Tail Event Prediction System with Automated Response
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Solution Overview
Problem
Current systems lack the capability to predict and prepare for rare or unpredictable 'tail events' that can significantly impact organizational operations, making it difficult to determine appropriate responses and implement actions in a timely manner.
Innovation Solution
A system that monitors multiple inputs, including third-party vendor data, social media, and tail event ledger data, to identify potential tail events, gather expert opinions through surveys, and automatically transmit responsive actions to relevant stakeholders, ensuring preparedness and timely response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and response planning for tail events is performed, then prediction accuracy and response appropriateness can be maintained, but response time and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-identifying potential tail events and pre-developing response plans before actual events occur. The automated monitoring system continuously analyzes data from multiple sources to detect early indicators of tail events, and response protocols are predetermined and stored in the system, enabling immediate activation when triggers are detected, thus resolving the contradiction between maintaining prediction accuracy and reducing response time.
Solution Approach 2:
The system replaces manual mechanical analysis processes with automated computational analysis. Machine learning algorithms and automated data processing systems substitute human analysts in monitoring data feeds, identifying patterns, and triggering responses. This substitution maintains or improves prediction accuracy through consistent automated analysis while dramatically reducing response time by eliminating manual intervention steps.
2Reliability
If comprehensive data from multiple sources is monitored to improve tail event detection, then prediction reliability is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the complex monitoring task into distinct modular components: data collection modules for different sources (social media, news feeds, internal systems), analysis modules for different types of indicators, and response modules for different event categories. Each module operates independently but contributes to the overall prediction reliability, managing system complexity through functional decomposition while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The system implements universality through a centralized automated monitoring platform that performs multiple functions: collecting data from diverse sources, analyzing various indicators, detecting different types of tail events, and triggering appropriate responses. This multi-functional system reduces overall complexity compared to having separate specialized systems for each function, while maintaining high prediction reliability through integrated analysis.
3Productivity
If automated response transmission is implemented, then response efficiency is improved, but potential errors in action execution increase
Solution Approach 1:
The system incorporates feedback mechanisms where the automated response transmission is monitored and verified. When responses are triggered, the system tracks their delivery and execution status, and feedback loops allow for correction of transmission errors or verification that appropriate actions were taken. This maintains high response efficiency through automation while ensuring action execution accuracy through continuous monitoring and correction capabilities.
Data Source
AI summary
Embodiments of the present invention provide a system for a managing entity to automatically provide alerts based on tail event analysis. The system may receive input data in real time from vendor data feeds, social media data feeds, and a tail event ledger. The system may then automatically populate surveys, transmit the surveys to responders, and receive survey results from the responders. The survey results may be transmitted to specialists that return a predicted tail event outcome. This predicted tail event outcome is then automatically transmitted to partners, or decision makers, that provide action steps for responding to the predicted tail event outcome. The system may then continuously monitor the input data, identify an indicator of an occurrence of the tail event, and then automatically transmit the action steps to appropriate parties.