Research Primitives

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

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

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

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

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

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

ARI(e) = D(e) × I(e) × C(e) × V(e) × P(e) × T(e) — A multiplicative index measuring whether an economic object is ready for AI-mediated discovery, comparison, verification, and transaction initiation.

D(e) — The ability of AI agents to find an economic object through computational search and discovery mechanisms.

I(e) — The degree to which an object is represented in machine-parseable forms that AI systems can understand and reason about.

C(e) — The extent to which an object's attributes enable machine comparison across alternatives within consideration sets.

V(e) — The ability to verify claims about an object through trusted, machine-readable evidence sources.

P(e) — The ability of AI agents to understand what actions are permitted and to act within defined authorization boundaries.

GARI(e, j) = ARI(e) × J(e, j) × S(e) — Extends ARI with jurisdictional legibility and semantic portability for cross-border AI-mediated markets.

J(e, j) — The ability of AI agents to understand the legal, regulatory, tax, compliance, ownership, and transaction context of an economic object within jurisdiction j.

S(e) — The ability of a representation to remain meaningful across languages, standards, units, market conventions, and jurisdictions.

CMA — The capacity of an economic object to remain discoverable and comparable across market boundaries, platforms, and jurisdictions.

The systems, protocols, and institutions that establish authority, verification, and interoperability for canonical representations in AI-mediated markets.

A machine-readable attestation, certification, or proof that validates the accuracy, authenticity, or status of a representation attribute.

The right to control, update, and authorize the canonical representation of an entity, determining discoverability autonomy and market participation.

A constraint requiring explicit approval from the entity owner before AI systems can execute binding actions like booking, payment execution, or contractual commitments.

RS — Control over how economic entities, assets, and services are represented to AI systems.

RSL — Governance infrastructure ensuring representation autonomy and control.

CCI — Control over systems that determine which options enter consideration sets in AI-mediated markets.

IM — Concentration over computational consideration infrastructure as allocative monopoly power.

DEP — Extent of dependence on AI systems for discovery and selection.

IA — Allocative assets outside traditional measurement frameworks.

NDL — Condition where valuation depends on network relationships and representation quality.

ICT — The threshold at which control over consideration infrastructure enables allocative monopoly.

PID — A canonical, persistent identifier for a property that works across all systems, platforms, and jurisdictions.

PROV — A record of what was done, by whom, when, and with what authority for each property record.

LEG — Ownership structure, encumbrances, liens, constraints, and rights attached to a property.

DOC — Supporting documents with verification status for a property record.

TAX — Assessment, payment status, and jurisdictional tax information for a property.

ZON — Land use rules, building codes, regulatory restrictions, and urban constraints affecting a property.

CTI — The representational layer of international trade infrastructure that allows firms, assets, services, and jurisdictions to be discovered, compared, verified, and acted upon by AI agents.

JLI — Systems that make legal, regulatory, tax, compliance, ownership, and transaction contexts machine-readable for AI agents.

RS-AIM — Control over how economic entities, assets, and services are represented to AI systems in cross-border markets.

DNTB — A non-interoperable representation, incompatible schema, fragmented verification system, or closed platform record that prevents economic objects from being parsed, verified, or compared by AI agents across markets.

The economic principle that fixed costs of agent-ready representation should be shared across participants rather than duplicated.

A standardized 0-100 score measuring how effectively a property record enables AI-mediated understanding, comparison, and selection.

A standardized score measuring how efficiently a property record conveys selection-relevant information, balancing completeness with concision.

A composite score combining representation quality, trust signals, and discoverability factors to predict AI-mediated selection likelihood.

The degree to which an asset representation contains the fields, attributes, metadata, and semantic descriptors required for computational evaluation.

The degree to which the representation corresponds to the actual state of the asset.

The degree to which claims about the asset can be externally checked, audited, certified, or linked to trusted evidence.

The process by which AI systems locate, evaluate, compare, and select entities through reasoning rather than human-mediated search or ranking. Also referred to as AI-Mediated Demand.

The effort required for AI systems to locate, parse, reconcile, infer, and validate information before including entities in consideration sets.

The process by which AI systems interpret user requirements, extract selection criteria, and formulate search strategies for discovery.

The degree to which an entity has the attributes, trust signals, and representation completeness required for AI-mediated selection and recommendation.

The amount or share of economically relevant demand captured as qualified enquiries, bookings, matches, or transactions.

An AI-constructed set of candidate assets that is not directly observable through conventional traffic, click, or website analytics.

An economic system where value is created through machine understanding, reasoning quality, and representation efficiency rather than attention acquisition and visibility.

The distinction between platform economics (based on inventory aggregation, attention monetization, and data moats) and protocol economics (based on interoperability, verification, and canonical representation).

The share of demand or revenue dependent on paid, commissioned, portal, OTA, or other intermediated channels.

The combined paid-media, portal, OTA, commission, representation-operating, and related costs incurred per qualified demand outcome.

A reduction in contribution margin caused by increased variable acquisition and distribution costs, holding other relevant factors constant.

The operating productivity of a physical asset, measured through occupancy, turnover, match velocity, time-to-transaction, utilization, or comparable sector-specific outcomes.

Limitations on autonomous AI actions that preserve human control over consequential decisions, particularly for binding transactions and commitments.

A standardized interface that enables representations, trust signals, and action protocols to work across multiple platforms, protocols, and AI systems.

Trust signals encoded in structured, machine-readable format that AI systems can autonomously verify and process for confidence assessment.

CS(e) — The process by which AI systems evaluate and select assets for inclusion in consideration sets, independent of human browsing or clicking.

CR(e) — The stage where AI systems present selected options to users with explanations and rationale for the recommendation.

CV(e) — The process by which AI systems verify claims, trust signals, and preconditions before recommending or acting on assets.

CAc(e) — The extent to which AI systems can initiate or coordinate transactions on behalf of users, subject to authorization and safety constraints.

CC — The rate at which AI-mediated selection and recommendation convert into actual transactions or economic outcomes.

PD — Economic demand that exists or could exist but is not realized through AI-mediated channels due to exclusion, friction, or transmission gaps.