# Markov versus quantum dynamic models of belief change during evidence monitoring

## TL;DR

This study empirically compares Markov and quantum dynamic models of belief change during evidence monitoring, revealing that a combination of features from both models best explains observed interference effects.

## Contribution

It provides the first empirical evaluation of quantum versus Markov models in belief change, demonstrating the need for a hybrid approach.

## Key findings

- Quantum model predicts interference effects in belief updating.
- Markov model alone cannot account for interference effects.
- A hybrid model combining features of both models fits the data best.

## Abstract

Two different dynamic models for belief change during evidence monitoring were evaluated: Markov and quantum. They were empirically tested with an experiment in which participants monitored evidence for an initial period of time, made a probability rating, then monitored more evidence, before making a second rating. The models were qualitatively tested by manipulating the time intervals in a manner that provided a test for interference effects of the first rating on the second. The Markov model predicted no interference whereas the quantum model predicted interference. A quantitative comparison of the two models was also carried out using a generalization criterion method: the parameters were fit to data from one set of time intervals, and then these same parameters were used to predict data from another set of time intervals. The results indicated that some features of both Markov and quantum models are needed to accurately account for the results.