Research Indexes
Standardized scoring systems and measurement frameworks for AI-mediated property selection, representation quality, computational transmission, and sovereign risk assessment.
Primary Indices
Core metrics measuring AI-mediated market readiness and risk
Agent Readiness Index (ARI)
Asset-Level AI-Mediated Market Readiness Assessment
ARI assesses asset-level readiness for AI-mediated economic allocation across six conditions: discoverability, interpretability, comparability, verifiability, permissioned access, and transaction capability. Higher ARI correlates with improved AI-mediated selection outcomes.
Global Agent Readiness Index (GARI)
Jurisdictional AI-Mediated Market Readiness Assessment
GARI assesses jurisdictional readiness for AI-mediated economic allocation across institutional quality, infrastructural legibility, interoperability, and portability. Higher GARI correlates with preserved allocative access under AI-mediated discovery.
Zero-Click Exposure Index (ZCEI)
Dependence on AI-Mediated Discovery Without Human-Initiated Clicks
ZCEI quantifies the degree to which an entity depends on AI-mediated allocation pathways that operate without user-initiated clicks. Higher ZCEI indicates greater exposure to AI-mediated discovery and recommendation systems.
Platform Dependency Index (PDI)
Concentration of Allocative Access Across External Platforms
PDI quantifies the degree to which an entity depends on a small number of platforms or AI intermediaries for allocative access. Higher PDI indicates greater concentration risk and platform lock-in.
Computational Business Risk Index (CBRI)
Composite Static Risk Integrating Exposure, Readiness, Dependency, and Financial Sensitivity
CBRI integrates static measures of AI-mediated risk: zero-click exposure, platform dependency, readiness gaps, and financial sensitivity. CBRI provides a baseline risk assessment before accounting for technological velocity and adaptation.
Dynamic Computational Risk Index (DCRI)
Dynamic Risk Combining Exposure, Velocity, Sensitivity, Readiness, and Adaptation
DCRI extends static risk by incorporating technological velocity and adaptation capacity. DCRI captures how risk evolves as AI-mediated markets change and firms respond or fail to adapt.
Enterprise Adaptation Velocity Index (EAVI)
Speed of Enterprise Adaptation Across Product, Data, Infrastructure, Organizational, and Commercial Dimensions
EAVI measures organizational response capacity to AI-driven economic change. EAVI assesses adaptation speed across product evolution, data infrastructure, AI readiness, organizational change, and commercial model adaptation.
Computable Asset Ratio (CAR)
Share of Assets That Are Registered, Identified, Structured, Verified, Fresh, Interoperable, Discoverable, and Actionable
CAR measures asset-level computability—the degree to which assets are registered, identified, structured, verified, fresh, interoperable, discoverable, and actionable for AI-mediated allocation.
National Computable Economy Index (NCEI)
National-Level Measure of Asset and Economic-Object Computability
NCEI aggregates CAR measures across asset classes and economic sectors to provide a national-level view of economic computability. NCEI indicates how well-positioned a jurisdiction is for AI-mediated economic activity.
Sovereign Adaptation Velocity Index (SAVI)
Effective Speed at Which Jurisdictions Recognize, Implement, Diffuse, Evaluate, and Update Responses to AI-Driven Economic Change
SAVI measures sovereign response capacity to AI-driven economic change across five stages: recognition, implementation, diffusion, evaluation, and update. Higher SAVI indicates better capacity to preserve policy effectiveness and competitiveness under AI-mediated allocation.
Derived, Diagnostic, and Sovereign Measures
Composite metrics, risk assessments, and sovereign capacity indicators
Compound Regional Adaptation Velocity Index (CRAVI)
Regional Aggregation of SAVI Adjusted for Coordination and Integration
CRAVI aggregates SAVI across jurisdictions within a region, adjusting for coordination and integration factors. CRAVI provides a regional-level view of adaptation capacity.
