BeTrueCore: «Dynamic Bayesian Evolution Cycle and Sociotechnical Robustness in the BeTrueCore Modular System»

Abstract

This paper outlines the architecture and methodological foundations of the BeTrueCore modular system, built upon the "Web3 Intuitive Symmetry Methodology" v1.2. Aaddress the critical challenges of destructive sociotechnical pressure, preference falsification, and enforced informational atomization inherent in modern centralized digital ecosystems. This paper is dedicated to the operationalization and mathematical description of the BeTrueCore modular system’s dynamic engine. The article bridges the traditional "verification gap" between abstract ethical-philosophical concepts and their rigorous software implementation. It deploys the concept of a fractal "Build → Measure → Learn" feedback loop, where participants' individual intuitive impulses (the Pulse) are converted into semantic vectors and weighted by the AI-Metronome. Particular attention is paid to the stochastic transition mechanism—the "White Feather Jump"—modeled via the Wiener differential equation as a method for extricating the collective intelligence from algorithmic deadlocks. The study thoroughly examines 9 key sociotechnical risks (including the Dunning-Kruger, Timur Kuran, and Paulo Freire phenomena) and provides systemic responses from the platform modules, secured by ZK-cryptography and Lit Protocol. The paper verifies BeTrueCore as a resilient tool for measuring and Bayesian updating of collective consensus under conditions of inherent systemic noise. The architecture executes a dialectical inversion: transforming isolation from an instrument of suppression into a protective 'digital citadel,' restoring the user’s absolute autonomy over their time and choices under the principle: 'My identity is my castle.' The study formalizes the 'Centaur' hybrid decision-making model. The study formalizes the "Centaur" hybrid decision-making model, where the synergy between non-linear human intuitive potential (Pulse) and AI computational efficiency (Metronom) is mathematically defined through a utility maximization function that accounts for systemic risks and machine ethics. Rather than suppressing the chaotic incoming entropy of the human factor, the algorithmic core converts it into stochastic resonance, enabling the system to escape local minima and converge on global maxima of truth.