Quantum-like Bayesian networks are frameworks that integrate concepts from quantum probability with classical Bayesian networks to model uncertain information and decision-making processes. They extend traditional Bayesian networks by incorporating quantum features such as superposition and entanglement, enabling more accurate representations of cognitive processes and decision outcomes. This approach highlights how human cognition and behavior can exhibit non-classical characteristics, thereby influencing strategies in decision-making and cognitive modeling.
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