This working paper develops a theoretical framework for computational intermediation in financial market economics. It examines how firm valuation, capital allocation, market efficiency, rating systems, competitive advantage, and investment screening may be affected when discovery, comparison, ranking, recommendation, trust formation, and selection are increasingly performed by computational systems and AI-mediated interfaces. The paper proposes computational intermediation as an extension of, not a replacement for, established economic and financial theory. It introduces candidate variables and theoretical constructs including Representation Capital, Inferential Accessibility, Inference Burden, AI Allocability, Computational Trust, AI Allocability Discount, Inference Burden Score, Computational Risk Premium, Computational Valuation Premium, Computational Allocation Error, and Representation-Adjusted Firm Value. The paper also proposes investor-relevant measurement frameworks and candidate indicators for computational allocation, including Representation Capital Score, Inference Burden Score, AI Allocability Score, Computational Intermediation Exposure, Computational Acquisition Risk, AI Visibility Delta, Computational Valuation Premium, Computational Risk Premium, and Representation-Adjusted Firm Value. All constructs are theoretical hypotheses requiring empirical validation. The paper does not claim that existing valuation models are invalid, but argues that they may require representation-adjusted extensions when computational intermediation becomes economically material.
Marco Patrone (Sat,) studied this question.