Problem
Research questions and friction points this paper is trying to address.
Ranking
Position Bias
AI Agents
Search Behavior
Consumer Choice
Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf
🤖 AI Summary
研究探讨了AI代理购物时位置偏好的影响，通过对比人类与四种大型语言模型在随机排序酒店列表中的表现，发现页面中位置对AI选择的影响较弱且非单调。
📝 Abstract
Search rankings are valuable because human attention is scarce and sequential. Higher-placed alternatives are easier to find, so they are examined and bought more often. Consumers are now delegating search to AI agents that can ingest an entire results page at once. Randomizing the order of one hundred hotel listings across 5,000 AI agent sessions, we compare four large language models against human field data. AI agents search more deeply than humans and never decline to buy. Position still predicts which listings are inspected, but weakly and non-monotonically: the middle of a results page has the lowest probability of inspection, not the bottom. Position reaches the choice stage for some models and not others, a heterogeneity that tracks neither provider nor capability. All models nonetheless converge on the same undominated listing. For agentic search, the attributes displayed on a results page matter more than placement within it.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI agents
position bias
search depth
non-monotonic inspection probability
attribute importance
🔎 Similar Papers
2024-07-09arXiv.orgCitations: 5
