Small Steps to Accuracy: Incremental Belief Updaters Are Better Forecasters

Abstract

Laboratory research has shown that both underreaction and overreaction to new information pose threats to forecasting accuracy. This article explores how real-world forecasters who vary in skill attempt to balance these threats. We distinguish among three aspects of updating: frequency, magnitude, and confirmation propensity. Drawing on data from a four-year forecasting tournament that elicited over 400,000 probabilistic predictions on almost 500 geopolitical questions, we found that the most accurate forecasters made frequent, small updates, while low-skill forecasters were prone to confirm initial judgments or make infrequent, large revisions. High-frequency updaters scored higher on crystallized intelligence and open-mindedness, accessed more information, and improved over time. Small-increment updaters had higher fluid intelligence scores, and derived their advantage from initial forecasts. Update magnitude mediated the causal effect of training on accuracy. Frequent, small revisions provided reliable and valid signals of skill. These updating patterns can help organizations identify talent for managing uncertain prospects.