Sparse Stochastic Roll-Up for Monte Carlo Simulation Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current stochastic simulation methods require excessive processing resources and time when dealing with large numbers of simulations, making it cumbersome to manage and perform calculations efficiently, especially when modeling rare events across multiple entities.
Innovation Solution
The implementation of sparse stochastic roll-up techniques, which store and compute only significant events meeting specified criteria, allowing for the aggregation of risks while preserving statistical relationships between entities, reducing data and computation requirements by a factor of approximately 1,000.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Monte Carlo simulation methods are used to model large numbers of entities with many trials, then comprehensive risk assessment coverage is achieved, but processing resources and computation time become excessively high
Solution Approach 1:
The patent extracts and stores only the significant non-zero trial outcomes from the full Monte Carlo simulation into a database, rather than retaining all trial data. This extraction of essential information reduces data volume while preserving the ability to perform comprehensive risk assessments on demand.
Solution Approach 2:
The patent performs preliminary aggregation of simulation results by storing pre-computed non-zero outcomes in a database before actual analysis is needed. This preliminary action allows the system to avoid re-computing full simulations during subsequent risk assessments, significantly improving computational efficiency.
2Loss of information
If full simulation trial data is stored for all entities, then complete statistical information is preserved, but data storage requirements become excessive
Solution Approach 1:
The system extracts only the non-zero trial outcomes from the full simulation data and stores these in a database. By eliminating the storage of zero-value outcomes, the data volume is reduced by a factor of approximately 1,000 while the essential statistical information needed for risk assessment remains intact.
Solution Approach 2:
The patent changes the parameter representation by storing aggregated non-zero outcomes with their associated metadata (entity identifiers, trial numbers, outcome values) rather than storing complete trial vectors. This parameter transformation enables efficient storage and retrieval while preserving statistical relationships.
3Measurement precision
If comprehensive simulations are performed for all entities, then accurate statistical relationships are captured, but computation time increases significantly
Solution Approach 1:
The patent performs the computationally intensive simulation work in advance and stores the results. When risk assessments are needed, the system retrieves pre-computed non-zero outcomes from the database rather than performing new simulations, reducing computation time from hours to seconds while maintaining statistical accuracy.
Solution Approach 2:
The system creates a compressed representation (copy) of the simulation results by storing only non-zero outcomes with their essential metadata. This copied data structure preserves the statistical relationships needed for accurate risk assessment while enabling rapid retrieval and analysis.
Data Source
AI summary
When storing the results of a very large number of stochastic simulation trials of rare events, the amount of data involved may be prohibitive. Sparse and Non-Congruent Stochastic Roll-up are methods for decomposing and storing the results from Monte Carlo simulations such that the data stored only reflects the trials on which a risk event occurred, or focuses attention on some trials over other trials. When the need arises to view or calculate with the fully expressed data set, the results may be aggregated while maintaining statistical relationships between the components of the simulation.