Many prediction systems fail to account for how probabilities should change over time when an anticipated event hasn't occurred. This leads to less accurate forecasts, especially for time-sensitive predictions like natural disasters or political events. For example, if an earthquake prediction made in 2022 hasn't materialized by 2023, the original probability estimate should logically decrease.

Core Concept and Methodology

The suggested approach involves creating mathematical models that automatically adjust prediction probabilities based on elapsed time without the event occurring. Different types of predictions would use different decay patterns:

• Uniform probability distributions might use linear decay
• Clustered events might follow exponential decay patterns

Bayesian principles would form the foundation, where the absence of an event serves as information to update the probability. The framework could provide both standardized decay models and options for custom functions where needed.

Practical Applications and Implementation

Potential users range from prediction markets and insurance companies to research institutions and policy makers. A phased implementation could start with:

1. Developing and testing core mathematical models
2. Creating open-source tools and APIs
3. Partnering with prediction platforms for integration

A minimal version might be a simple web tool that takes a prediction's initial probability and timeframe, then outputs the current adjusted probability based on today's date.

Distinguishing Features

This approach differs from existing prediction platforms by systematically addressing the time-value of predictions rather than treating all forecasts as equally valid regardless of when they were made. While some platforms track prediction accuracy over time, they typically don't adjust the probabilities themselves as time passes without the event occurring.

The framework could improve decision-making across various fields by providing more accurate, time-sensitive probability estimates, especially for predictions with clear temporal components.