Accuracy

Representation Capital Measurement Theory

Representation Capital Measurement Theory supplies the measurement layer of the Representation Economy research program. This paper formalizes how Representation Capital—the accumulated stock of machine-readable qualities that increases computational admissibility probability—can be measured through observable primitives, composite indices, admissibility functions, Representation Yield, Allocation Influence, and threshold-based exclusion. The framework introduces six measurable primitives (Completeness, Accuracy, Verifiability, Freshness, Portability, Actionability), formalizes additive and multiplicative measurement approaches, defines computational admissibility functions with threshold-based exclusion, derives Representation Yield as the allocative return on representation investment, specifies Allocation Influence as the probability shift from representation changes, and provides testable predictions linking measurement outcomes to selection probability.