Year: 2026
Type: preprint
Abstract: When shopping is delegated to AI agents, it is unclear whether the ranking advantage documented for humans persists. Across 7,000 sessions with five large language models (LLMs) and varying reasoning effort, we compare AI agents with human field data. AI agents search more extensively than human consumers. As with humans, lower-ranked listings are less likely to be inspected, although the effect is smaller for AI agents. Unlike humans, AI agents show a pattern consistent with the lost-in-the-middle effect, whereby middle listings have the lowest probability of inspection. At the choice stage, position effects are concentrated at lower reasoning effort, but higher effort reduces the middle penalty for every LLM that exhibits it. These findings suggest that, when search is delegated, displayed attributes may matter more than placement, and that exposure to position bias depends on how the AI agent is configured.
Source: arXiv (Cornell University)
Authors: Wadi, Davood, Ma, Yu
Institution: McGill University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204189884