🤖 AI Summary
This study addresses the challenge of managing residual risks that cannot be fully eliminated, noting that existing qualitative analyses lack actionable dynamic management mechanisms. To bridge this gap, the authors propose formalizing the Bowtie risk diagram as a directed acyclic graph (DAG) capable of supporting Bayesian inference and causal intervention. By incorporating safety-state semantics and explicit intervention nodes, and integrating expert probability assessments with do-calculus, the framework enables risks to be observable, quantifiable, and intervenable. The work introduces Realtime Risk Studio—a modeling tool—and Probability Capture—a method for eliciting probabilistic judgments—to construct, for the first time, an executable real-time risk reasoning model. Validation in an instant payment gateway scenario demonstrates the efficacy of transforming Bowtie diagrams into DAGs, fusing noisy expert probabilities, and performing “What-if” causal intervention analyses.
📝 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.