Predictive Fit Model System for Collaborative Decision Making
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Collaborative decision-making processes involving multiple users and criteria are data-intensive and computationally resource-heavy, requiring extensive user surveys that are time-consuming and burdensome, especially as the number of users and criteria increase.
Innovation Solution
A self-learning predictive fit model system that receives user responses, associates them with user characteristics, and creates predictive models to reduce the need for surveys by determining confidence scores and providing targeted surveys only to users with low confidence scores, thereby minimizing computational resources and user burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive user surveys are conducted for every feature assessment, then decision accuracy is improved, but computational resources and time consumption increase drastically
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing user preference profiles, response patterns, and criteria weightings before actual decision-making occurs. Historical survey data is processed in advance to create predictive models that can quickly estimate user responses without conducting full surveys each time, thus reducing time consumption while maintaining decision accuracy.
Solution Approach 2:
The system creates copies of user response patterns and preferences through predictive models. Instead of collecting actual survey responses from all users for every feature assessment, the system generates synthetic response data based on learned user profiles and historical behavior, significantly reducing time and computational resources while preserving measurement precision.
2Measurement precision
If comprehensive user surveys are conducted for every feature assessment, then decision accuracy is improved, but computational resources increase drastically
Solution Approach 1:
The system creates copies of user response patterns and preferences through predictive models. Instead of collecting actual survey responses from all users for every feature assessment, the system generates synthetic response data based on learned user profiles and historical behavior, significantly reducing time and computational resources while preserving measurement precision.
Solution Approach 2:
The system develops universal predictive models that can be applied across multiple feature assessments and decision-making scenarios. By creating generalizable user preference profiles and response patterns that work across different features and contexts, the system eliminates the need to conduct separate comprehensive surveys for each assessment, reducing computational resource requirements while maintaining accuracy.
3Loss of information
If every user responds to surveys for every feature assessment, then data completeness is improved, but user burden and complexity increase
Solution Approach 1:
The system implements self-service by automatically generating predictive responses using learned user profiles and historical data. Instead of requiring active user participation in every survey, the system autonomously estimates user responses based on previously collected information, reducing user burden while maintaining data completeness through the use of robust predictive algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms where actual survey responses are continuously compared with predictive model outputs. This feedback loop allows the system to refine and update user profiles and predictive algorithms over time, ensuring that predicted responses remain accurate and complete without requiring exhaustive user input for each assessment.
Data Source
AI summary
Disclosed is systems, methods, and computer program products that provide for a technique for reducing computing resources, storage space needs, and network bandwidth associated with collaborative decision making. More particularly, this disclosure relates to a system for performing automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user, and using the results to reduce the amount of computing and operational resources needed to operate a collaborative decision engine.