Adaptive Sampling Policies Imply Biased Beliefs: A Generalization of the Hot Stove Effect

TL;DR

This paper generalizes the Hot Stove Effect to settings where negative beliefs lead to reduced sampling rather than avoidance, demonstrating that negativity bias persists even with Bayesian learners under these conditions.

Contribution

It extends the Hot Stove Effect theory to include partial sampling biases, showing that negativity bias remains in broader learning scenarios including Bayesian models.

Findings

Negativity bias persists with reduced sampling instead of full avoidance.

Bayesian learners tend to underestimate the true value of alternatives.

The generalized model explains biases in various adaptive learning contexts.

Abstract

The Hot Stove Effect is a negativity bias resulting from the adaptive character of learning. The mechanism is that learning algorithms that pursue alternatives with positive estimated values, but avoid alternatives with negative estimated values, will correct errors of overestimation but fail to correct errors of underestimation. Here, we generalize the theory behind the Hot Stove Effect to settings in which negative estimates do not necessarily lead to avoidance but to a smaller sample size (i.e., a learner selects fewer of alternative B if B is believed to be inferior but does not entirely avoid B). We formally demonstrate that the negativity bias remains in this set-up. We also show there is a negativity bias for Bayesian learners in the sense that most such learners underestimate the expected value of an alternative.