The AI Recommendation Economy: Measurement, Execution, and Trust in AI-Mediated Markets (Technical Revision 1.2.0)

The market's instinct has been to count "mentions" or "AI visibility." This instinct is wrong. Counting mentions measures text volume, not commercial consideration. It ignores whether a recommended supplier can actually be contacted, can meet compliance requirements, or exists as a resolvable legal entity. In high-consequence B2B domains, a recommendation that cannot be executed is a loss.

**The AI Recommendation Economy** proposes a rigorous, auditable measurement architecture for this new paradigm. It argues that the correct measurand is a distribution over recommendation outcomes, conditioned on a precisely specified commercial context. 

#### Core Concepts Introduced in This Framework:
*   **Market Question Cell (MQC):** A context-specific unit of measurement defined by intent, specification, geography, language, and buyer role.
*   **AI Recommendation Share (ARS) & Intensity (ARI):** Formal metrics that separate event probabilities from relative allocations, ending the era of vanity metrics.
*   **Executable Recommendation Share (ERS) & Recommendation-to-Executable Loss (REL):** A three-state audit (Pass/Fail/Unknown) that measures how much of a brand’s recommendation mass has a buyer-verifiable path to procurement.
*   **Evidence Verifiability Rate (EVR):** An integrity instrument designed to detect and deter synthetic authority and manipulation.
*   **Protocol Pre-registration:** A governance mechanism requiring the measurement protocol to be hashed and committed before baseline data collection.

#### Who Should Read This Book?
This book is written for measurement designers, procurement leaders, enterprise software evaluators, and researchers in AI-mediated markets. It focuses on high-consequence domains—such as B2B SaaS, medical devices, and industrial procurement—where the gap between an AI recommendation and a verified purchase order is widest.

#### A Note on Epistemic Status:
Crucially, this volume is a **Proposed Framework**, not an empirically validated industry standard. All numeric illustrations are explicitly labeled as synthetic. It provides the mathematical definitions, sampling protocols, and statistical estimators necessary to conduct rigorous empirical validation, rather than claiming to have completed it. 

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**Official DOI (Zenodo):** [10.5281/zenodo.24237460](https://doi.org/10.5281/zenodo.24237460)
**Author Website:** [https://almodarresieh.github.io/](https://almodarresieh.github.io/)