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Pooling decisions decreases variation in response bias and accuracy
Ralf H J M Kurvers1,2, Stefan M Herzog1, Ralph Hertwig1
1Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.
Iscience
|July 19, 2021
Summary
Pooling expert decisions in medical and judicial fields significantly reduces variability in accuracy and bias. Collective decision-making systems enhance reliability and fairness by mitigating individual differences among experts.
Area of Science:
- Decision analysis
- Cognitive science
- Expert judgment
Background:
- Individual decision-makers exhibit significant variability in accuracy and response bias across diverse fields like medicine, law, and politics.
- This heterogeneity undermines the reliability and fairness of decision-making systems.
Purpose of the Study:
- To investigate whether collective decision-making systems can overcome the problem of expert heterogeneity.
- To quantify the impact of pooling decisions on reliability and fairness.
Main Methods:
- Theoretical modeling and empirical testing across five distinct domains.
- Analysis of decision pooling in breast cancer diagnostics, skin cancer diagnostics, and fingerprint analysis.
- Measurement of sensitivity, specificity, and response bias variations.
Main Results:
- Pooling decisions of five experts reduced variation in sensitivity by 52% (breast cancer), 54% (skin cancer), and 41% (fingerprint analysis).
- Similar significant reductions were observed for specificity and response bias.
- These improvements were consistent across multiple domains.
Conclusions:
- Collective systems based on pooling decisions effectively mitigate individual decision-maker variability.
- Pooling decisions enhances the reliability and fairness of decision-making systems.
- This approach has the potential to increase trust in expert-driven systems.
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