2-Step Agent: How a Bayesian Decision Maker Learns from AI-Decision Support
The 2-Step Agent framework models how rational Bayesian decision-makers learn from machine learning predictions in high-stakes fields. It shows that even with a perfect model, a single misaligned prior belief can lead to worse outcomes than no support at all. The framework details two steps: updating beliefs from the prediction and then using those updated beliefs for treatment decisions.