Global Computable Economy Index (GCEI)
Cross-Jurisdiction Aggregate of National Computability
GCEI aggregates NCEI across jurisdictions to provide a global view of economic computability for AI-mediated markets.
Sovereign Adaptation Gap (SAG)
Difference or Ratio Between Technological Velocity and Sovereign Adaptation Velocity
SAG measures the disconnect between how fast AI-mediated markets are changing and how quickly sovereign institutions can respond. Larger SAG indicates greater adaptation risk.
Dynamic Monetary Sovereignty Risk Index (DMSRI)
Principal Sovereign-Risk Output Linking External AI Dependency, Transmission Gaps, Adaptation Velocity, and Monetary-Policy Effectiveness
DMSRI links external AI dependency, computational transmission gaps, adaptation velocity, and monetary-policy effectiveness into a sovereign risk measure. Higher DMSRI indicates greater risk to monetary sovereignty under AI-mediated allocation.
Operational Demand Readiness Index (ODRI)
Exploratory Composite Combining VIS, GARI, ARS, and Inverse CDL to Assess Operational Demand Readiness
ODRI is an exploratory composite combining VIS, GARI, ARS, and inverse CDL to assess operational demand readiness. Candidate components: VIS (representation quality), GARI (readiness), ARS (market outcomes), inverse CDL (demand capture). Exploratory composite — weights not empirically calibrated.
Financial Distribution Efficiency Index (FDEI)
Exploratory Composite Combining Inverse PDD, Inverse RAAC, Inverse CMP, and Asset-Productivity Outcomes
FDEI is an exploratory composite combining inverse PDD, inverse RAAC, inverse CMP, and asset-productivity outcomes to assess financial distribution efficiency. Candidate components: (1 − PDD), (1/RAAC), (1 − CMP), and asset productivity. Exploratory composite — not suitable for ranking or valuation without validation.
HomeSelf Demand Efficiency Index (HDEI)
Umbrella Dashboard Construct Connecting Operational Readiness, Demand Capture, Distribution Dependency, Acquisition Costs, Margin Pressure, and Asset Productivity
HDEI is an umbrella dashboard construct connecting operational readiness, demand capture, distribution dependency, acquisition costs, margin pressure, and asset productivity. HDEI must not be used for ranking, valuation, or capital allocation until empirically validated. RROI is excluded from the primary composite to avoid circularity; RROI may instead be used as an outcome variable for predictive-validity testing.
Representation and Readiness Primitives
Foundational metrics for representation quality and AI-readiness
Representation Composability Score (RCS)
Structured Representation Composability for AI-Mediated Selection
RCS evaluates the composability of property representations—the degree to which structured attributes enable combination, comparison, and integration with other representations in AI-mediated consideration sets.
Machine Readability Index (MRI)
Structured Representation Quality for AI-Mediated Understanding
MRI evaluates the machine-readiness of property records by assessing completeness, structure quality, verifiability, and consistency. Higher MRI correlates with improved AI-mediated discoverability and selection.
Representation Efficiency Score (RES)
Information Density and Computational Efficiency for AI-Mediated Processing
RES quantifies the information density of representation—how much selection-relevant information is conveyed per unit of content. Efficient representations enable faster reasoning without information overload.
Inference Burden Score (IBS)
Computational Cost Required for AI-Mediated Information Extraction
IBS quantifies the computational effort AI systems must expend to understand a representation. High IBS arises from unstructured data, missing attributes, narrative-embedded facts, or ambiguous semantics.
Structural Readiness Score (SRS)
Infrastructure and Protocol Readiness for AI-Mediated Transaction Support
SRS evaluates how well asset infrastructure supports AI-mediated transaction workflows. High SRS indicates structured action protocols, authorization systems, verification mechanisms, and error handling.
Token Efficiency Ratio (TER)
Information Density per Token for Inference-Efficient Processing
TER measures how efficiently asset representations convey information per token. Higher TER indicates more compact, inference-efficient representation.
