A Quantum-like Model for Predicting Human Decisions in the Entangled Social Systems
This work addresses the challenge of modeling human behavior in complex systems like social networks, offering a novel predictive tool for researchers in psychology and social sciences, though it is incremental as it builds on existing quantum-like theories.
The authors tackled the problem of predicting human decisions under uncertainty in social systems by introducing a predictive entangled quantum-like Bayesian network (PEQBN), which outperformed classical Bayesian networks and three recent quantum-like approaches on 22 experimental tasks.
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.