AI Article Synopsis
- Quantum active Bayesian inference and quantum Markov blankets help effectively model complex behaviors seen in natural agents interacting with their environments.
- These frameworks allow for better understanding of irrational decision-making by optimizing free energy within a cognitive structure that doesn't adhere strictly to realism.
- Observations that are inconsistent or violate established theories can provide important, testable insights into real-world phenomena.
Article 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.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1017/S0140525X24000244 | DOI Listing |
Publication Analysis
Top Keywords
quantum markov
12
markov blankets
12
classical inferential
8
inferential paradoxes
8
free energy
8
quantum
4
blankets meta-learned
4
meta-learned classical
4
paradoxes suboptimal
4
suboptimal free
4
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!