Neuro-Bayesian Architecture in Economic Modeling: Overcoming Agent System Limitations via Latent Variable Integration

In the context of the modern digital economy and the exponential growth of unstructured data volumes, an automation paradox is observed: despite the increase in computational power, standard "out-of-the-box" Agentic AI solutions demonstrate a decline in predictive accuracy in tasks containing latent variables. This study proposes and substantiates a new "Neuro-Bayesian Monte Carlo" (NB-MC) methodology, which integrates the semantic capabilities of Large Language Models (LLMs) with Bayesian inference mechanisms for processing multimodal data.

Drawing on the empirical basis of 2024–2025 research, specifically works on economic productivity scaling laws and agent system limitations, we formulate the hypothesis that overcoming the limitations of agentic systems in actuarial analysis with hidden visual variables is possible through the integration of LLM and Bayesian inference. To test this hypothesis, a large-scale simulation (N=50 iterations, 2,500 observations) was conducted using synthetic insurance portfolio data.

Experimental results indicate that the proposed architecture, utilizing an internal model self-verification mechanism (Gnosis) to weight extracted signals, increases the Normalized Gini Coefficient from 0.486 (Baseline Agent) to 0.746 (Neuro-Bayesian Agent), representing a 53.5% efficiency gain. The study demonstrates the necessity of transitioning from fully automated agents to hybrid systems capable of epistemic self-reflection.