Order Effects in Bayesian Updates
This addresses a cognitive bias issue in decision-making for psychology and AI researchers, but it is incremental as it builds on existing Bayesian and quantum models.
The paper tackled the problem of order effects in probability judgments by proposing a Bayesian update model, showing that order effects arise due to prior beliefs about question correlation and deriving conditions that limit their existence.
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.