Representation Capital Measurement Theory
Formalizing How Representation Capital Can Be Quantified
Abstract
This paper introduces a formal measurement framework for Representation Capital—the accumulated stock of machine-readable qualities that may increase computational admissibility probability in AI-mediated markets. We develop primitive-based measurement approaches, composite indices, and admissibility functions that can quantify representation quality across different contexts and use cases.
The measurement framework is entirely theoretical. All proposed metrics and indices require empirical validation before being applied to real-world allocation systems.
Epistemic Status: Theoretical / Non-Empirical
This paper presents a theoretical measurement framework. All proposed metrics and indices are speculative and require empirical validation.
Measurement Framework
Theoretical approaches to quantifying Representation Capital
Three Measurement Approaches
Primitive-Based Measurement
Direct measurement of six primitives: Completeness, Accuracy, Verifiability, Freshness, Portability, and Actionability.
Composite Indices
Aggregated scores combining multiple primitives with weighted functions.
Admissibility Functions
Context-specific functions mapping representation quality to inclusion probability.
Citation
How to cite this research publication
APA Style
Patrone, M. (2026). Representation Capital Measurement Theory: Formalizing How Representation Capital Can Be Quantified. HomeSelf Research. DOI: 10.5281/zenodo.20824904