Debiasing training reduces confirmation bias in national risk analysts

Area of Science:

• Cognitive psychology
• Risk analysis
• Decision science

Background:

• State risk forecasts are vital for resource allocation against global threats like pandemics and climate change.
• Human judgment in forecasting is prone to cognitive biases, impacting accuracy.
• Confirmation bias and bias blind spot are key cognitive biases affecting expert judgment.

Purpose of the Study:

• To compare confirmation bias and bias blind spot susceptibility between national risk analysts and master's students.
• To evaluate the effectiveness of a one-shot debiasing training intervention on cognitive biases in both groups.
• To investigate the role of expertise and experience in mitigating cognitive biases in risk forecasting.

Main Methods:

• An experiment was conducted with national risk analysts from a European country and a matched sample of master's students.
• Participants' susceptibility to confirmation bias and bias blind spot was assessed in risk-related and unrelated judgments.
• The impact of a single debiasing training session was evaluated across both participant groups.

Main Results:

• National risk analysts demonstrated significantly less confirmation bias compared to students, both in risk-related and unrelated judgments.
• A single debiasing training intervention effectively reduced confirmation bias in both the analyst and student groups.
• Expertise and experience appear to confer a broader reduction in cognitive bias than previously understood.

Conclusions:

• Cost-effective debiasing interventions can enhance expert judgment in national risk forecasting.
• Experience and expertise play a significant role in reducing cognitive biases.
• Targeted training can improve the reliability of national risk assessments.