Decision Simulator Using Knowledge Graphs
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Solution Overview
Problem
Current decision-making processes often rely on gut feeling rather than data analysis, leading to inefficiencies in reconciling independent decisions and adjusting to volatile conditions, with significant efforts spent on reconciling decisions and managing uncertainties outside of decision-makers' control.
Innovation Solution
The system employs scenario planning using probabilistic techniques and knowledge graphs to simulate conditions, generating probability distributions from historical data, allowing users to modify parameters and run simulations to predict outcomes, thereby facilitating data-driven decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional decision-making processes are used based on gut feeling, then simplicity and ease of operation are maintained, but decision accuracy and reliability deteriorate due to lack of data analysis
Solution Approach 1:
The system segments decision-making into distinct components: data collection module, knowledge graph construction module, scenario simulation module, and outcome analysis module. Each module handles specific tasks independently, improving decision accuracy through systematic data analysis while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary structure that connects historical data, current conditions, and potential outcomes. This intermediary enables systematic reasoning between disparate data points, enhancing decision reliability without requiring direct complex analysis of all raw data simultaneously.
2Productivity
If comprehensive data analysis and scenario planning are implemented, then decision reliability and productivity are improved, but computing resources and time requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing historical data and pre-construcing knowledge graphs before actual decision-making scenarios arise. This advance preparation stores structured relationships and patterns that can be quickly queried during time-critical decisions, improving productivity without proportionally increasing real-time computing resource consumption.
Solution Approach 2:
The patent creates simplified copies of complex scenarios through scenario planning simulations. Instead of analyzing every possible real-world variation, the system generates representative simulated scenarios that capture essential dynamics, enabling efficient analysis of multiple outcomes while consuming fewer computing resources than exhaustive real-world analysis would require.
3Adaptability or versatility
If independent decisions are made in isolation, then ease of operation is maintained, but adaptability deteriorates due to inability to reconcile decisions with volatile conditions
Solution Approach 1:
The knowledge graph serves as a universal framework that can accommodate multiple types of decisions and data sources simultaneously. It provides a common language and structure that integrates diverse independent decisions, enabling them to be reconciled and adjusted for volatile conditions without requiring separate complex systems for each decision type.
Data Source
AI summary
Example methods and systems are directed to simulating conditions for decision making. To help with decision making, simulated conditions may be used to generate probabilities of different events. Historical time-series data may be used to generate a probability distribution of values for simulated conditions. A user may be enabled to modify the probability distribution that was generated from the historical time-series data. The relationship between the value being simulated and other values may be represented by a knowledge graph. The knowledge graph may include nodes that represent arithmetic operations, input variables, external functions, database queries, and predictions. By running thousands of simulations with different values for input variables, as determined by the probability distributions for the input variables, a range of possible outcomes and their probabilities is generated. The simulation results are presented to a user to facilitate decision making.