Representation Sovereignty is introduced as a distinct form of sovereignty emerging within AI-mediated allocation systems. Whereas traditional sovereignty concerns territorial authority, legal enforcement, and institutional control, Representation Sovereignty concerns control over the infrastructures, protocols, and standards that determine machine-readable admissibility and allocative participation. The paper develops a theoretical framework explaining how visibility, accessibility, and machine-readability become prerequisites for participation in computational markets. It argues that entities may retain formal legal sovereignty while lacking effective representation within AI-mediated decision environments, producing a sovereignty gap between institutional authority and allocative participation. Building on the Representation Economy research program, the manuscript distinguishes Representation Sovereignty from Representation Capital, Representation Investment, and Representation Yield, positioning it as a governance-layer concept rather than an allocative asset. The analysis explores implications for discoverability, admissibility, machine-readable trust verification, protocol participation, and AI-mediated market access. The paper concludes by outlining the potential emergence of representation infrastructures as strategic governance assets in computational economies and identifies future research directions concerning representation concentration, allocative dependency, and antitrust implications in agent-mediated markets.
Marco Patrone (Fri,) studied this question.