AI Allocability Score (AAS)
Probability That an Asset Is Admitted, Processed, Compared, and Recommended by AI Systems
AAS measures the probability that an asset successfully passes through AI-mediated consideration set construction, comparison, and recommendation stages.
AI Allocability Discount (AAD)
Potential Valuation, Liquidity, or Demand-Access Penalty from Poor Computational Representation
AAD captures the potential reduction in asset value, liquidity, or demand access from expensive computational representation. AAD bridges allocability and valuation risk.
Verified Property Record Score (VPRS)
Completeness and Quality of Verified Property Record Representation
VPRS assesses the completeness and quality of verified property record (VPR) representation for AI-mediated discovery and selection.
Computational Liquidity (CL)
Degree to Which an Asset Can Be Discovered, Interpreted, Verified, Compared, and Acted Upon by Computational Agents
CL measures machine-processability under bounded inference. Higher CL indicates better allocability with lower computational cost.
VPR Integrity Score (VIS)
Technical Integrity of an Asset Representation Across Completeness, Verification, Freshness, Provenance, and Consistency
VIS measures the technical integrity of an asset representation across five dimensions: Completeness, Verification, Freshness, Provenance, and Consistency. VIS informs representation-dependent GARI dimensions but is not mechanically embedded in GARI.
Computational Transmission Metrics
Metrics measuring value transmission through AI-mediated channels
Citation Transmission Rate (CiTR)
Rate at Which Citations Successfully Transmit Through AI-Mediated Consideration Sets
CiTR measures how effectively citations or references transmit through AI-mediated consideration sets to reach users and influence allocation.
Traffic Transmission Rate (TTR)
Rate at Which Traffic-Based Discovery Successfully Converts to AI-Mediated Consideration
TTR measures how effectively traffic-based discovery converts to AI-mediated consideration sets. Lower TTR indicates greater leakage between traffic and consideration.
Recommendation Transmission Rate (RTR)
Rate at Which Recommendations Successfully Transmit to User Consideration and Action
RTR measures how effectively AI-generated recommendations transmit to user consideration and action. Lower RTR indicates recommendation leakage or rejection.
Action Transmission Rate (ATR)
Rate at Which Actionable Recommendations Successfully Convert to Completed Transactions
ATR measures how effectively actionable recommendations convert to completed transactions. Lower ATR indicates failure at final transaction stage.
Computational Demand Transmission Rate (CDTR)
Rate at Which Demand Successfully Transmits Through Computational Allocation Channels
CDTR measures overall demand transmission effectiveness through AI-mediated allocation channels from signal emission to allocative outcome.
Visibility Transmission Gap (VTG)
Difference Between Visibility-Based Exposure and AI-Mediated Consideration Inclusion
VTG captures the gap between how visible an entity is (human-accessible) and how often it appears in AI-mediated consideration sets. Positive VTG indicates visibility without allocability.
Computational Visibility Loss (CVL)
Proportion of Visible Entities Excluded from AI-Mediated Consideration Sets
CVL measures the proportion of entities that are human-visible but excluded from AI-mediated consideration sets. Higher CVL indicates greater allocative exclusion despite visibility.
Computational Access Gap (CAG)
Gap Between Potential and Actual Computational Access to Entities
CAG measures the gap between what computational access is potentially possible for an entity and what is actually realized. Higher CAG indicates unrealized allocative potential.
Economic Recommendation Loss (ERL)
Economic Value of Recommendations Lost to Transmission Failure
ERL quantifies the economic value lost when recommendations fail to transmit due to computational transmission attrition. ERL measures allocative inefficiency in monetary terms.
Action Success Rate (ASR)
Rate at Which AI-Initiated Actions Successfully Complete Without Error or Interruption
ASR measures how reliably AI-initiated actions complete successfully. Higher ASR indicates more reliable agent infrastructure.
