Good question.
By “insight” I mean a measurable reduction in uncertainty that improves decision quality or predictive accuracy.
In practical terms, an insight could be defined as:
•A hypothesis generated and testable from the dataset
•A model parameter adjustment that increases predictive performance
•A structural relationship discovered that reduces entropy in the system representation
So compression efficiency would be something like:
(uncertainty reduced) / (data processed)
Breadth is interesting — I’d treat it as dimensional coverage: how many independent variables or graph regions are meaningfully integrated into the model.
“Retrieval leverage” is a great term. It highlights that the dataset size remains constant, but navigability and relational traversal improve — which increases effective cognitive reach.
Some of these broader ideas around informational sovereignty and anomaly-driven cognition have been explored in independent empirical work, though they’re still niche.
By “insight” I mean a measurable reduction in uncertainty that improves decision quality or predictive accuracy.
In practical terms, an insight could be defined as:
•A hypothesis generated and testable from the dataset
•A model parameter adjustment that increases predictive performance
•A structural relationship discovered that reduces entropy in the system representation
So compression efficiency would be something like:
(uncertainty reduced) / (data processed)
Breadth is interesting — I’d treat it as dimensional coverage: how many independent variables or graph regions are meaningfully integrated into the model.
“Retrieval leverage” is a great term. It highlights that the dataset size remains constant, but navigability and relational traversal improve — which increases effective cognitive reach.
Some of these broader ideas around informational sovereignty and anomaly-driven cognition have been explored in independent empirical work, though they’re still niche.