Causal Network Data Structure for Time-Delay Modeling
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Solution Overview
Problem
Conventional causal networks fail to accurately model causal propagation strength and delay, making it difficult to represent complex causal relationships involving time delays.
Innovation Solution
A data structure and system for representing causal relationships that include target identification, index identification, causal relationship information with direction, strength, time, and correlation information, using transfer entropy to evaluate causal strength and delay times between indexes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional causal networks are used to represent causal relationships, then the structure is simple and easy to understand, but the propagation strength and propagation delay cannot be accurately modeled
Solution Approach 1:
The patent segments the causal relationship representation into multiple distinct components: direction information (cause-effect orientation), strength information (causal strength magnitude), time information (propagation delay), and correlation information (direction of change). This segmentation allows each aspect to be modeled independently with appropriate data structures, achieving comprehensive accuracy without overwhelming complexity.
Solution Approach 2:
The patent adds multiple dimensions to the traditional causal network representation by introducing time delay as a temporal dimension, causal strength as a magnitude dimension, and correlation direction as an orientational dimension. This multi-dimensional approach transforms the simple binary cause-effect relationship into a rich, nuanced model that captures complex temporal and quantitative relationships.
2Reliability
If time delay information is added to causal relationships, then the accuracy of behavior prediction is improved, but the complexity of the causal network increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing time delay information, causal strength, and correlation data during the causal relationship evaluation phase. This allows the causal network to be built once with all necessary temporal and quantitative parameters, avoiding the need for complex real-time calculations during prediction, thus improving reliability without proportionally increasing operational complexity.
3Loss of information
If multiple parameters (strength, time, correlation) are included in causal relationship data, then the representation of complex causal relationships is improved, but the data processing complexity increases
Solution Approach 1:
The patent segments causal relationship information into distinct fields (direction, strength, time, correlation) that can be independently processed, stored, and retrieved. This segmentation reduces data processing complexity by allowing selective access to specific parameters based on analytical needs, rather than processing entire complex relationship structures.
Solution Approach 2:
Instead of deriving multiple parameters through complex post-processing of simple causal links, the patent inverts the approach by directly evaluating and storing all necessary parameters (strength, time delay, correlation) during the initial causal relationship assessment. This inversion eliminates the need for complex subsequent processing to extract these parameters.
Data Source
AI summary
A causal relationship is represented with a data structure including target identification information for identifying a target, index identification information of each of indexes to be used for quantitatively describing an event that occurs to the target, and causal relationship information for each pair of two indexes selected from the indexes. The causal relationship information includes direction information representing which one of the two indexes is a cause index and which one is an effect index, strength information representing a causal strength between the cause index and the effect index, time information representing a delay time for propagation of an influence of the cause index to the effect index, and correlation information representing a direction of change in the effect index with respect to an increase or decrease in the cause index.