Maintaining prediction quality under the condition of a growing knowledge space

TL;DR

This paper introduces a mathematical model to analyze how the quality of a knowledge space evolves as it grows, considering error rates, propagation, and removal strategies, to maintain effective prediction capabilities.

Contribution

It presents a novel mathematical framework for understanding and managing the quality of knowledge spaces during growth, addressing error dynamics and countermeasures.

Findings

Quality of knowledge space can collapse if low-quality fragments are removed too slowly.

Error propagation significantly impacts knowledge space quality over time.

Countermeasures can mitigate quality degradation during growth.

Abstract

Intelligence can be understood as an agent's ability to predict its environment's dynamic by a level of precision which allows it to effectively foresee opportunities and threats. Under the assumption that such intelligence relies on a knowledge space any effective reasoning would benefit from a maximum portion of useful and a minimum portion of misleading knowledge fragments. It begs the question of how the quality of such knowledge space can be kept high as the amount of knowledge keeps growing. This article proposes a mathematical model to describe general principles of how quality of a growing knowledge space evolves depending on error rate, error propagation and countermeasures. There is also shown to which extend the quality of a knowledge space collapses as removal of low quality knowledge fragments occurs too slowly for a given knowledge space's growth rate.