MCMC Risk Dashboard
Interactive probabilistic risk network visualisation using Markov Chain Monte Carlo simulation for complex risk assessment scenarios.
Core functionality implemented and ready for customisation to specific risk environments.
React, D3.js, WebAssembly (for MCMC computation), TypeScript
Can be deployed and customised for specific organisational contexts within weeks.
What the Dashboard Does
This prototype demonstrates how Bayesian networks and MCMC simulation can transform risk assessment from static matrices to dynamic, interactive exploration tools.
Displays risks as nodes in a network with connections showing probabilistic relationships. Users can explore how changes in one area affect the entire risk landscape.
- Interactive node manipulation and exploration
- Real-time relationship strength indicators
- Conditional probability displays
- Network clustering and grouping
Runs thousands of scenarios using Markov Chain Monte Carlo methods to explore the full space of possible outcomes under uncertainty.
- Posterior distribution sampling
- Convergence diagnostics and validation
- Scenario probability calculations
- Sensitivity analysis across parameters
Interactive scenario exploration allowing users to adjust assumptions and immediately see how changes propagate through the risk network.
- Real-time parameter adjustment
- Evidence insertion and belief updating
- Comparative scenario analysis
- Impact pathway tracing
Identifies which risks have the greatest influence on others, helping prioritise attention and mitigation efforts where they'll have maximum impact.
- Influence strength calculations
- Critical pathway identification
- Leverage point analysis
- Cascade effect prediction
Technical Approach
Bayesian Network Foundation
The dashboard is built on a Bayesian network engine that represents probabilistic relationships between risk factors using directed acyclic graphs.
Network Structure
- • Nodes represent risk factors or events
- • Edges encode conditional dependencies
- • Conditional probability tables define relationships
- • Network topology captures domain expertise
Inference Engine
- • Belief propagation for exact inference
- • MCMC sampling for complex networks
- • Evidence incorporation and updating
- • Query answering and marginalisation
MCMC Implementation
For complex networks where exact inference is computationally intractable, we use Markov Chain Monte Carlo methods to sample from posterior distributions.
Sampling Strategy
For networks with conjugate priors and well-behaved posteriors
For general-purpose sampling when Gibbs isn't applicable
For continuous variables requiring efficient exploration
Interactive Visualisation
The user interface translates complex probabilistic computations into intuitive visual interactions that domain experts can understand and manipulate.
Network Layout
- • Force-directed layout algorithms
- • Hierarchical positioning options
- • Clustering and grouping capabilities
- • Zoom and pan for large networks
Real-time Updates
- • WebAssembly for fast computation
- • Incremental belief updating
- • Progressive result rendering
- • Responsive parameter adjustment
Potential Applications
This prototype can be adapted for various risk assessment contexts. Here are some potential applications we're excited to explore with early adopters.
Model how disruptions propagate through supply networks, identifying critical suppliers and vulnerable pathways.
Understand how security vulnerabilities combine to create attack vectors, prioritising defences based on network effects.
Model how market conditions affect correlations between assets, especially during stress periods when diversification fails.
Explore how operational failures in one area affect others, identifying system-wide vulnerabilities and mitigation priorities.
Map how regulatory changes in one area trigger requirements in others, enabling proactive compliance planning.
Model interactions between physical climate impacts and transition policies, understanding compound risk effects.
Implementation Pathways
Deploy the dashboard for a specific risk domain in your organisation. Learn what works and refine the approach.
- • 4-8 week deployment timeline
- • Custom network configuration
- • User training and support
- • Iterative refinement process
Work with us to enhance the prototype with features specific to your industry or risk environment.
- • Joint development partnership
- • Domain-specific enhancements
- • Shared intellectual property
- • Open source contributions
Access the open source codebase and implement the dashboard within your own technical environment.
- • Full source code access
- • Implementation documentation
- • Community support forums
- • Optional consulting support
Early Adopter Opportunities
We're particularly interested in working with organisations willing to share their experience and help us understand how probabilistic risk assessment performs in real-world environments. Early adopters benefit from preferential pricing, close collaboration, and the opportunity to influence the tool's evolution.
Technical Requirements
Cloud Hosted (Recommended)
We host and maintain the dashboard, you access via web browser.
- • No infrastructure requirements
- • Automatic updates and maintenance
- • Enterprise security and compliance
- • Usage-based pricing
On-Premises Installation
Deploy within your own infrastructure for maximum control.
- • Docker container deployment
- • Kubernetes orchestration support
- • Integration with existing systems
- • Custom security configurations
Data Sources
- • REST API for external data feeds
- • CSV/Excel file import
- • Database connectivity (SQL/NoSQL)
- • Real-time streaming data support
Export and Reporting
- • Interactive report generation
- • PDF/PowerPoint export
- • Embedded dashboard widgets
- • API for programmatic access
User Management
- • Role-based access control
- • Single sign-on (SSO) integration
- • Audit logging and compliance
- • Multi-tenant configuration
Ready to Explore Probabilistic Risk Assessment?
This dashboard represents a new approach to risk assessment that moves beyond traditional matrices to embrace the complexity and uncertainty of real-world risk environments.
What Happens Next?
- 1. Discovery Call - We discuss your risk environment and specific challenges
- 2. Prototype Demo - See the dashboard in action with sample scenarios
- 3. Pilot Planning - Design a focused pilot deployment for your context
- 4. Implementation - Deploy and customise the dashboard for your needs
- 5. Learning Journey - Iterate and refine based on real-world experience