AI Recommendation Share (ARS)
Share of Relevant AI Responses That Include or Recommend an Asset or Operator
ARS measures the share of relevant AI responses that include or recommend an asset or operator. Includes granular variants: citation share, mention share, shortlist share, recommendation share, and action share. ARS is an observed controlled-inclusion metric, not an estimate of total economic demand.
Computational Demand Leakage (CDL)
Portion of Estimated Relevant AI-Mediated Demand Not Captured Due to Representation Deficits
CDL measures the portion of estimated relevant AI-mediated demand that is not captured due to representation deficits. CDL = 1 − Captured Relevant AI-Mediated Demand / Estimated Relevant AI-Mediated Demand. The denominator is latent and must be independently estimated. CDL must not be defined simply as 1 − ARS.
Computational Visibility Gap (CVG)
Difference Between Traditional Search/Platform Visibility and AI-Mediated Visibility Inside Consideration Sets
CVG measures the difference between traditional digital visibility (SEO rankings, platform presence) and AI-mediated visibility inside consideration sets. CVG = Conventional Digital Visibility − AI-Mediated Visibility. Assets may be highly visible to humans but invisible to AI consideration.
Financial and Empirical Bridge Metrics
Financial outcomes and empirical validation measures
Computational Revenue at Risk (CRaR)
Revenue Exposed to Computational Transmission Attrition
CRaR quantifies the revenue at risk from computational transmission attrition—the portion of revenue that may be lost if AI-mediated allocation channels degrade or fail.
Representation Return on Investment (R-ROI)
Return on Investment in Representation Infrastructure Improvements
R-ROI measures the return on investment in representation infrastructure—the allocative benefit gained per unit of investment in improving computability.
AI-Mediated Revenue Share (AMRS)
Proportion of Revenue Attributable to AI-Mediated Allocation Channels
AMRS measures what proportion of an entity's revenue is attributable to AI-mediated allocation channels. Higher AMRS indicates greater dependence on AI for demand.
Representation Selection Elasticity (RSE)
Responsiveness of Selection Probability to Representation Quality Changes
RSE measures how sensitive selection probability is to changes in representation quality. Higher RSE indicates greater marginal benefit from representation improvements.
Inference Cost per Successful Action (ICSA)
Computational Cost per Successfully Completed Transaction
ICSA measures the computational cost incurred per successfully completed transaction. Lower ICSA indicates more efficient transaction processing.
Monetary Velocity Gap (MVG)
Difference Between Traditional and AI-Mediated Monetary Velocity
MVG captures the disconnect between traditional monetary velocity measurements and AI-mediated velocity. Positive MVG indicates traditional metrics overstate effective velocity.
Transmission-Adjusted Revenue Variation (TRVR)
Revenue Volatility Adjusted for Computational Transmission Factors
TRVR adjusts revenue volatility measures to account for computational transmission factors. Traditional volatility measures may understate or overstate risk when transmission channels change.
Cross-Border Computational Reallocation Index (CBCRI)
Extent to Which AI-Mediated Allocation Redirects Demand Across Jurisdictions
CBCRI measures cross-border computational reallocation—the extent to which AI-mediated allocation causes demand intended for one jurisdiction to be reallocated to another. Higher CBCRI indicates greater cross-border leakage.
Monetary Policy Adaptation Index (MPAI)
Degree to Which Monetary Policy Accounts for AI-Mediated Allocation Effects
MPAI assesses the extent to which monetary policy frameworks, calibrations, and communications account for AI-mediated allocation effects. Higher MPAI indicates more AI-aware monetary policy.
Paid Demand Dependency (PDD)
Share of Measurable Demand or Revenue Dependent on Paid or Commissioned Channels
PDD measures the share of measurable demand or revenue dependent on paid or commissioned channels. PDD = Paid and Commissioned Demand or Revenue / Total Measurable Digital Demand or Revenue. Lower PDD indicates more owned-channel performance.
