Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Kurvers, RHJM; Herzog, SM; Hertwig, R; Krause, J; Moussaid, M; Argenziano, G; Zalaudek, I; Carney, PA; Wolf, M.
How to detect high-performing individuals and groups: Decision similarity predicts accuracy.
Sci Adv. 2019; 5(11):eaaw9011
Doi: 10.1126/sciadv.aaw9011
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- Co-authors Med Uni Graz
- Zalaudek Iris
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- Abstract:
- 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Algorithms - administration & dosage
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