Knowledge Architecture:ConceptsObservationsEvidence
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Representation Layer

The foundational layer that encodes market-relevant information in machine-readable form. Includes economic entities, representations, representation capital, computational visibility, eligibility, admissibility, and VPRs.

Primitives (11)

established

Canonical Representation

A single, authoritative machine-readable representation of an entity that serves as the source of truth for AI-mediated discovery and coordination.

canonical_entity_infrastructurerepresentation_governanceentity_driftcanonical_resolution
established

Machine-Readable Entity

An entity represented in structured, machine-readable format that AI systems can interpret, compare, and reason about without ambiguity.

canonical_representationverified_property_recordvpr
established

Verified Property Record (VPR)

A canonical, machine-readable property record with verified attributes, trust signals, and action protocols designed for AI-mediated discovery and transaction coordination.

canonical_representationmachine_readable_entityverification_primitive
established

Representation Efficiency

The degree to which a representation conveys selection-relevant information concisely and completely, enabling efficient AI reasoning without redundancy or omission.

representation_efficiency_scoreinference_burden_scoreselection_readiness
hypothesis

Four-Layer Architecture

A framework for understanding AI-mediated markets as four interdependent layers: Representation, Reasoning, Action, and Governance.

cognitive_market_infrastructurerepresentation_governanceai_mediated_markets
established

Economic Entity (e)

e — An economic entity or asset that can be represented in AI-mediated markets.

machine_readable_entitycanonical_representation
established

Entity Representation r(e)

r(e) — The representation of entity e in machine-readable form.

economic_entitycanonical_representationmachine_readable_entity
hypothesis

Representation Capital (RC)

RC(e) — Accumulated stock of machine-readable representation quality that increases computational admissibility probability.

computational_admissibilitycomputational_visibility
hypothesis

Computational Visibility (CV)

CV(e) — Extent to which an asset is discoverable by AI systems through machine-readable channels.

representation_capitaldiscoverability
hypothesis

Computational Admissibility (CA)

CA(e) — Technical eligibility for allocative processing, determined by representation quality thresholds.

computational_eligibilityrepresentation_capital
hypothesis

Representation Deficit

A condition in which an asset or operator lacks sufficient completeness, verification, freshness, provenance, consistency, or machine interpretability for reliable AI-mediated discovery and comparison.

completenessaccuracyfreshnessverifiability