Hagenberg Risk Management Process (Part 3): Operationalization, Probabilities, and Causal Analysis

• URL: http://arxiv.org/abs/2604.09153v1
• Date: Fri, 10 Apr 2026 09:40:07 GMT
• Title: Hagenberg Risk Management Process (Part 3): Operationalization, Probabilities, and Causal Analysis
• Abstract summary: We bridge the gap between qualitative risk analysis and realtime risk management by extending Bowtie diagrams into a formal probabilistic runtime model.<n>Realtime Risk Studio transforms Bowtie structures into a directed acyclic graph (DAG) suitable for Bayesian inference.<n>Probability Capture automatically generates questionnaires from a DAG model so experts can provide estimates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: For risks that cannot be accepted, sufficiently mitigated, or eliminated, continuous observation is a viable approach but requires a model that can be operationalized. The Hagenberg Risk Management Process bridges this gap between qualitative risk analysis, using contextualized polar heatmaps (triage), and realtime risk management by extending Bowtie diagrams into a formal probabilistic runtime model. We introduce Realtime Risk Studio, a domain-specific modeling tool that (i) transforms Bowtie structures (causes, top event, barriers, consequences) into a directed acyclic graph (DAG) suitable for Bayesian inference, (ii) adds explicit safe-state semantics, and (iii) designates Activation Nodes as intervention points. Bowtie models are qualitative; however, Bayesian inference requires actual probabilities. As a second contribution, we present Probability Capture, a tool that complements our Realtime Risk Studio by automatically generating questionnaires from a DAG model so experts can provide estimates. The tool analyzes disagreement and aggregates conditional-probability assessments using both descriptive dispersion analysis and prior-regularized methods. Causal analysis can then provide insights into the DAG model, for example, via d-separation, adjustment-set inspection, do-calculus for what-if analysis, local independence checks, evidence updating, and impact-oriented searches for effective interventions. This workflow is illustrated with an instant-payments gateway scenario, demonstrating (a) explicit safe-state semantics, (b) Bowtie-to-DAG operationalization, (c) probability capture with visible expert noise, and (d) causal what-if analysis on a transformed and enriched model. Rather than presenting a statistical validation, the paper contributes a method and prototype system that transforms partially mitigated risks into observable, probabilistic, and intervention-ready models.

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