Polynomial Discounting Peer Review System
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Solution Overview
Problem
Peer review systems face challenges in discouraging bad faith endorsements and virtue signaling, as reviewers may incentivize their opinions based on who is watching, leading to biased ratings.
Innovation Solution
Implementing a computerized peer review system with enforced anonymity and polynomial discounting, where individual endorsements are discounted, reducing the incentive for virtue signaling and encouraging peers to use their endorsements based on genuine preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reviewers' identities are disclosed in peer review systems, then accountability and traceability are improved, but bad faith endorsements and virtue signaling increase
Solution Approach 1:
The patent extracts the harmful element (reviewer identity) from the review process by implementing enforced anonymity. Reviewers can submit endorsements without disclosing their identities, which eliminates the incentive for virtue signaling and bad faith endorsements while preserving the integrity of the review content itself
Solution Approach 2:
The system introduces an intermediary mechanism (polynomial discounting algorithm) that mediates between individual endorsements and final rankings. This algorithmic intermediary processes reviews in a way that discounts excessive or coordinated endorsements, preventing manipulation while maintaining reviewer anonymity
2Power
If individual endorsements are given full weight, then reviewer impact is maximized, but virtue signaling and biased ratings increase
Solution Approach 1:
The patent applies polynomial discounting that dynamically changes the weight parameter of individual endorsements based on the number of reviews. As a reviewer's endorsement count increases, the marginal impact of each additional endorsement decreases according to a polynomial function, preventing any single reviewer from dominating rankings through excessive virtue signaling
Solution Approach 2:
The system implements dynamic weighting where the impact of each endorsement is not fixed but adjusts based on the reviewer's overall review history and the distribution of endorsements. This dynamic approach ensures that early reviews have more impact while preventing later coordinated manipulation
3Reliability
If polynomial discounting is applied to endorsements, then bad faith endorsements are reduced, but system complexity increases
Solution Approach 1:
The patent replaces complex human judgment mechanisms with a mathematical polynomial discounting algorithm. This algorithmic substitution automatically calculates weighted endorsements based on simple input data (review counts and ratings), achieving sophisticated manipulation prevention through straightforward mathematical operations rather than complex procedural safeguards
Data Source
AI summary
Technology for voting, or endorsing with votes, a set of subjects under review, such as a group of human individual peers or a set of products. Each voter in this system is provided with an amount of voting credits that may be allocated among and between at least some of the subjects under review. In some embodiments a discounting scheme is applied to the voting credit allocations so that multiple credits allocated to a single subject will typically count for fewer net “votes” for the subject as the number of credits allocated to that single subject increases. In some embodiments, the discounting scheme is polynomial voting.