What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?

Geopolitical forecasting tournaments have stimulated the development of methods for improving probability judgments of real-world events. But these innovations have focused on easier-to-quantify variables, like personnel selection, training, teaming, and crowd aggregation—bypassing messier constructs, like qualitative properties of forecasters’ rationales. Here, we adapt methods from natural language processing (NLP) and computational text analysis to identify distinctive reasoning strategies in the rationales of top forecasters, including: (a) cognitive styles, such as dialectical complexity, that gauge tolerance of clashing perspectives and efforts to blend them into coherent conclusions and (b) the use of comparison classes or base rates to inform forecasts. In addition to these core metrics, we explore metrics derived from the Linguistic Inquiry and Word Count (LIWC) program. Applying these tools to multiple tournaments and to forecasters of widely varying skill (from Mechanical Turkers to carefully culled “superforecasters”) revealed that: (a) top forecasters show higher dialectical complexity in their rationales and use more comparison classes; (b) experimental interventions, like training and teaming, that boost accuracy also influence NLP profiles of rationales, nudging them in a “superforecaster” direction.