Pre-Click Exclusion
Exclusion of an asset before any observable human exposure, visit, click, enquiry, or booking event occurs.
Description
Pre-Click Exclusion occurs when AI systems exclude assets during consideration set formation, before any human interaction. This exclusion is invisible to conventional analytics. PCE differs from post-click conversion failure—assets excluded pre-click never have the chance to be considered. PCE is a mechanism of Computational Demand Leakage and creates the Invisible Consideration Set phenomenon.
Related Concepts
Related Research
The Balance-Sheet Economics of AI-Mediated Demand
The migration of discovery and comparison from human-mediated search to AI-generated answers and agentic interfaces may alter the economics of acquiring and distributing demand in physical-asset markets. This paper examines how AI-mediated demand formation could affect customer acquisition costs, distribution dependency, contribution margins, and asset productivity in real estate and hospitality. We propose that zero-click—initially observed as a traffic problem—may transmit structurally into distribution cost inflation and ultimately appear as margin pressure. We formalize a transmission mechanism in which representation deficits may transmit through demand leakage, distribution dependency, and acquisition-cost inflation to contribution-margin compression, while lower qualified-demand capture may separately affect occupancy, time-to-match, and asset productivity. Contribution margin and asset productivity may subsequently interact through operating and reinvestment feedback effects. The paper introduces a measurement architecture designed for empirical validation: representation quality (VIS), readiness (GARI), market outcomes (ARS, PDD, CDL), financial impact (RAAC, CMP, RROI), and exploratory composite indices. The Verified Property Representation (VPR) is positioned as a proposed persistent representation layer intended to improve computational legibility—a testable intervention through which the paper's hypotheses may be validated.
The Zero-Click Economy
The Zero-Click Economy examines how AI-mediated discovery, selection, recommendation, verification, and action alter the transmission of economic signals from policy and demand to firms, assets, households, sectors, and jurisdictions. We introduce the Current Reporting-Period Hypothesis, which states that AI systems construct consideration sets from representations as they exist at inference time, not from the period the policy or demand signal was emitted. This creates Computational Transmission Attrition—policy or demand-induced signals may attenuate, misallocate, or leak before reaching intended economic targets. We formalize Dynamic Computational Risk as the interaction between exposure (dependence on AI-mediated allocation), technological velocity (rate of change in AI-mediated discovery), financial sensitivity (margin of capital, liquidity dependence), and adaptation capacity (speed of organizational response). The paper consolidates the Representation Economy measurement stack: Agent Readiness Index (ARI), Global Agent Readiness Index (GARI), Zero-Click Exposure Index (ZCEI), Platform Dependency Index (PDI), Computational Business Risk Index (CBRI), Dynamic Computational Risk Index (DCRI), Enterprise Adaptation Velocity Index (EAVI), Computable Asset Ratio (CAR), National Computable Economy Index (NCEI), Sovereign Adaptation Velocity Index (SAVI), and sovereign outputs including Compound Regional Adaptation Velocity Index (CRAVI), Global Computable Economy Index (GCEI), Sovereign Adaptation Gap (SAG), and Dynamic Monetary Sovereignty Risk Index (DMSRI).
Related Primitives
Computational Eligibility (CE)
CE(e) — The condition of being discoverable, interpretable, comparable, verifiable, permissioned, and actionable by artificial agents. Also referred to as AI Eligibility.
Computational Transmission Gap (CTG)
CTG = PD - RD — The portion of potential economic demand that is lost due to exclusion, friction, or gaps in AI-mediated channels. Also referred to as Computational Demand Leakage.
Invisible Consideration Set
An AI-constructed set of candidate assets that is not directly observable through conventional traffic, click, or website analytics.