Group Performance Attribution via Residual Data Segmentation
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Solution Overview
Problem
Conventional tools fail to accurately represent the performance of a group of entities, such as machines, by not indicating the sources of performance changes, leading to unreasonable management decisions and inability to distinguish between intrinsic group properties, agent skill, or luck.
Innovation Solution
A computer-implemented method and system that generates historical group residual performance data by analyzing historical group performance, factor exposure, and factor performance data, providing values that represent member selection performance and skill scoring to accurately attribute performance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tools are used to measure group performance, then the measurement process is simple, but the measurement precision is insufficient because they cannot indicate the sources of performance changes
Solution Approach 1:
The patent segments group performance measurement into distinct components: intrinsic group properties, agent skill, and luck. By decomposing the overall performance metric into these separable elements, the system achieves precise attribution of performance sources while maintaining a structured analytical framework that manages complexity through organized data collection and processing.
Solution Approach 2:
The patent introduces an intermediary analytical system that processes raw performance data and attributes it to specific sources. This intermediary layer (the performance attribution system) acts as a mediator between simple data collection and complex performance analysis, enabling precise measurement without requiring the end user to directly manage the underlying complexity of multiple performance components.
2Loss of information
If conventional tools measure only whether performance has increased or decreased, then the measurement process is straightforward, but information loss occurs because sources of performance changes are not indicated
Solution Approach 1:
The patent segments performance information into distinct categories (intrinsic properties, agent skill, luck) to prevent information loss. By dividing the performance attribution into these separate components, the system preserves complete information about performance sources while organizing data in a manageable structure that reduces processing complexity through systematic categorization.
Solution Approach 2:
The patent transforms the performance measurement parameter from a simple binary indicator (increased/decreased) into a multi-dimensional attribution framework. This parameter change enables comprehensive information capture about performance sources while the structured approach to measuring each component separately keeps data processing complexity manageable through standardized measurement protocols.
3Reliability
If agents take management actions based on conventional tools, then decision-making is simple, but reliability of decisions deteriorates because unreasonable actions are taken due to inadequate performance source indication
Solution Approach 1:
The patent implements a feedback mechanism that provides agents with detailed information about the sources of performance changes (intrinsic properties, agent skill, luck). This feedback enables agents to make more reliable decisions by understanding what drove performance outcomes, while the structured presentation of feedback information maintains ease of operation through clear, organized delivery of actionable insights.
Solution Approach 2:
The patent enables agents to independently assess their own performance by providing them with attribution data that highlights their specific contributions versus external factors. This self-service approach improves decision reliability by allowing agents to objectively evaluate their skill impact, while maintaining ease of operation through automated calculation and presentation of attribution results without requiring complex external analysis.
Data Source
AI summary
A method for quantifying performance of a group includes obtaining, for the group, historical group performance data, historical group factor exposure data for a plurality of predefined factors, and historical factor performance data for the plurality of predefined factors. The method includes generating historical group residual performance data for the group in accordance with the historical group performance data, the historical group factor exposure data for the plurality of predefined factors, and the historical factor performance data for the plurality of predefined factors. A respective entry in the historical group residual performance data represents member selection performance associated with the group. The method includes storing the historical group residual performance data for the group; and providing one or more values that represent the historical group residual performance data.