Weirdly enough, people would rather listen to an advisor who's usually 'right,' even if following their advice actually makes things worse for them.
March 16, 2026
Original Paper
Learning to choose between advisors, algorithmic and human, over repeated interactions.
PsyArXiv · uqbce_v1
AI-generated illustration
The Takeaway
When choosing between a human and an algorithm, people prioritize 'frequency of being right' over 'total value gained.' This means we will trust a source that gives us small wins often, even if it causes us to lose more money or resources in the long run compared to a less frequent but more accurate advisor.
From the abstract
People increasingly consult algorithmic aids repeatedly, yet most evidence on algorithm aversion/appreciation comes from one-shot decisions. Across five preregistered incentive-compatible studies (Prolific; N=1,351), we examine how people learn whom to trust when advisors disagree. Study 1 elicits advice from experienced participants, revealing a bias towards the option that is better most of the time, even when it’s worse in expectation. Studies 2–5 then paired this human advice with algorithms