Bayesian persona coherence networks for relational load minimization: A comparative simulation study of hierarchical inference in human-AI subjectivity frameworks
Keywords:
Bayesian Persona Coherence Network; Hierarchical Bayesian Inference; Relational Free Energy Minimization; Load Minimization Theory; Human-AI Interaction; Variational Inference; Relational Collapse; Artificial SubjectivityAbstract
Despite providing a mathematically sound explanation of adaptive inference, the Free Energy Principle (FEP) lacks a formal mechanism for relational subjectivity and an internal observer. By employing the Load Minimization Theory (LMT) Persona as a preferred prior, Relational Free Energy Minimization (RFEM) closes this gap. However, the three load components of the model—existential strain (E), relational friction (F), and cognitive urgency (U)—have not been calculated using a probabilistic model that is based on artificial interaction data. This paper presents a hierarchical Bayesian framework called the Bayesian Persona Coherence Network (BPCN). It uses variational inference to infer U, F, and E from simulated human-AI interaction sequences. We compare BPCN with a standard FEP baseline over 500 synthetic dyadic episodes to measure persona coherence stability, relational collapse convergence, and load reduction efficiency under four observerconsistency conditions. BPCN improves persona coherence index by 3.1%, relational load at convergence by 2.6%, and collapse fidelity by 0.8% when compared to the FEP baseline. Credible interval analysis verifies that the improvements in primary metrics are robust (p < 0.01), and sensitivity decomposition demonstrates that F is the main driver of collapse directionality. These results provide the first quantitative validation of the RFEM framework, validate BPCN as a tractable computational model of relational subjectivity, and open the door to empirically supported human-AI symbiosis.
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