# Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf

arXiv: https://arxiv.org/abs/2608.22697 · HTML: https://mlsanity.com/paper/2608.22697/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 6 of 6)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs](https://mlsanity.com/paper/2507.02076/) (2025)
- [Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biases](https://mlsanity.com/paper/2608.10503/) (2026)
- [Consider or Choose? The Role and Power of Consideration Sets](https://mlsanity.com/paper/2302.04354/) (2023)

## Built on by (most cited first, 4 of 4)

- [Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders](https://mlsanity.com/paper/2609.17989/) (2026)
- [Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer](https://mlsanity.com/paper/2609.28372/) (2026)
- [The Default Trap: Rethinking Plan Evaluation in Tool-Using LLM Agents](https://mlsanity.com/paper/2609.36829/) (2026)
- [Who Keeps the Gains from Personal AI Assistants? Seller Adaptation and the Unassisted in a Language-Model Market Simulation](https://mlsanity.com/paper/2610.05823/) (2026)

## Abstract (arXiv)

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.