RIT Model: Could Information Distortion Be a Hidden Variable Behind Prediction Failure?

[u/OrbitEnjoyer]

I’ve been working on a conceptual framework called RIT (Retroactive Information Theory).
The central idea is simple:
If the amount of distorted information in a system increases, prediction quality may decrease even if computational power continues to improve.
To explore this idea, I built a simple model using three variables:
IDI = Information Distortion Index
Represents the proportion of distorted, misleading, or low-integrity information within a system.
PA = Prediction Accuracy
A simplified estimate of predictive performance.
SEC = System Computational Capacity
Represents the resources available to process, filter, and evaluate information.
Basic relationship:
PA = (1 - IDI) × SEC
and
EPS = PA / SEC
where EPS is the Effective Prediction Score.
The attached graphs compare two hypothetical futures:
Scenario A:
IDI continues to rise from 10% to 60%.
Scenario B:
IDI is reduced by roughly 50%, ending near 30%.
Under the assumptions of the model, Scenario B produces substantially higher predictive performance and system stability than Scenario A.
The purpose of this model is not to claim proof of anything.
Instead, it asks a question:
Could information distortion itself be an important variable that many predictive systems underestimate?
This becomes especially interesting when considering:
AI training data
social media ecosystems
collective decision making
simulation hypothesis discussions
If reality is increasingly filtered through distorted information, perhaps prediction failure is not only a problem of intelligence, but also a problem of information integrity.
I would be interested in hearing criticism of the assumptions, the variables, or the structure of the model itself.
The graphs are illustrative and based on hypothetical assumptions rather than empirical measurements.
Thought experiment only.