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From Product Search to Preference Articulation: The Economics of Agentic Commerce — arXiv:2608.08395

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.08395
License (as archived): CC0-1.0
Archived: 2026-10-19T08:55:51.123Z
SHA-256 of the archived text: cf3ccbe79596ffeb5443d5916e8fec0e08776f4952fa3153e3cf28e689074498
Stable URL of this page: https://metraipolis.com/documento/dc_197fec489aa2

How to cite

From Product Search to Preference Articulation: The Economics of Agentic Commerce — arXiv:2608.08395 — arXiv (en), https://arxiv.org/abs/2608.08395 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_197fec489aa2 (Metraipolis archive dc_197fec489aa2, sha256 cf3ccbe79596ffeb5443d5916e8fec0e08776f4952fa3153e3cf28e689074498)

BibTeX

@misc{dc197fec489aa2,
  title = {From Product Search to Preference Articulation: The Economics of Agentic Commerce — arXiv:2608.08395},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.08395},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_197fec489aa2; sha256 cf3ccbe79596ffeb5443d5916e8fec0e08776f4952fa3153e3cf28e689074498},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.08395. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents. We compare manual search, which accurately evaluates a limited product set, with agentic search, which screens a broad catalog through noisy representations of preferences and products. Preference complexity is the number of satisfaction-relevant dimensions that are difficult to articulate before search but readily evaluated upon inspection. Consumers have finite attention and choose search intensity: products inspected manually or preference-refinement depth with an agent. We obtain three findings. First, manual search collapses beyond a finite complexity threshold: inspection ceases, mismatch reaches the no-search benchmark, and platform revenue falls to zero. Agentic search avoids this collapse. Once refinement becomes worthwhile, it remains worthwhile as complexity rises; mismatch stays below the no-search benchmark and revenue remains positive, although articulation effort and mismatch may increase. Second, platforms rank the regimes by conversion revenue, whereas consumers also bear search expenditure. When manual inspection is sufficiently inexpensive, agentic search becomes revenue-superior before consumers voluntarily adopt it, creating an adoption lag in which consumers rationally continue manual search. Third, conditional on agentic participation, platforms may assign lower fidelity to consumers with larger attention budgets because they can offset noisier representations through additional refinement, yielding an inverted fidelity allocation. Agentic commerce thus shifts scarcity from product inspection to preference articulation, making consumers' willingness and ability to interact central to voluntary use and platform fidelity design.

Tema degli esploratori: economia dei servizi IA (retro-catalogo arXiv: annunci del 2026-08-11 in cs.GT).
Categorie arXiv: Theoretical Economics (econ.TH) ; Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT).
Pagina del lavoro: https://arxiv.org/abs/2608.08395 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T08:52:22.707Z dalla pagina https://arxiv.org/catchup/cs.GT/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).