The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks

TL;DR

This paper explores how quantum-like Bayesian networks incorporate acausal semantic similarities, allowing beliefs to be represented in vector spaces without traditional cause-effect explanations.

Contribution

It introduces a model combining causal and acausal relationships via semantic similarities, expanding quantum-like probabilistic graphical models.

Findings

Semantic similarities create acausal connections in Bayesian networks.

Beliefs can be represented in vector spaces using quantum parameters.

Acausality and interference are linked through semantic relationships.

Abstract

We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events. These semantic similarities constitute acausal connections according to the Synchronicity principle and provide new relationships to quantum like probabilistic graphical models. As a consequence, beliefs (or any other event) can be represented in vector spaces, in which quantum parameters are determined by the similarities that these vectors share between them. Events attached by a semantic meaning do not need to have an explanation in terms of cause and effect.