Workforce Elasticity Indexing via Machine Learning Prediction
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Solution Overview
Problem
It is challenging to predict how local area economies will respond to a sudden increase in available labor following workforce reduction events, making it difficult for displaced workers to find new jobs, as some regions are more elastic than others in absorbing new workers.
Innovation Solution
A computer-implemented method using machine learning predictive modeling aggregates employment data to construct a model that calculates workforce elasticity indices for geographic regions, ranking them based on the ease or difficulty of finding new jobs, anticipating the ability of local economies to absorb displaced workers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning predictive modeling is used to aggregate and analyze employment data, then the accuracy of predicting workforce elasticity is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the analysis by geographic region, creating separate predictive models for different areas. This allows the complex machine learning model to be applied systematically across multiple regions without overwhelming complexity, as each region can be analyzed independently with standardized procedures.
Solution Approach 2:
The patent introduces an intermediary database layer that stores predicted employment values and workforce elasticity indices. This database acts as a mediator between the complex machine learning modeling process and the final output, simplifying the system architecture by separating data storage, prediction generation, and result retrieval functions.
2Reliability
If iterative machine learning analysis is performed to construct predictive models, then the reliability of employment predictions is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and aggregating employment data before the iterative machine learning analysis. Historical employment values and workforce elasticity indices are calculated in advance and stored in the database, allowing the iterative modeling to focus only on refining predictions rather than performing all calculations from scratch, thus reducing overall analysis time.
Solution Approach 2:
The iterative machine learning process incorporates feedback mechanisms where predicted employment values are compared against actual observed values to refine the model. This feedback loop improves prediction reliability over time while the system efficiently manages computational resources to minimize analysis time through optimized iteration cycles.
3Adaptability or versatility
If workforce elasticity indices are calculated for multiple geographic regions, then the comprehensiveness of labor market analysis is improved, but the computational resources required increase
Solution Approach 1:
The system divides the analysis into separate geographic regions, allowing computational resources to be allocated efficiently. Each region can be processed independently using the same standardized machine learning model, enabling comprehensive coverage of multiple regions without proportionally increasing computational resources for each additional region.
Solution Approach 2:
The patent creates a universal predictive model that can be applied across multiple geographic regions using the same core machine learning algorithms and data processing procedures. This multi-functional approach allows the system to analyze different regions with consistent methodology, reducing the need for region-specific computational overhead and enabling comprehensive labor market analysis across diverse geographic areas.
Data Source
AI summary
A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with employment; performs iterative analysis on the data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted employment values for predefined geographic regions; converts the predicted employment values in the database into percentages of observed employment values for the predefined geographic regions over a specified time period to create indices of workforce elasticity for each geographic region; and rank orders the predefined geographic regions according to their indices of workforce elasticity.