  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 society.
Accordingly, society's effect on the dynamic evolution of the decision-making
process, which is less often considered in decision theories, is modeled by the
entanglement measures. The proposed predictive entangled quantum-like Bayesian
network (PEQBN) is evaluated on 22 experimental tasks. Results confirm that
PEQBN provides more realistic predictions of human decisions under uncertainty,
when compared with classical Bayesian networks and three recent quantum-like
approaches.