OTA Dependency Ratio (ODR)
Share of Room Revenue Through OTAs Versus Total Room Revenue
ODR measures the share of hospitality room revenue booked through Online Travel Agencies versus total room revenue. ODR = Room Revenue Through OTAs / Total Room Revenue. Applicable sector: Hospitality.
Portal Dependency Ratio (PDR)
Share of Qualified Demand Through Portals Versus Total Qualified Demand
PDR measures the share of qualified real estate demand that arrives through property portals versus total qualified demand. PDR = Qualified Demand Through Portals / Total Qualified Demand. Applicable sector: Real Estate.
Representation-Adjusted Acquisition Cost (RAAC)
Full Acquisition and Distribution Cost Per Qualified Outcome When Representation Is Treated as Operating Infrastructure
RAAC measures the full acquisition and distribution cost per qualified outcome when representation is treated as operating infrastructure. RAAC = (Paid Media Costs + Portal or OTA Commissions + Representation Operating Costs) / Qualified Demand Outcomes. RAAC captures the true cost of demand acquisition when representation quality affects channel mix.
Computational Margin Pressure (CMP)
Ratio of Incremental Acquisition and Distribution Cost to Contribution Margin Before Incremental Cost
CMP measures the ratio of incremental acquisition and distribution cost to contribution margin before incremental cost. CMP = Incremental Acquisition and Distribution Cost / Contribution Margin Before Incremental Cost. Alternative formulations: CMP1 = Total Distribution Cost / Revenue, CMP2 = Total Distribution Cost / Contribution Margin, CMP3 = Incremental Distribution Cost / Incremental Contribution Margin. No single formulation is empirically validated.
Distribution Cost per Occupied Night (DCON)
Total Distribution Costs Per Occupied Room Night Across OTA Commissions, Paid Media, Metasearch, and Representation
DCON measures total distribution costs per occupied room night. DCON = (OTA Commissions + Paid Media + Metasearch Fees + Representation Costs) / Occupied Room Nights. Applicable sector: Hospitality.
Qualified Match Rate (QMR)
Ratio of Qualified Enquiries to Total Enquiries as a Measure of Demand Quality
QMR measures the ratio of qualified enquiries to total enquiries. QMR = Qualified Enquiries / Total Enquiries. Higher QMR indicates better lead quality and more efficient acquisition spend. Applicable sector: Real Estate.
Computational RevPAR (cRevPAR)
Revenue from Computationally Eligible Inventory Per Available Eligible Room Night
cRevPAR measures revenue from computationally eligible inventory per available eligible room night. cRevPAR = Revenue from Computationally Eligible Inventory / Available Eligible Room Nights. Clearly distinguish this proposed metric from conventional gross RevPAR. Applicable sector: Hospitality.
AI-Adjusted Days on Market
Expected Time to Qualified Match or Transaction After Controlling for Representation Quality and Asset Characteristics
AI-DOM measures the expected time to qualified match or transaction after controlling for representation quality and relevant asset and market characteristics. This is an empirically estimated time-to-event metric, not a fixed formula. Applicable sector: Real Estate.
Computational Property Liquidity
Capacity of a Property to Enter AI Consideration Sets and Generate Qualified Matches
Computational Property Liquidity measures the capacity of a property to enter AI consideration sets and generate qualified matches. This is a proposed construct; no validated universal formula exists. Applicable sector: Real Estate.
Computational Occupancy Leakage
Potentially Sellable Room Nights Not Captured Because Inventory Is Excluded, Misunderstood, Distrusted, Stale, or Non-Actionable in AI-Mediated Discovery
Computational Occupancy Leakage measures potentially sellable room nights that are not captured because inventory is excluded, misunderstood, distrusted, stale, or non-actionable in AI-mediated discovery. This is a proposed construct; the denominator requires estimation. Applicable sector: Hospitality.