Correlated Risk Quantification in Networked Asset Dependencies
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Solution Overview
Problem
Existing methods for reliability and risk analysis in networked environments assume uncorrelated and independent failures, leading to inaccuracies and understated risks due to the complexity of dependency relationships.
Innovation Solution
The development of methods and systems to identify and quantify correlated risk in networks by capturing relationships among entities, assets, and dependencies using a dependency graph, and conducting Monte Carlo simulations to propagate disruptions and assess aggregate losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional reliability analysis methods assuming independent failures are used, then analysis simplicity is improved, but risk assessment accuracy deteriorates
Solution Approach 1:
The system segments the network into discrete assets, dependencies, and entities, representing them as separate nodes in a dependency graph. This segmentation allows the complex interdependencies to be modeled systematically while maintaining analytical tractability through structured representation.
Solution Approach 2:
The patent introduces a dependency graph as an intermediary structure that captures relationships between assets and dependencies. This graph serves as a mediator between the complex real-world network and the risk analysis process, enabling accurate propagation of disruptions through defined relationship paths.
2Measurement precision
If dependency relationships are mapped comprehensively using dependency graphs and Monte Carlo simulations, then risk quantification accuracy is improved, but computational complexity worsens
Solution Approach 1:
The system performs preliminary actions by pre-mapping the dependency graph structure and pre-identifying critical paths and relationships before conducting risk simulations. This preliminary structuring reduces the computational burden during actual risk analysis by having the framework ready to propagate disruptions efficiently.
Solution Approach 2:
The Monte Carlo simulation framework automatically propagates disruptions through the dependency graph and calculates aggregate risks without requiring manual intervention for each scenario. The system self-services by systematically exploring the sample space and computing risk metrics through automated probabilistic propagation.
Data Source
AI summary
Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.