Representation Governance: Institutional Control, Protocol Coordination, and Allocative Authority in AI-Mediated Markets

Representation Governance introduces a theoretical framework for understanding how governance authority shifts toward representation infrastructures in AI-mediated markets. As computational systems increasingly determine allocative access before human consideration occurs, governance expands beyond institutions and algorithms to the protocols, standards, verification systems, and coordination mechanisms that define computational admissibility. The paper distinguishes three complementary forms of governance authority: Institutional Authority, Representation Authority, and Allocative Authority. It develops a governance framework for machine-readable representations, analyzes governance failure modes including representation exclusion, capture, lock-in, and allocative manipulation, and proposes governance principles such as interoperability, verifiability, contestability, portability, auditability, and machine-readable accountability. The framework clarifies the boundary between representation governance and autonomous AI systems: governance concerns the quality, integrity, provenance, and admissibility of representations, while external AI systems remain autonomous in inference, ranking, recommendation, and decision-making. The paper positions Representation Governance as the institutional layer of the Representation Economy Research Program and outlines future research on representation infrastructure, allocative sovereignty, and inferential monopoly.