Method — Epistemic Uncertainty
Definition, scope boundary, and structural model.
Definition
Epistemic uncertainty describes uncertainty arising from limitations in available knowledge about a represented system, model, parameter, hypothesis, relationship, prediction, or state.
The model distinguishes knowledge-dependent uncertainty from variability treated as inherent in the represented process and separates the knowledge state, the unresolved object, and the representation used to express the uncertainty.
Model Classification
The epistemic uncertainty model is structured as a descriptive and analytical reference model.
It provides a framework for examining knowledge limitation, uncertainty objects, uncertainty representation, and epistemic–aleatoric boundaries without defining implementation-specific estimation algorithms, operational procedures, or decision rules.
Scope Boundary
Included
Excluded
Structural Model
Knowledge State
The available knowledge concerning the represented system, model, parameter, hypothesis, relationship, prediction, or state.
Knowledge Limitation
The incomplete, limited, or unresolved condition within the available knowledge.
Uncertainty Object
The represented object about which available knowledge remains incomplete or unresolved.
Uncertainty Representation
The explicit form used to represent uncertainty associated with the current knowledge state.
Structural Components
Knowledge State
The bounded set of available knowledge concerning the represented object.
Knowledge Limitation
The incompleteness or unresolved condition responsible for the epistemic uncertainty.
Uncertainty Object
The state, parameter, model, hypothesis, relationship, prediction, or condition about which knowledge remains incomplete.
Uncertainty Representation
The explicit representation of uncertainty associated with the current knowledge state.
Transferability
The epistemic uncertainty model is not limited to a specific domain or technology.
It can be applied across scientific, engineering, computational, analytical, and organizational domains in which limitations of available knowledge are represented explicitly.
The model remains consistent by focusing on knowledge limitation, the object of uncertainty, uncertainty representation, and the epistemic–aleatoric boundary rather than implementation-specific estimation mechanisms.