Predicting progress: Intellectual humility and accuracy in forecasts of global welfare

Experts and pundits routinely forecast societal trends, yet these predictions often fall short, leading to poor policy decisions. What distinguishes accurate forecasters? In a three-year longitudinal tournament (N = 520), we tested whether intellectual humility (IH)—recognizing the limits of one’s knowledge—predicts accuracy in forecasting global welfare trends (e.g., armed conflict, CO2 concentrations). While fluid intelligence and political ideology offered limited predictive power, IH consistently predicted accuracy, particularly in volatile domains. High-IH individuals engaged in a self-correcting cycle: they updated predictions more frequently and calibrated uncertainty intervals more effectively. Crucially, and challenging “wisdom of crowds” models, exposure to diverse peer predictions did not improve accuracy; diversity without metacognitive scaffolding provided no benefit. Automated topic modeling confirmed that accurate forecasters focused on base rates and uncertainty, whereas inaccurate ones defaulted to generalized pessimism. Thus, in complex domains, metacognitive discipline predicts foresight more than intellect or information access alone.