Decision Delta Generation for Dynamic Simulation Analytics
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Solution Overview
Problem
Computer modeling of complex systems faces challenges in efficiently adjusting inputs to achieve user-requested changes in predicted outcomes, due to the intricate interactions and dependencies among various data types.
Innovation Solution
A computer-program product that generates a set of decision deltas for a computer model, allowing for the derivation of a derived set of inputs that can affect a user-requested change in the initial predicted state, by analyzing the allowable changes in initial inputs across different data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer modeling is used to predict outcomes for complex systems with multiple inputs, then prediction capability is improved, but adjusting inputs to achieve user-requested changes becomes difficult and time-consuming
Solution Approach 1:
The system pre-calculates and stores decision deltas for multiple inputs before runtime. When a user requests a change in output, the system retrieves pre-computed decision deltas that indicate how much each input should be adjusted to achieve the desired outcome, eliminating the need for time-consuming iterative adjustments during actual use
Solution Approach 2:
The system implements a feedback mechanism where the computer model's output is continuously monitored, and decision deltas are automatically calculated and applied to adjust inputs. This closed-loop approach allows the system to self-correct and reach the desired output state efficiently by using the predicted change as feedback to guide input adjustments
2Reliability
If the number of inputs and data types in the computer model is increased to improve comprehensiveness, then model accuracy is improved, but the complexity of managing and adjusting inputs increases
Solution Approach 1:
The system introduces decision deltas as an intermediary layer between the user's desired output change and the actual input adjustments. Instead of directly managing complex relationships between multiple inputs of different data types, the system uses decision deltas to mediate and simplify the adjustment process, translating high-level goals into specific input modifications
Solution Approach 2:
The system changes the parameter representation from individual input values to decision deltas that represent allowable changes. This parameter transformation simplifies the management complexity by focusing on relative changes rather than absolute values, and by providing a unified way to handle adjustments across different data types through standardized delta representations
Data Source
AI summary
A computing device obtains a computer model that predicts a predicted output for a studied system. The device obtains an initial predicted state for an applied system according to initial inputs to the computer model. The device receives a request for derived inputs that will generate, for the applied system, a user-requested change in the initial predicted state. The device generates decision deltas for the computer model. The device determines allowable function inputs to a computer function. The allowable function inputs are derived based on the decision deltas, the user-requested change, and the computer model. The device computes, using one or more of the allowable function inputs, at least one minimum or maximum value for the computer function. The device outputs output information based on the derived inputs that, according to the computer model, will affect the user-requested change in the initial predicted state.