of estimating team strength before the season starts. We can use last year’s preseason survey data and game results to estimate a simple statistical model that accounts for two teams’ strength differential and home-field advantage. Then, we can apply that model to this year’s survey results to get game-level forecasts.

In the last post, I used the initial model estimates to generate predicted net scores (home minus visitor) and confidence intervals. This time, I thought I’d push it a little further and use predictive simulations. Following Gelman and Hill’s Data Analysis Using Regression and Multilevel/Hierarchical Models (2009), I generated 1,000 simulated net scores for each game and then summarized the distributions of those scores to get my statistics of interest.

The means of those simulated net scores for each game represent point estimates of the outcome, and the variance of those distributions gives us another way to compute confidence intervals. Those means and confidence intervals closely approximate the ones we’d get from a one-shot application of the predictive model to the 2014 survey results, however, so there’s no real new information there.

What we can do with those distributions that is new is compute win probabilities. The share of simulated net scores above 0 gives us an estimate of the probability of a home-team win, and 1 minus that estimate gives us the probability of a visiting-team win.

A couple of pictures make this idea clearer. First, here’s a histogram of the simulated net scores for tonight’s Packers-Seahawks game. The Packers fared pretty well in the preseason wiki survey, ranking 5th overall with a score of 77.5 out of 100. The defending-champion Seahawks got the highest score in the survey, however—a whopping 92.6—and they have home-field advantage, which is worth about 3.1 points on average, according  to my model. In my predictive simulations, 673 of the 1,000 games had a net score above 0, suggesting a win probability of 67%, or 2:1 odds, in favor of the Seahawks. The mean predicted net score is 5.8, which is pretty darn close to the current spread of -5.5.

Seattle Seahawks.Green Bay Packers

Things look a little tighter for the Bengals-Ravens contest, which I’ll be attending with my younger son on Sunday in our once-annual pilgrimage to M&T Bank Stadium. The Ravens wound up 10th in the wiki survey with a score of 60.5, but the Bengals are just a few rungs down the ladder, in 13th, with a score of 54.7. Add in home-field advantage, though, and the simulations give the Ravens a win probability of 62%, or about 3:2 odds. Here, the mean net score is 3.6, noticeably higher than the current spread of -1.5 but on the same side of the win/loss line. (N.B. Because the two teams’ survey scores are so close, the tables turn when Cincinnati hosts in Week 8, and the predicted probability of a home win is 57%.)

Baltimore Ravens.Cincinnati Bengals

Once we’ve got those win probabilities ginned up, we can use them to move from game-level to season-level forecasts. It’s tempting to think of the wiki survey results as season-level forecasts already, but what they don’t do is account for variation in strength of schedule. Other things being equal, a strong team with a really tough schedule might not be expected to do much better than a mediocre team with a relatively easy schedule. The model-based simulations refract those survey results through the 2014 schedule to give us a clearer picture of what we can expect to happen on the field this year.

The table below (made with the handy ‘textplot’ command in R’s gplots package) turns the predictive simulations into season-level forecasts for all 32 teams.* I calculated two versions of a season summary and juxtaposed them to the wiki survey scores and resulting rankings. Here’s what’s in the table:

  • WikiRank shows each team’s ranking in the final preseason wiki survey results.
  • WikiScore shows the score on which that ranking is based.
  • WinCount counts the number of games in which each team has a win probability above 0.5. This process gives us a familiar number, the first half of a predicted W-L record, but it also throws out a lot of information by treating forecasts close to 0.5 the same as ones where we’re more confident in our prediction of the winner.
  • WinSum, is the sum of each team’s win probabilities across the 16 games. This expected number of wins is a better estimate of each team’s anticipated results than WinCount, but it’s also a less familiar one, so I thought I would show both.

Teams appear in the table in descending order of WinSum, which I consider the single-best estimate in this table of a team’s 2014 performance. It’s interesting (to me, anyway) to see how the rank order changes from the survey to the win totals because of differences in strength of schedule. So, for example, the Patriots ranked 4th in the wiki survey, but they get the second-highest expected number of wins this year (9.8), just behind the Seahawks (9.9). Meanwhile, the Steelers scored 16th in the wiki survey, but they rank 11th in expected number of wins with an 8.4. That’s a smidgen better than the Cincinnati Bengals (8.3) and not much worse than the Baltimore Ravens (9.0), suggesting an even tighter battle for the AFC North division title than the wiki survey results alone.

2014 NFL Season-Level Forecasts from 1,000 Predictive Simulations Using Preseason Wiki Survey Results and Home-Field Advantage

2014 NFL Season-Level Forecasts from 1,000 Predictive Simulations Using Preseason Wiki Survey Results and Home-Field Advantage

There are a lot of other interesting quantities we could extract from the results of the game-level simulations, but that’s all I’ve got time to do now. If you want to poke around in the original data and simulation results, you can find them all in a .csv on my Google Drive (here). I’ve also posted a version of the R script I used to generate the game-level and season-level forecasts on Github (here).

At this point, I don’t have plans to try to update the forecasts during the season, but I will be seeing how the preseason predictions fare and occasionally reporting the results here. Meanwhile, if you have suggestions on other ways to use these data or to improve these forecasts, please leave a comment here on the blog.

* The version of this table I initially posted had an error in the WikiRank column where 18 was skipped and the rankings ran to 33. This version corrects that error. Thanks to commenter C.P. Liberatore for pointing it out.

