The Representation Economy
Computational Market Access as Allocative Infrastructure in AI-Mediated Markets
Abstract
This paper introduces the Representation Economy framework, examining how AI-mediated allocation systems may create a new economic constraint class: computational market access. We argue that the structural transition from visibility-based markets to admissibility-based markets represents a fundamental shift in how economic participation is determined.
When AI systems construct consideration sets before ordering, exclusion precedes ranking. Under bounded inference (K < n), not all accessible options can be considered. This creates inferential scarcity—a new economic constraint where reasoning capacity binds allocation. We introduce computational admissibility as the technical eligibility for allocative processing and examine how representation quality may become allocative infrastructure.
Epistemic Status: Theoretical / Non-Empirical
This paper presents a theoretical framework examining structural constraints in AI-mediated allocation systems. All claims require empirical validation through observational study and experimental measurement.
Core Concepts
Foundational concepts of the Representation Economy framework
Inferential Scarcity
A new economic constraint class where reasoning capacity binds allocation. When inference is bounded, not all accessible options can be considered.
Computational Admissibility
Technical eligibility for allocative processing. An artifact must meet representation cost thresholds to enter consideration sets.
K < n Constraint
K < nThe consideration set size (K) is necessarily smaller than the accessible set size (n), creating permanent exclusion pressure.
Silent Exclusion
Exclusion from consideration without explicit decision or visibility loss. The artifact is accessible but never considered.
Consideration Set Construction
The AI-mediated process of selecting a subset of accessible artifacts for deeper evaluation before ranking or comparison.
Representation as Infrastructure
Machine-readable representation becomes allocative infrastructure—prerequisite for economic participation in AI-mediated markets.
The Core Insight
Ranking presupposes inclusion. Before any AI system can rank options, it must first construct a consideration set. When consideration set capacity (K) is smaller than the accessible set (n), exclusion precedes competition. This creates a new structural condition where computational admissibility, not competitive advantage, determines economic participation.
Paper Structure
Five-part framework examining the structural transition
From Visibility to Admissibility
Inferential Scarcity
Computational Admissibility
Representation as Infrastructure
Structural Consequences
Key Insights
Structural implications of the Representation Economy framework
Exclusion Precedes Ranking
Before any AI system can rank options, it must first construct a consideration set. When K < n, exclusion is structural and unavoidable.
Admissibility Determines Participation
In AI-mediated markets, computational admissibility may determine whether economic participation occurs at all. Without admissibility, competitive advantages cannot be exercised.
Representation Becomes Infrastructure
When AI-mediated allocation becomes infrastructure-dependent, machine-readable representation becomes prerequisite for economic participation.
Inferential Scarcity Creates New Economics
When reasoning capacity binds allocation, new economic dynamics emerge. Representation quality may become allocative infrastructure.
Research Program Context
How this paper establishes the foundation for the research program
Program Development Flow
The Representation Economy (this paper)
├── Institutional Layer
│ ├── Computational Market Access (No. 2)
│ └── AI-Mediated Markets (No. 11)
├── Mathematical Layer
│ ├── Computational Market Economics (No. 3)
│ └── Network-Dependent Allocation (No. 4)
└── Application Layer
├── Computational Pricing Theory (No. 6)
├── Representation Capital (No. 7, Volume I)
├── Representation Sovereignty (No. 5, Volume II)
└── Computational Creditworthiness (No. 8, Volume III)Caveats and Scope Limitations
What this paper is NOT about
Important: Scope Clarification
This paper presents a theoretical framework. No empirical claims or predictive guarantees are made.
This is NOT SEO theory
We do not discuss search engine optimization or content marketing. Computational admissibility is about AI-mediated consideration sets, not search rankings.
This is NOT platform optimization
We do not discuss platform-specific optimization for Amazon, Uber, or Airbnb. The framework operates at infrastructure level.
No empirical claims
All claims about allocative consequences are theoretical. Empirical validation is required before any policy or business implications.
Not predictive
We do not claim that admissibility-based markets will definitively emerge. We examine potential structural consequences.
Not investment advice
This is a research paper examining structural economic constraints, not investment guidance or business strategy.
Citation
How to cite this research publication
APA Style
Patrone, M. (2026). The Representation Economy: Computational Market Access as Allocative Infrastructure in AI-Mediated Markets. HomeSelf Research. DOI: 10.5281/zenodo.20692182BibTeX
@workingpaper{patrone2026representation_economy,
title={The Representation Economy: Computational Market Access as Allocative Infrastructure in AI-Mediated Markets},
author={Patrone, Marco},
year={2026},
number={1},
institution={HomeSelf Research},
doi={10.5281/zenodo.20692182},
url={https://homeself.ai/research/representation-economy/umbrella}
}Download Full Paper
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