Quantum Markov blankets for meta-learned classical inferential paradoxes with suboptimal free energy

Area of Science:

• Quantum mechanics
• Cognitive science
• Computational modeling

Background:

• Classical inferential paradoxes pose challenges for agent-based modeling.
• Understanding decision-making in complex, task-specific environments requires advanced theoretical frameworks.

Purpose of the Study:

• To introduce quantum active Bayesian inference and quantum Markov blankets for robust modeling.
• To explain meta-learned irrational decision-making within a non-realist cognitive completeness regime.

Main Methods:

• Utilizing quantum Markov blankets to ensure decision-making fits explainable manifolds.
• Optimizing free energy for meta-learned processes.
• Interfacing agent-based models with task-specific environments.

Main Results:

• Demonstrated robust modeling and simulation of classical inferential paradoxes.
• Showcased how quantum Markov blankets facilitate fitting irrational decision-making to optimal free energy.
• Identified acceptable incompatible observations and temporal Bell-inequality violations as verifiable real-world outcomes.

Conclusions:

• Quantum active Bayesian inference and quantum Markov blankets offer a powerful framework for complex cognitive modeling.
• The approach provides verifiable real-world outcomes, including temporal Bell-inequality violations.
• This research bridges quantum theory and cognitive science for advanced AI and agent-based systems.