Hierarchical Risk Model Simulation with Time-Adjusted Values
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Solution Overview
Problem
Existing risk modeling techniques fail to accurately simulate risks over time and identify component uncertainty contributions, leading to inaccurate projections and a lack of focus on reducing uncertainty in risk models, especially due to the influence of external factors.
Innovation Solution
A computer system and method for simulating a risk model over time using a hierarchical tree of component assumption objects with parent-child relationships, where assumption objects correspond to distribution functions and time functions, allowing for sampling, instantaneous value calculation, and time-adjusted value generation to accurately model and project risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk modeling techniques are used to project risks over time, then the modeling process is simple, but the accuracy of risk projections deteriorates
Solution Approach 1:
The risk model is segmented into a hierarchical tree structure with parent and child assumption objects. Each assumption object represents a specific risk factor or parameter, allowing the complex risk model to be broken down into manageable, independently simulatable components while maintaining overall accuracy.
Solution Approach 2:
The simulation system dynamically adjusts values across multiple time periods by propagating changes from child to parent assumption objects. This dynamic approach allows the model to adapt to changing conditions over time, improving projection accuracy compared to static traditional models.
2Reliability
If component uncertainty contributions are not identified, then the risk model remains simple, but the ability to reduce uncertainty deteriorates
Solution Approach 1:
The hierarchical tree structure segments the overall uncertainty into contributions from individual child assumption objects. This segmentation enables identification of which specific components contribute most to total uncertainty, allowing targeted efforts to reduce uncertainty where it matters most.
Solution Approach 2:
The simulation provides feedback on uncertainty contributions from each child assumption object to the parent objects. This feedback mechanism enables users to understand the source of uncertainty and make informed decisions about where to focus analysis efforts to improve model reliability.
3Measurement precision
If external factors are not considered, then the model is easier to manage, but the accuracy of risk understanding deteriorates
Solution Approach 1:
External factors are segmented into separate child assumption objects within the hierarchical tree. This allows them to be clearly identified and managed as distinct components, maintaining ease of operation while accurately capturing their impact on parent risk metrics.
Solution Approach 2:
The child assumption objects act as intermediaries between external factors and parent risk metrics. This intermediary structure allows external factors to be systematically incorporated into the model without directly complicating the parent-level risk calculations, balancing accuracy with manageability.
Data Source
AI summary
Techniques for a modeling platform associated with simulating risk models. According to certain aspects, systems and methods include simulating the risk model over time. The risk model may include a hierarchical tree formed of component assumption objects associated with distribution functions. The systems and methods may apply a time function to generate time-adjusted values for the component assumption objects corresponding to defined distribution functions. The systems and methods may then combine the time-adjusted values to generate time-adjusted values for parent assumption objects.