How to detect high-performing individuals and groups: Decision similarity predicts accuracy

R. H.J.M. Kurvers, S. M. Herzog, R. Hertwig, J. Krause, M. Moussaid, G. Argenziano, I. Zalaudek, P. A. Carney, M. Wolf

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article numbereaaw9011
JournalScience Advances
Volume5
Issue number11
DOIs
StatePublished - Nov 20 2019

ASJC Scopus subject areas

  • General

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