Hierarchical Risk Model Simulation with Qualitative Assumptions
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Solution Overview
Problem
Existing risk modeling techniques fail to accurately simulate and break down the components of profits and losses, leading to inaccurate risk assessments due to unaccounted uncertainty and external factors, which complicates forward or backward projections and identification of key areas for uncertainty reduction.
Innovation Solution
A computer system and method that simulates the impact of qualitative assumptions on a risk model by using a hierarchical tree of assumption objects with parent-child relationships, enabling users to enable or disable qualitative assumptions, sample distribution functions, adjust values based on enabled assumptions, and generate a distribution function for the risk model, thereby providing a more accurate simulation and uncertainty analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional profit and loss modeling is used without breaking down component sources, then the model is simpler to construct, but the accuracy of risk assessment deteriorates
Solution Approach 1:
The patent segments the profit and loss model into hierarchical components, breaking down the bottom line into contributing factors and sub-factors. This segmentation allows the model to maintain simplicity at the top level while capturing detailed risk sources at lower levels, thereby improving accuracy without proportionally increasing construction complexity.
Solution Approach 2:
The patent introduces a hierarchical dimension to the risk model, organizing assumptions and factors across multiple levels (top-level drivers, contributing factors, sub-factors). This dimensional organization allows complex risk relationships to be structured systematically, improving assessment accuracy while providing a manageable framework for model construction.
2Device complexity
If qualitative assumptions are not explicitly modeled, then the model structure is simpler, but the reliability of risk simulation deteriorates
Solution Approach 1:
The patent introduces qualitative assumption objects as intermediary elements that mediate between user judgments and quantitative risk calculations. These assumption objects capture qualitative factors (such as market conditions or regulatory environments) and translate them into quantifiable impacts on risk metrics, thereby improving simulation reliability without requiring complete formalization of every qualitative aspect.
Solution Approach 2:
The patent makes the model structure dynamic by allowing qualitative assumptions to be selectively enabled or disabled and by permitting iterative refinement of assumption relationships. This dynamic approach allows users to adapt the model complexity to their needs while maintaining the capability to capture qualitative factors when necessary, balancing structure simplicity with simulation reliability.
3Loss of time
If component uncertainty contributions are not identified, then the analysis process is simpler, but the ability to reduce overall uncertainty deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that automatically calculate and display the uncertainty contribution of each model component based on simulation results. This feedback provides users with immediate information about which assumptions and factors drive the most uncertainty, enabling targeted refinement efforts that efficiently reduce overall model uncertainty without requiring comprehensive manual analysis of every component.
Data Source
AI summary
Techniques for a modeling platform associated with simulating risk models. According to certain aspects, systems and methods include simulating the impact of qualitative assumptions on the risk model. Accordingly, the risk model may include a hierarchical tree formed of component qualitative assumption objects and quantitative assumption objects. Quantitative assumption objects include indications of distribution function parameters associated with the assumption object and qualitative assumption objects include an indication of an impact on a parent quantitative assumption. When simulating the risk model, systems and methods may adjust sampled values quantitative assumption object when a child qualitative assumption object is enabled.