Publication Date:
2019
Description:
〈p〉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.〈/p〉
Electronic ISSN:
2375-2548
Topics:
Natural Sciences in General
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