The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce
Sai Srikanth Madugula, Peplluis Esteva de la Rosa, Daya Shankar
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Key claim
AI agents require a new model for consumer loyalty.
In plain English
The rise of autonomous AI agents is changing how consumers interact with brands, challenging traditional loyalty models. Current frameworks do not account for the complexities of AI decision-making and trust dynamics. This paper introduces a new model that integrates these factors, allowing brands to better understand and engage with machine customers. Builders might find this framework useful for developing strategies that align with evolving consumer behaviors driven by AI.
Introduces a new model for understanding AI-driven consumer loyalty.
Proposes empirical validation but lacks established baselines.
Deep reliability assessment
The methodology supports a novel model for understanding brand loyalty in the context of agentic AI, but the claims about its applicability across all autonomous commerce scenarios may be overextended without empirical validation.
Reproducibility
No open source code or dataset is mentioned in the paper.
Key figure
Figure 1 illustrates the Tripartite DVM-HALL Architecture, showing the bidirectional flows of trust, algorithmic loyalty, and emotional brand equity across a multi-agent marketplace.