What are all these violent images doing to us?

Early this morning, I got up, made some coffee, sat down at my desk, and opened Twitter to read the news and pass some time before I had to leave for a conference. One of the first things I saw in my timeline was a still from a video of what was described in the tweet as an ISIS fighter executing a group of Syrian soldiers. The soldiers lay on their stomachs in the dirt, mostly undressed, hands on their heads. They were arranged in a tightly packed row, arms and legs sometimes overlapping. The apparent killer stood midway down the row, his gun pointed down, smoke coming from its barrel.

That experience led me to this pair of tweets:

tweet 1

tweet 2

If you don’t use Twitter, you probably don’t know that, starting in 2013, Twitter tweaked its software so that photos and other images embedded in tweets would automatically appear in users’ timelines. Before that change, you had to click on a link to open an embedded image. Now, if you follow someone who appends an image to his or her tweet, you instantly see the image when the tweet appears in your timeline. The system also includes a filter of sorts that’s supposed to inform you before showing media that may be sensitive, but it doesn’t seem to be very reliable at screening for violence, and it can be turned off.

As I said this morning, I think the automatic display of embedded images is great for sharing certain kinds of information, like data visualizations. Now, tweets can become charticles.

I am increasingly convinced, though, that this feature becomes deeply problematic when people choose to share disturbing images. After I tweeted my complaint, Werner de Pooter pointed out a recent study on the effects of frequent exposure to graphic depictions of violence on the psychological health of journalists. The study’s authors found that daily exposure to violent images was associated with higher scores on several indices of psychological distress and depression. The authors conclude:

Given that good journalism depends on healthy journalists, news organisations will need to look anew at what can be done to offset the risks inherent in viewing User Generated Content material [which includes graphic violence]. Our findings, in need of replication, suggest that reducing the frequency of exposure may be one way to go.

I mostly use Twitter to discover stories and ideas I don’t see in regular news outlets, to connect with colleagues, and to promote my own work. Because I study political violence and atrocities, a fair share of my feed deals with potentially disturbing material. Where that material used to arrive only as text, it increasingly includes photos and video clips of violent or brutal acts as well. I am starting to wonder how routine exposure to those images may be affecting my mental health. The study de Pooter pointed out has only strengthened that concern.

I also wonder if the emotional power of those images is distorting our collective sense of the state of the world. Psychologists talk about the availability heuristic, a cognitive shortcut in which the ease of recalling examples of certain things drives our expectations about the likelihood or risk of those things. As Daniel Kahneman describes on p. 138 of Thinking, Fast and Slow,

Unusual events (such as botulism) attract disproportionate attention and are consequently perceived as less unusual than they really are. The world in our heads is not a precise replica of reality; our expectations about the frequency of events are distorted by the prevalence and emotional intensity of the messages to which we are exposed.

When those images of brutal violence pop into our view, they grab our attention, pack a lot of emotional intensity, and are often to hard to shake. The availability heuristic implies that frequent exposure to those images leads us to overestimate the threat or risk of things associated with them.

This process could even be playing some marginal role in a recent uptick in stories about how the world is coming undone. According to Twitter, its platform now has more than 270 million monthly active users. Many journalists and researchers covering world affairs probably fall in that 270 million. I suspect that those journalists and researchers spend more time watching their timelines than the average user, and they are probably more likely to turn off that “sensitive content” warning, too.

Meanwhile, smartphones and easier Internet access make it increasingly likely that acts of violence will be recorded and then shared through those media, and Twitter’s default settings now make it more likely that we see them when they are. Presumably, some of the organizations perpetrating this violence—and, sometimes, ones trying to mobilize action to stop it—are aware of the effects these images can have and deliberately push them to us to try to elicit that response.

As a result, many writers and analysts are now seeing much more of this material than they used to, even just a year or two ago. Whatever the actual state of the world, this sudden increase in exposure to disturbing material could be convincing many of us that the world is scarier and therefore more dangerous than ever before.

This process could have larger consequences. For example, lately I’ve had trouble getting thoughts of James Foley’s killing out of my mind, even though I never watched the video of it. What about the journalists and policymakers and others who did see those images? How did that exposure affect them, and how much is that emotional response shaping the public conversation about the threat the Islamic State poses and how our governments should respond to it?

I’m not sure what to do about this problem. As an individual, I can choose to unfollow people who share these images or spend less time on Twitter, but both of those actions carry some professional costs as well. The thought of avoiding these images also makes me feel guilty, as if I am failing the people whose suffering they depict and the ones who could be next. By hiding from those images, do I become complicit in the wider violence and injustice they represent?

As an organization, Twitter could decide to revert to the old no-show default, but that almost certainly won’t happen. I suspect this isn’t an issue for the vast majority of users, and it’s hard to imagine any social-media platform retreating from visual content as sites like Instagram and Snapchat grow quickly. Twitter could also try to remove embedded images that contain potentially disturbing material. As a fan of unfettered speech, though, I don’t find that approach appealing, either, and the unreliability of the current warning system suggests it probably wouldn’t work so well anyway.

In light of all that uncertainty, I’ll conclude with an observation instead of a solution: this is one hell of a huge psychological experiment we’re running right now, and its consequences for our own mental health and how we perceive the world around us may be more substantial than we realize.

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