A Quantum-like Model for Predicting Human Decisions in the Entangled Social Systems
Aghdas. Meghdadi, M. R. Akbarzadeh-T., Kourosh Javidan

TL;DR
This paper introduces a novel entangled quantum-like Bayesian network model that predicts human decision-making in social systems more accurately than classical models, by incorporating societal entanglement effects.
Contribution
It presents the first predictive quantum-like Bayesian network model that accounts for societal entanglement in human decision-making processes.
Findings
PEQBN outperforms classical Bayesian networks in prediction accuracy.
PEQBN provides more realistic human decision predictions under uncertainty.
The model successfully captures societal influence effects on decisions.
Abstract
Human-centered systems of systems such as social networks, Internet of Things, or healthcare systems are growingly becoming major facets of modern life. Realistic models of human behavior in such systems play a significant role in their accurate modeling and prediction. Yet, human behavior under uncertainty often violates the predictions by the conventional probabilistic models. Recently, quantum-like decision theories have shown a considerable potential to explain the contradictions in human behavior by applying quantum probability. But providing a quantum-like decision theory that could predict, rather than describe the current, state of human behavior is still one of the unsolved challenges. The main novelty of our approach is introducing an entangled Bayesian network inspired by the entanglement concept in quantum information theory, in which each human is a part of the entire…
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Taxonomy
TopicsQuantum Mechanics and Applications · Paranormal Experiences and Beliefs · Quantum Computing Algorithms and Architecture
