Human and Algorithmic Predictions in Geopolitical Forecasting: Quantifying Uncertainty in Hard-to-Quantify Domains
Barbara A. Mellers, John McCoy, Louise Lu, Philip E. Tetlock
Abstract
Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon-nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms.