From Narratives to Networks: AI-Generated Risk-Centric Causal Networks for Complex Threat Analysis
Thursday, January 22, 2026, from 11:00 a.m. to 12 p.m. (EST, UTC-5)
This webinar will be delivered as a LinkedIn Live event.
This webinar demonstrates how AI supports the development of Risk-Centric Causal Networks (RCCNs) from heterogeneous knowledge sources, including narratives, intelligence-style reporting, and technical analyses, and how these models contribute to mission-relevant risk assessment, escalation analysis, and decision support.
Participants will see how generative AI transforms both hypothetical crisis scenarios and detailed scientific literature into coherent causal models that strengthen decision-making in complex, multi-domain environments.
Part I — Nuclear Crisis Scenario from A House of Dynamite
The first segment showcases an RCCN derived from the 17-minute nuclear missile crisis depicted in the 2025 political thriller A House of Dynamite. Hellixia converts a fictional yet plausible tactical-strategic scenario into an operational RCCN that captures:
- Delayed mid-course detection and compressed sensor-to-shooter timelines
- Interceptor failures and degraded defensive posture
- Conflicting attribution signals, cyber interference, and stealth ambiguity
- High-pressure decision-making under deep uncertainty (retaliate vs. absorb)
- Command-and-control, institutional, and human-factor breakdowns
- Multiple escalation pathways shaped by unresolved decisions in the narrative
This narrative-to-RCCN transformation illustrates how AI can convert unstructured material into structured analytic frameworks for escalation analysis, indicators and warnings (I&W), and red-teaming adversary COAs.
Part II — SBLOCA in Marine Nuclear Power Plants
The second example demonstrates how Hellixia automatically generates an RCCN from an academic study analyzing nuclear accidents in marine nuclear power plants—a context relevant to maritime operations, distributed power architectures, and mission assurance.
Multi-Domain Threat Structure
- Technical system failures (pump failures, valve malfunctions, loss of cooling sources)
- Human and environmental stressors (operator isolation, severe sea states, offshore remoteness)
- Organizational and policy drivers (inadequate procedures, weak oversight, policy gaps)
Preventive and Mitigating Barriers
- Redundant design and rigorous surveillance programs
- Passive safety features that extend response windows
- Human-performance support measures
- Emergency communication architectures
- International information-sharing mechanisms
Consequences Modeled
- Crew casualties, radiation exposure, and marine contamination
- Long-term power-loss effects and operational degradation
- Maritime mission disruption
- Public panic and global reputational impacts
- Regulatory and policy shifts affecting future operations
This case study demonstrates how AI-enabled RCCNs reveal socio-technical interdependencies, quantify systemic vulnerabilities, and support operational risk management, resilience planning, and mission impact assessment.
About the Presenters
Lionel Jouffe
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
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.