Below Idean Salehyan grapples with the question whether forecasting conflict can help to make better foreign policy decisions?
Most social scientists are concerned with explaining the behavior of individuals and groups: Why do some people commit crime? What explains the organizational decisions of firms? Why are some countries more democratic than others? However, an increasing number of articles on violent conflict have turned their attention to predicting things like war and state collapse rather than simply explaining their occurrence (here are a few examples: 1, 2, 3, and 4). Forecasts guide many of our decisions, such as what to wear tomorrow and where to invest our money. But can forecasting conflict and war help to make better foreign policy decisions?
The advocates of conflict forecasting are often explicit about their desire to be ‘policy relevant’ by drawing attention to potential hot spots. Indeed, some of these efforts are funded by government agencies, such as the US Pentagon, which would like to develop better crystal balls (see here and here). Conflict scholars have long made arguments about what may transpire in the future, even if they are not explicitly engaged in forecasting. These assessments about troubled areas and potential violence are certainly useful and have undoubtedly played an important role in policy debates. Yet, scholars would do well to consider some of the normative issues involved in prognostication.
For now, I will leave aside methodological concerns that arise in debates about forecasting, such as the ‘black swan’ problem (or the difficultly of predicting very rare but influential events, such as the ‘Arab Spring’). Assuming we can devise a method by which we are reasonably confident in our ability to forecast conflict—albeit with some error—what should we do with such knowledge? What ethical and practical issues arise when using forecasts to guide policy? I argue that scholars cannot be aloof from the real-world implications of their work, but must think carefully about the potential uses of forecasts.
First and foremost is the issue of false positives and false negatives. False positives—or predicting that an event will occur when it actually does not—is relatively costless if it causes you to carry an umbrella even though it doesn’t rain. However, what level of precision is needed before making life and death decisions? If it is foretold that there is an 80% chance that North Korea will attack the South in the next year, is this sufficient to launch a preemptive strike? Is 95% or 99% confidence a better benchmark? Decisions based upon beliefs about the future always have a degree of uncertainty associated with them, but there is no clear ethical standard for assessing the tradeoff between thousands of lives and the risk of being on the wrong side of the probability distribution. In addition, actions to forestall a conflict or prevent a terrorist attack make ex post assessments extremely difficult: did the event not occur because of the policy intervention, or because of a false positive? We can never know if a forecast is truly accurate if knowledge of the future meaningfully shapes that very future.
False negatives—failing to predict events—raise problems of their own. If we are too wedded to them, forecasts may cause us to ignore potential problem areas. To use the weather example again, the forecast may have predicted sunny skies but if one were to see heavy grey clouds it would be wise to carry an umbrella nonetheless. In addition, makers of policy would do well to pay attention to improbable but significant events. If there be a 0.01% predicted probability that a terrorist will release a biological agent in New York, killing hundreds of thousands of people, most would agree that some preventative measures should be taken. Forecasts themselves do not give us a standard for when to take action.
Rather than preemptive strikes and interventions, forecasts could simply help policy makers take precautionary measures. But here we face the potential for self-fulfilling prophecies. Actions taken to forestall or prevent conflict may lead to a feedback loop, or spiral of events that make conflict more likely to occur than it would have otherwise. Say for example, that in response to a predicted North Korean strike, South Korean, Japanese and US warships were put on alert. This would likely be seen as a provocative move, leading to countermeasures by Pyongyang and Beijing. One can see how such a chain of events could easily lead to missteps and errors, making war more likely.
To these concerns, many forecasters would likely respond that false positives and false negatives are unavoidable, but that their precise statistical methodology, which gives levels of confidence in the estimate, are no doubt superior to ‘educated guessing’. This is a convincing point. But in addition, some would argue that it is up to others to decide the level of confidence they need before acting and what specific measures to take—the scholar’s job is only to inform policy, not make normative judgments. This argument is less satisfying. The same scientific precision that makes statistical forecasts better than ‘gut feelings’ makes it even more imperative for scholars to engage in policy debates. Because statistical forecasts are seen as more scientific and valid they are likely to carry greater weight in the policy community. I would expect—indeed hope—that scholars care about how their research is used, or misused, by decision makers. But claims to objectivity and coolheaded scientific-ness make many academics reluctant to advocate for or against a policy position.
Forecasting war is not like forecasting the weather or predicting who will win the next Presidential election. On this issue, science cannot be divorced from morality. Researchers must be attuned to the real-world implications of their findings and be bold enough to take stand (see here on the ethics of forecasting). If social scientists will not use their research to engage in policy debates about when to strike, provide aid, deploy troops, and so on, others will do so for them. Conflict forecasting should not be seen as value-neutral by the academic community—it will certainly not be seen as such by others.
ps. Jay Ulfelder has just posted a thoughtful reply.