Computational Creditworthiness is introduced as a theoretical framework for understanding how economic subjects are assessed for trust, admissibility, and allocative access in AI-mediated markets. The paper argues that, in markets increasingly mediated by autonomous systems, creditworthiness is no longer limited to financial solvency, repayment capacity, or institutional trust. A new dimension of creditworthiness emerges: the computational assessability of an economic subject. The framework defines Computational Creditworthiness as the degree to which an entity — whether an individual, firm, asset, property, or market participant — can be reliably evaluated, trusted, compared, and admitted by computational allocation systems on the basis of its machine-readable representation. The paper connects this concept to Representation Capital and Representation Sovereignty, and applies it to property and hospitality markets, where hotels and properties may become computationally creditworthy or uncreditworthy depending on the quality, verifiability, and machine-readability of their representations. This is Volume III of the Representation Economy Research Program.
Marco Patrone (Sat,) studied this question.