The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce

TL;DR — A conceptual/theoretical paper proposing a framework (DVM-HALL) for how autonomous purchasing agents reshape customer loyalty, plus an auditable metric (NHAS) for measuring human-agent alignment in "machine customer" commerce. It matters because it tries to formalize trust, delegation, and verifiable execution for agents that autonomously buy on a human's behalf.

• Introduces the DVM-HALL model, formalizing brand choice via a softmax over human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution, with recursive per-interaction updates to trust and delegation.
• Proposes the Net Human-Agent Score (NHAS), a risk-weighted, auditable alignment metric built from human feedback, execution logs, benchmark comparisons, and verifiable receipts.
• Integrates a verifiable execution layer for DeFi/tokenized loyalty, treating gas costs, slippage, MEV exposure, and smart-contract risk as predictors of agent brand preference.
• Outlines a three-stage validation plan (controlled shopping experiments, multi-agent market simulations, DeFi testbeds); note this is a proposed framework — no empirical results are reported yet.