Prediction Market Database Aggregating Confidence Ranges
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Solution Overview
Problem
Existing prediction market systems lack the ability to collect robust data and express varying levels of confidence effectively, leading to inaccurate forecasts and limited flexibility in adjusting underlying assumptions.
Innovation Solution
A relational database-based prediction market system that aggregates user predictions into a cumulative probability distribution, allowing users to express beliefs through point bets, confidence ranges, or distributions, and implements roll-up and roll-down functionality to update forecasts across dependent events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If prediction markets use simple betting systems, then ease of operation is improved, but measurement precision of confidence levels deteriorates
Solution Approach 1:
The patent segments the prediction market into multiple independent databases: a first database storing user predictions with confidence levels, a second database storing event outcomes, and a third database storing weighted predictions. This segmentation allows the system to maintain simple user interaction while precisely measuring confidence through structured data separation.
Solution Approach 2:
The patent introduces a weighting mechanism as an intermediary between user input and forecast generation. The weighting system translates varying confidence levels into quantitative weights that are applied to predictions, enabling precise measurement of confidence without complicating the user interface.
2Measurement precision
If prediction markets collect detailed confidence data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complexity of confidence measurement and weighting calculations from the user-facing prediction market interface and places it in the backend database system. Users simply input predictions and confidence levels through a simple interface, while the complex weighting and aggregation operations are performed automatically by the system behind the scenes.
Solution Approach 2:
The system performs automatic weighting and aggregation of predictions without requiring manual intervention. The databases automatically compute weighted predictions based on confidence levels and generate forecasts, making the complex processing transparent and self-service oriented.
3Ease of operation
If prediction markets use fixed outcome structures, then ease of operation is improved, but adaptability to changing assumptions deteriorates
Solution Approach 1:
The patent implements a dynamic prediction market system where weights and confidence levels can be adjusted as new information becomes available. The system allows re-weighting of existing predictions and incorporation of new outcomes, enabling flexible adaptation to changing assumptions while maintaining the basic ease of use through consistent database operations.
4Quantity of substance
If prediction markets aggregate multiple predictions, then information aggregation is improved, but loss of information increases
Solution Approach 1:
The patent preserves local quality of individual predictions by maintaining separate stored predictions with their specific confidence weights in the database. When aggregating forecasts, the system selectively applies weights based on local characteristics of each prediction, preserving important information from individual contributors while still benefiting from aggregation.
Data Source
AI summary
This disclosure provides a database, methods and associated software to implement and manage a prediction market. By collecting individual predictions as a set of predicted outcomes, such as a range of outcomes, the principles presented herein permit aggregation of overlapping predictions to generate a cumulative probability distribution, effectively using “crowd wisdom” to build a probability model of an event. Through the use of “roll-up” and “roll-down” techniques, this disclosure provides a way of applying new information not only to an event being predicted, but also to the outcomes of other, related events. Conversion to a common probability distribution format may be used to simplify and accelerate mathematical operations, easing the burden in quickly calculating and updating a cumulative probability model for events in each affected dimension in the predicted market. These teachings may be applied to a wide variety of applications, including gaming, blog and opinion sites, spreadsheet programs, and date, logistics, accounting and other forecasting tools.