Bayesian Epistemology with Weighted Authority: A Formal Architecture for Truth-Promoting Autonomous Scientific Reasoning
This addresses the problem of epistemic overload in science for AI systems, proposing a foundational framework for autonomous reasoning, though it appears incremental as it builds on existing Bayesian and network-based methods.
The paper tackles the challenge of processing the vast scientific literature by introducing Bayesian Epistemology with Weighted Authority (BEWA), a formal architecture that models belief as a probabilistic function over scientific claims, resulting in a system that supports truth-promoting machine reasoning with features like audit verification and rational belief convergence.
The exponential expansion of scientific literature has surpassed the epistemic processing capabilities of both human experts and current artificial intelligence systems. This paper introduces Bayesian Epistemology with Weighted Authority (BEWA), a formally structured architecture that operationalises belief as a dynamic, probabilistically coherent function over structured scientific claims. Each claim is contextualised, author-attributed, and evaluated through a system of replication scores, citation weighting, and temporal decay. Belief updates are performed via evidence-conditioned Bayesian inference, contradiction processing, and epistemic decay mechanisms. The architecture supports graph-based claim propagation, authorial credibility modelling, cryptographic anchoring, and zero-knowledge audit verification. By formalising scientific reasoning into a computationally verifiable epistemic network, BEWA advances the foundation for machine reasoning systems that promote truth utility, rational belief convergence, and audit-resilient integrity across dynamic scientific domains.