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BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference

arxiv.org · 39,829 words · saved by 2 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. HTML conversions sometimes display errors due to content that did not convert correctly from the source. This paper uses the following packages that are not yet supported by the HTML conversion tool. Feedback on these issues are not necessary; they are known and are being worked on. Authors: achieve the best HTML results from your LaTeX submissions by following these best practices. The proliferation of scientific literature and the accelerating complexity of epistemic discourse have outpaced the evaluative capacities of both human scholars and conventional artificial intelligence systems. In response, we propose Bayesian Epistemology with Weighted Authority (BEWA), a computational architecture for truth-oriented knowledge modelling. BEWA formalises beli

BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference Craig S. Wright Department of Computer Science University of Exeter cw881@exeter.ac.uk (June 19, 2025) Abstract The proliferation of scientific literature and the accelerating complexity of epistemic discourse have outpaced the evaluative capacities of both human scholars and conventional artificial intelligence systems. In response, we propose Bayesian Epistemology with Weighted Authority (BEWA), a c

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