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

Area of Science:

• Decision science
• Cognitive psychology
• Behavioral economics

Background:

• Accurate assessment of individual and group performance is vital in high-stakes situations.
• Traditional performance evaluation relies on historical data, which is often unavailable in real-world scenarios.
• Binary decision problems are common across various domains, necessitating effective performance assessment methods.

Purpose of the Study:

• To develop and test a novel approach for distinguishing high-performing individuals and groups when historical performance data is absent.
• To investigate the predictive power of decision similarity as a heuristic for performance accuracy.
• To provide a broadly applicable method for improving decision-making systems.

Main Methods:

• A combined theoretical and empirical approach was employed.
• Mathematical arguments and numerical simulations were used to establish the theoretical link between decision similarity and accuracy.
• Large datasets from diverse fields (medical diagnostics, geopolitical forecasting, general knowledge) were used for empirical validation.

Main Results:

• Decision similarity to others was found to be a powerful predictor of individual decision accuracy.
• Empirical testing across multiple datasets confirmed that decision similarity robustly identifies high-performing individuals and groups.
• The findings demonstrate the effectiveness of decision similarity as a performance indicator.

Conclusions:

• Decision similarity offers a simple and broadly applicable heuristic for identifying high performers in the absence of historical data.
• This approach can significantly enhance the effectiveness of decision-making systems in various high-stakes contexts.
• The study provides a practical tool for improving performance assessment and decision quality.