When Does Advisor Confidence Improve Decisions? Evidence from Human and Algorithmic Advice

Confidence often accompanies advice, but its usefulness depends on what confidence actually reveals. This paper distinguishes between two dimensions of confidence quality: discrimination, that is, whether confidence tracks correctness at the decision level, and calibration, that is, whether average confidence matches average accuracy. In a controlled advice-taking experiment comparing human and algorithmic advisors, discrimination is the main driver of both advice adoption and post-advice accuracy, whereas calibration plays a more limited role. Source matters only in a specific case: when discrimination is high, participants are more likely to follow overconfident algorithmic advice than equally overconfident human advice. Advice taking also varies with participants’ own metacognitive characteristics. Higher discrimination ability is associated with more conservative advice taking, while better-calibrated participants rely more on stated confidence, benefiting when advisor confidence has high discrimination and performing worse when it is miscalibrated.