Order Effects in Bayesian Updates

TL;DR

This paper introduces a Bayesian update model to explain order effects in probability judgments, showing how prior beliefs influence the presence of order effects and contrasting it with quantum models.

Contribution

The paper proposes a Bayesian model for order effects, providing new insights into conditions affecting their occurrence and comparing it to quantum models.

Findings

Order effects depend on prior beliefs about question correlation.

Certain priors limit the occurrence of order effects.

The Bayesian model has fewer parameters than quantum models.

Abstract

Order effects occur when judgments about a hypothesis's probability given a sequence of information do not equal the probability of the same hypothesis when the information is reversed. Different experiments have been performed in the literature that supports evidence of order effects. We proposed a Bayesian update model for order effects where each question can be thought of as a mini-experiment where the respondents reflect on their beliefs. We showed that order effects appear, and they have a simple cognitive explanation: the respondent's prior belief that two questions are correlated. The proposed Bayesian model allows us to make several predictions: (1) we found certain conditions on the priors that limit the existence of order effects; (2) we show that, for our model, the QQ equality is not necessarily satisfied (due to symmetry assumptions); and (3) the proposed Bayesian model has the advantage of possessing fewer parameters than its quantum counterpart.