2019/11/01 by Ralf H. J. M. Kurvers, Stefan M. Herzog, Ralph Hertwig +6 · 1 voice · 1 citation
Business, Management and Accounting · Computer Science · Decision Sciences · #Advanced Text Analysis Techniques #Competitive and Knowledge Intelligence #Forecasting Techniques and Applications
paper · pdf · doi:10.1126/sciadv.aaw9011
openalex publication_date 2019/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Distinguishing between high- and low-performing individuals and groups is of prime importance in a wide range of high-stakes contexts. While this is straightforward when accurate records of past performance exist, these records are unavailable in most real-world contexts. Focusing on the class of binary decision problems, we use a combined theoretical and empirical approach to develop and test a approach to this important problem. First, we use a general mathematical argument and numerical simulations to show that the similarity of an individual's decisions to others is a powerful predictor of that individual's decision accuracy. Second, testing this prediction with several large datasets on breast and skin cancer diagnostics, geopolitical forecasting, and a general knowledge task, we find that decision similarity robustly permits the identification of high-performing individuals and groups. Our findings offer a simple, yet broadly applicable, heuristic for improving real-world decision-making systems.