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

Published online by Cambridge University Press:  23 September 2024

Kevin B. Clark Open the ORCID record for Kevin B. Clark [Opens in a new window]
Kevin B. Clark*
Affiliation:
Cures Within Reach, Chicago, IL, USA [email protected] www.linkedin.com/pub/kevin-clark/58/67/19a https://access-ci.org/ Felidae Conservation Fund, Mill Valley, CA, USA Campus and Domain Champions Program, Multi-Tier Assistance, Training, and Computational Help (MATCH) Track, National Science Foundation's Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support (ACCESS) Expert Network, Penn Center for Innovation, University of Pennsylvania, Philadelphia, PA, USA Network for Life Detection (NfoLD), NASA Astrobiology Program, NASA Ames Research Center, Mountain View, CA, USA Multi-Omics and Systems Biology & Artificial Intelligence and Machine Learning Analysis Working Groups, NASA GeneLab, NASA Ames Research Center, Mountain View, CA, USA Frontier Development Lab, NASA Ames Research Center, Mountain View, CA, USA SETI Institute, Mountain View, CA, USA Peace Innovation Institute, The Hague 2511, Netherlands & Stanford University, Palo Alto, CA, USA Shared Interest Group for Natural and Artificial Intelligence (sigNAI), Max Planck Alumni Association, Berlin, Germany Biometrics and Nanotechnology Councils, Institute for Electrical and Electronics Engineers (IEEE), New York, NY, USA
*
*Corresponding author.
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Abstract

Quantum active Bayesian inference and quantum Markov blankets enable robust modeling and simulation of difficult-to-render natural agent-based classical inferential paradoxes interfaced with task-specific environments. Within a non-realist cognitive completeness regime, quantum Markov blankets ensure meta-learned irrational decision making is fitted to explainable manifolds at optimal free energy, where acceptable incompatible observations or temporal Bell-inequality violations represent important verifiable real-world outcomes.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e150
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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