Free Seminar: Beyond Black-Box AI—Bayesian Networks for Defense Analysis
Free Workshop in Arlington, Virginia
December 5, 2025, from 10:00 p.m. to 12:00 p.m. (ET, UTC−05:00)
Virginia Tech Executive Briefing Center, Foggy Bottom Room, 900 N. Glebe Road,Arlington, VA 22203
Abstract
Large language models (LLMs) are increasingly used by analysts for drafting, summarizing, and exploring courses of action. Yet they remain statistical language systems rather than explicit representations of operational environments. As a result, they cannot provide the transparent, auditable reasoning that military and intelligence contexts require, especially when decisions must withstand later review or adversarial scrutiny.
This talk introduces a novel, hybrid analytic approach that integrates LLMs with Bayesian networks to generate explicit, reproducible models of uncertainty and causality. In contrast to traditional practice, where AI outputs and quantitative models often remain separate, this method systematically incorporates LLM-generated hypotheses into a formal probabilistic structure that can be inspected, validated, and audited. Bayesian networks then provide quantified uncertainty, value-of-information analysis, and principled comparisons of acting now versus waiting for additional intelligence, thereby integrating disciplines to extend conventional analytic paradigms.
Examples from joint operations, diplomatic negotiation, and search-and-rescue illustrate how this combined framework unifies human expertise, empirical data, and LLM insights within a single decision model. The result is a practical and rigorous way to enhance analytic effectiveness in national security contexts, particularly when time is limited and assumptions must be clearly understood and defensible.
About the Presenter
Stefan Conrady
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.