Scaled Voting Decision System with Centralized Officiating
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Solution Overview
Problem
Conventional computer systems fail to adequately account for uncertainty and previous decisions when making choices, leading to errors and frustration, as they often require yes/no votes and do not allow for scaled voting or individual participation in decision-making processes.
Innovation Solution
A computer-based decision-making system that allows components to cast tunable votes and evaluates the success of previous decisions to modify future ones, using a centralized officiating member to manage the voting process and track data items, enabling proposals to be acted upon and adjusted based on success measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems use yes/no voting for decisions, then the decision process is simple and quick, but the system cannot adequately account for uncertainty and individual component participation
Solution Approach 1:
The system transitions from static yes/no voting to dynamic scaled voting where components can adjust their vote intensity continuously. Each component's vote is multiplied by a confidence factor that reflects its certainty about the decision, allowing the system to weigh uncertain decisions differently from certain ones while maintaining a relatively simple voting mechanism.
Solution Approach 2:
The voting mechanism changes from binary (yes/no) to continuous scaled values. Components provide votes as floating-point numbers that can be scaled by confidence factors, transforming the decision-making parameters from discrete to continuous and enabling nuanced expression of uncertainty without fundamentally complicating the voting structure.
2Adaptability or versatility
If conventional systems treat decisions as independent events, then each decision is made quickly without historical context, but previous decisions and their outcomes are not learned from
Solution Approach 1:
The system implements feedback loops where outcomes of previous decisions are measured and fed back into the decision-making process. Success measurements from past decisions modify future voting behaviors, allowing components to learn from experience while maintaining efficient decision-making through automated feedback integration rather than manual review.
Solution Approach 2:
The system pre-calculates and stores success measurements from previous decisions, making this historical data immediately available for future voting without requiring time-consuming analysis during new decision-making events. This preliminary preparation of outcome data enables rapid adaptation to lessons learned.
3Ease of operation
If representative systems give plenary power to a small number of decision-makers, then the decision process is efficient, but individual participation and scaled voting intensity are lost
Solution Approach 1:
The system segments decision-making power from centralized representatives to individual components, each capable of independent scaled voting. Instead of one representative making decisions for many components, each component maintains its own voting authority with adjustable intensity, distributing governance complexity across many simple voting units rather than one complex decision-maker.
Data Source
AI summary
A method for decision-making by an electronic system comprising a plurality of components is provided herein. The components of the system are given tunable voting authority v=[Vmin . . . Vmax], typically [−1 . . . 1]. A singleton officiating member manages the notification and voting process. A current representation of the entire system is stored in the model. The model is a state storage system that tracks the existence and condition of a group of tracked data items, called Entities. The model is managed by the singleton officiating member but may be queried by any component.