1 research outputs found
Maintaining prediction quality under the condition of a growing knowledge space
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.Comment: 8 pages, 3 figure