AI Decision Support With Utility Functions for Reliable Candidate Matching
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Solution Overview
Problem
Existing decision support systems face challenges with information availability, reliability, and relevance, leading to suboptimal decisions due to high computational resources, storage requirements, and inefficient processing of information, particularly in multilateral contexts.
Innovation Solution
A system and methodology that enables entities to act as both parties and counterparties, utilizing forced-choice surveys and conjoint analysis to derive utility functions, minimizing computational cycles and storage needs while optimizing mutual utility through symmetrical evaluation of preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If more information is shared with the market regarding specific interests, then the pool of decision candidates increases, but the number of impostor counterparties increases and information reliability decreases
Solution Approach 1:
The patent introduces an intermediary evaluation system that acts as a mediator between information sharing and decision-making. This system evaluates counterparties using standardized criteria and utility functions, filtering out impostors while maintaining information reliability even as the pool of candidates expands.
Solution Approach 2:
The patent transforms qualitative information reliability into quantifiable parameters through utility functions and evaluation metrics. By changing the parameter representation of information quality, the system can objectively assess and compare counterparties, maintaining reliability standards while expanding the candidate pool.
2Productivity
If more information is shared by market participants, then more decisions can be made, but the computational resources and time required to process and verify information increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-establishing evaluation frameworks, utility functions, and decision criteria before the actual decision-making process. This allows information to be processed and evaluated more efficiently when decisions need to be made, reducing the time and computational resources required during the decision-critical moment.
Solution Approach 2:
The patent converts complex information processing into parameter-based evaluations using utility functions. By changing the representation of information from raw data to standardized parameters, the system reduces computational complexity and processing time while maintaining decision quality.
3Reliability
If traditional decision support systems process all available information, then comprehensive decisions can be made, but the computational cycles and storage requirements become prohibitively high
Solution Approach 1:
The patent extracts only the essential and relevant information needed for decision-making using utility functions and evaluation criteria. By taking out only the critical parameters and ignoring redundant information, the system maintains decision comprehensiveness while significantly reducing computational resource consumption and storage requirements.
4Reliability
If participants provide extensive information to avoid Type I errors, then the downside of decisions is limited, but the pool of decision candidates becomes heavily restricted
Solution Approach 1:
The patent changes the parameter requirements for decision safety from extensive qualitative information to specific quantifiable thresholds defined by utility functions. This allows the system to maintain decision safety through objective parameter thresholds rather than restricting the candidate pool through subjective information requirements.
Data Source
AI summary
A system and methodology which can effectively provide decision makers with a better means of making decisions in a way that greatly improves the availability, reliability, and relevance of the information which they provide and use to make decisions. The system and methodology facilitates maximizing mutual utility in the context of a mutual decision between multiple users and groups of users identified generally as Parties and Counterparties and performs user specified actions based on meeting mutual threshold parameters. The system provides significant technical advantages over the prior art in that it uses helps Parties and Counterparties identify optimal arrangements and configurations with less errors, fewer computational cycles, less storage medium, and a smaller amount of time than would be possible using prior art systems.