The Hot Stove Effect: Why AI Learns to Be a Pessimist

The Hot Stove Effect is a negativity bias in learning where people underestimate alternatives after negative experiences, even with smaller sample sizes. This effect applies to various learning algorithms and Bayesian learners. Adaptive sampling policies can result in seemingly biased beliefs without psychological biases in information processing. This theory has implications for understanding judgment biases in risk-taking, trust, and productivity assessments.