Early Warning Event Prediction Using Indirect Contagion Effects
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Solution Overview
Problem
Existing systems are unable to effectively predict future events and their contagion effects due to the complexity of variables and lack of coordination in information sources, often failing to provide timely warnings.
Innovation Solution
An early warning and event monitoring system that utilizes real-time risk intelligence and complex accumulative risks to predict future events by deriving interrelationships, generating pairwise event relationship metrics, and predicting direct and indirect contagion effects using AI and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems attempt to predict future events using traditional methods, then the system complexity remains manageable, but the prediction accuracy and timeliness deteriorate due to inability to handle massive variables and uncoordinated information sources
Solution Approach 1:
The system segments the complex prediction problem into multiple manageable components: event detection module, interrelationship derivation module, metric generation module, and contagion effect prediction module. Each module processes specific aspects of the data independently before integrating results, allowing the system to handle massive variables without overwhelming complexity.
Solution Approach 2:
The system introduces intermediary structures including a standardized event schema for uniform data representation, an interrelationship database for storing event connections, and a metrics calculation layer that transforms raw data into actionable predictions. These intermediaries coordinate information from uncoordinated sources and reduce overall system complexity.
2Adaptability or versatility
If the system processes more variables and information sources to improve prediction coverage, then the comprehensiveness of event prediction improves, but the processing time and responsiveness deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing event data in standardized formats, pre-calculating interrelationships between events, and maintaining updated event graphs. This preparation allows the system to quickly generate predictions without processing all data from scratch, reducing response time while maintaining comprehensive coverage.
Solution Approach 2:
The system dynamically adjusts its processing based on the complexity and urgency of each prediction task. For high-urgency predictions, it uses pre-computed metrics and simplified models; for comprehensive analysis, it processes all variables. This dynamic adaptation balances processing time with event coverage requirements.
3Reliability
If the system analyzes detailed interrelationships and contagion effects, then the prediction depth and actionable insights improve, but the computational resources and processing complexity increase
Solution Approach 1:
The system applies local quality by analyzing interrelationships and contagion effects only for specific event pairs and regions where predictions are most needed, rather than processing all possible combinations uniformly. It prioritizes analysis based on event importance, spatial proximity, and temporal relevance, reducing computational resources while maintaining high reliability for critical predictions.
Data Source
AI summary
An early warning and event monitoring computer device for predicting events is provided. The computer device programmed to a) receive a plurality of events of interest; b) derive interrelationships for the plurality of events of interest; c) generate pairwise event relationship metrics for the plurality of events of interest based upon the derived interrelationships; d) predict one or more future events based upon the pairwise event relationship metrics; e) predict a direct, first order contagion effect based upon an interrelationship between two events of the plurality of events and the one or more future events; f) predicting indirect, second and third order contagion effects based upon the interrelationships between multiple plurality of events; and g) extracting one or more variables from the two or more events based upon the first, second and third order contagion effect as strategies to change contagion effects.