From Meta-Reasoning to Model-Based Thinking: Grounding Generative AI in Bayesian Networks

Abstract

Large language models reason at a meta level, operating several layers of abstraction removed from the problem domain. Their outputs reflect statistical regularities in how humans describe and reason about the world, not the world itself. This talk explores how Bayesian networks can serve as an interpretive bridge between the high-dimensional semantic space of LLMs and the explicit, causal structure of decision-analytic models. By extracting and encoding knowledge from generative AI into Bayesian networks, we can relocate reasoning from opaque statistical representations to transparent, domain-anchored models. The resulting framework enables explicit causal inference, uncertainty quantification, and decision optimization, capabilities absent in pure LLM reasoning. This hybrid paradigm suggests a path toward explainable AI systems that combine linguistic breadth with formal reasoning depth.

About the Presenter

Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy, having worked with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan across North America, Europe, and Asia. As Managing Partner of Bayesia USA and Bayesia Singapore, he is widely recognized as a thought leader in applying Bayesian networks to research, analytics, and decision-making. Together with his business partner, Dr. Lionel Jouffe, he co-authored Bayesian Networks & BayesiaLab — A Practical Introduction for Researchers, an influential resource now widely cited in academic literature. With their deep expertise in Bayesian networks for Key Driver Analysis and Optimization, Stefan and Lionel are highly sought-after consultants, advising global leaders such as Procter & Gamble, Coca-Cola, UnitedHealth Group, L’Oréal, the World Bank, and many of the world’s largest market research firms.