Can Water Help Predict Conflict?

Forecast Cloudy: A Case Study in Predicting Conflict Risk

Conclusion

Ultimately, conflict is a human activity, and the actions of human beings can be hard to predict. At the same time, conflict does not happen in a vacuum: Even if the specific catalyst of violence can be hard to divine, the conditions most likely to encourage violence are knowable. The Water, Peace and Security team consulted dozens of experts and conducted extensive research to gather data on those conditions, testing which variables were most indicative of future conflict. The model cannot perfectly predict where and when violent conflict will occur, of course, but it can provide insights as to where conflict is likely, and what might be its underlying drivers. One of the most powerful indicators is also one of the most obvious: a history of conflict often begets more conflict.

Nonetheless, such foresight can give local, national, and international actors insights into how to mitigate the risk of violence. And while the model cannot tell an international organization exactly what investment in which local water system will have the greatest impact, a local water authority cannot always access the same expertise as a large international organization when it needs help making such an investment. This tool can help close the information gap between high-level decision makers and at-risk localities or regions. The Water, Peace and Security partnership, in fact, intends to use this model to inform and prioritize its investments in fieldwork in high-risk countries. Inclusion of climate change projections would greatly enhance that work.

While such predictive models hold great promise for understanding the drivers of conflict and how water and other factors affect risk, there are also limits to what these models can or should do. On a practical level, there is that unmappable corner of human nature, as well as highly localized dynamics that cannot necessarily be generalized. There is also an underlying risk that modelers need to take seriously and discuss openly. The aggregation of such a volume of data constitutes a powerful tool that can be used for ends that are good as well as those that are not so good. Model development should bear in mind the medical profession’s cornerstone principle primum non nocere, or “first, do no harm.” Household-level data, for example, may illuminate underlying drivers for instability and discontent with unprecedented clarity—and simultaneously offer unscrupulous actors a detailed road map for targeting restive individuals. The sad truth is that a region at high risk for conflict is likely also at high risk for human rights violations. For that matter, national or international actors that use data to design local interventions without consulting the local population may do as much harm as good. So, while fine-grained, local data might afford a model greater fidelity, designing the model so that its forecasts can be translated into action only with the cooperation and engagement of local actors may ultimately lead to better outcomes.

Finally, it is worth considering that U.S. armed forces make extensive use of such models and simulation. Even though their models are no closer to perfect predictions than this one, they inform billion-dollar decisions about how to organize, train, and equip military forces. Given what is at stake in national defense, being unprepared is not an option, and making such significant decisions without the best available information would be unacceptable. Yet civilian organizations facing issues every bit as significant as those of the military—how to prevent a war or alleviate human suffering—do not routinely utilize such decision support tools. Hopefully, our conflict forecasting website will prove a valuable tool, and encourage the use and expansion of such decision support tools in this sphere.