This paper introduces Representation Governance as the institutional and procedural layer that determines how machine-readable economic representations are standardized, verified, maintained, audited, contested, and made admissible in computational allocation systems. In AI-mediated markets, governance increasingly shifts upstream from visible market behavior to the representation infrastructures that determine whether an entity can be discovered, assessed, trusted, compared, and admitted. The paper distinguishes Representation Governance from Representation Sovereignty, Representation Capital, and Computational Creditworthiness, and introduces a governance stack spanning data, schema, verification, provenance, access, admissibility, and accountability layers. The analysis examines standards and interoperability, verification and provenance, access and agent-readability, update rights and correction mechanisms, dispute resolution, governance models, and applications to property and hospitality markets. It argues that without adequate Representation Governance, markets may experience silent exclusion, platform dependency, and allocative outcomes determined by opaque representation infrastructures rather than competitive merit. This is Volume IV of the Representation Economy Research Program.
Marco Patrone (Sat,) studied this question.