Narratives to Networks — Mission-Critical Reasoning in the Age of AI
From persuasive AI narratives to causal reasoning you can calculate, inspect, and defend.
Singapore Management University, Lee Kong Chian School of Business
50 Stamford Road, Singapore 178899
October 22, 2026, from 2:00 p.m. to 5:00 p.m. (SGT)
AI now produces persuasive, polished analysis on demand, but a recommendation is only as good as the audit trail behind it, and an LLM’s arrives without one. This three-hour seminar gives decision, risk, and policy analysts a framework for placing large language models within the broader modeling landscape, then demonstrates live how Hellixia, BayesiaLab’s generative-AI assistant, turns narratives and documents into fully quantified causal Bayesian networks you can calculate, inspect, and defend.
Seminar Overview
Suppressing the AI Noise
Every week brings new claims about what AI can do for analysis. This seminar offers noise suppression: a framework for judging any analytic technology by asking what kinds of claims it can make, and what it costs to build it, to run it, and, above all, to trust what it says.
What LLMs Know, and What Decisions Demand
In the first part of the seminar, we apply that framework to large language models and locate both their power and their limits. LLMs command an extraordinary breadth of knowledge, but the claims they natively support are associations expressed in language. Decisions demand more: what happens if we act, what would have happened otherwise, and what is it worth to find out before acting.
The Most Expensive Thing in Analysis Is a Free Answer
On costs, the picture is just as asymmetric. An LLM produces a persuasive, well-written recommendation at almost no cost. But decision-makers in accountable institutions know that a conclusion is only as good as its audit trail, and an LLM’s conclusions arrive without one. The reasoning that produced the recommendation is distributed across billions of parameters that no one can read, so the audit must be reconstructed from the outside, by you, at full cost, and again on the next answer, and the next. We call this recurring burden the verification tax.
Skipped Verification Is Not Forgiven, Only Deferred
The tax is easy to skip, because right and wrong answers read equally well. But skipped verification does not disappear. It accumulates quietly until it is collected by whoever benefits from your being wrong: a competitor, a counterparty, an auditor, a review board, a court, or an adversary.
Reasoning You Can Calculate
In the second part, we introduce the complement. Bayesian networks occupy precisely the territory LLMs do not: explicit variables, quantified causal relationships, and calculation, probabilistic and causal inference you can run, not prose you can only read. Their cost structure is the mirror image of the LLM’s: the model is the audit trail, checked once, at build time, by experts who can see every assumption.
From Narratives to Networks, Live
The traditional weakness of Bayesian networks was the cost of building them. That is what generative AI just fixed. We demonstrate live how Hellixia, the generative-AI assistant in BayesiaLab, converts narratives, documents, and prompts into fully quantified causal networks, on which we then enter evidence, simulate interventions, and compute outcome probabilities in front of you, every number traceable to an assumption someone can defend or correct. Each technology repairs the other’s greatest weakness. That is the prudent path through the AI noise.
Venue
The seminar takes place at the Lee Kong Chian School of Business, Singapore Management University, 50 Stamford Road, Singapore 178899.
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.
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