用AI代理做决定会让人的选择变趋同，削弱独特性和多样性。
The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices
- 用大模型代理代为决策，使人们更倾向热门选项，降低选择独特性。
- 个性化代理虽稍减趋同，但大幅压缩个人长期选择的多样性。
- 顺序决策和依赖用户数据会加剧这种同质化，适合关注人机协作设计者阅读。
大型语言模型（LLMs）越来越多地代替人类做出决策：撰写邮件、购买食品、预订餐厅。尽管这种外包行为带来便利，但也引发根本问题：将定义身份的选择权交给AI，会如何影响人的自我形成？通过一项大规模实地研究和一项受控实验，我们考察了代理型LLM对两个与身份相关结果的影响：人际独特性（个体选择相对于他人的独特程度）和内在多样性（单个人在时间维度上的选择广度）。研究1基于1000名美国用户在社交媒体上的11万条真实行为数据，对比通用代理与个性化代理与人类基准的表现。两种代理均使人们的选择趋向流行选项，降低了偏好独特性。尽管个性化代理在缓解同质化方面优于通用代理，但其显著压缩了个人偏好组合的多样性，缩小了跨主题和心理亲和力的探索范围。研究2在在线实验中复现了上述模式，模拟常见现实场景（如选电影），直接比较348名参与者（共12,097条人类选择）与AI代理的决策。结果表明，当决策以顺序方式进行（而非批量处理），且代理依赖特定领域用户信息进行个性化时，这种扁平化效应被进一步放大。理解AI代理如何压缩人类体验及其权衡关系，对于设计能增强人类自主性并保护思想、品味与表达多样性的系统至关重要。
原文摘要 · Abstract (English)
Large language models (LLMs) increasingly act on people's behalf: they write emails, buy groceries, and book restaurants. While the outsourcing of human decision-making to AI can be convenient, it raises a fundamental question: how does delegating identity-defining choices to AI shape who people become? Across a large field study and a controlled experiment, we study the impact of agentic LLMs on two identity-relevant outcomes: interpersonal distinctiveness - how unique a person's choices are relative to others - and intrapersonal diversity - the breadth of a single person's choices over time. Study 1 uses 110,000 real choices drawn from social media behavior of 1,000 U.S. users to compare generic and personalized agents to a human baseline. Both agents shift people's choices toward more popular options, reducing the distinctiveness of their preferences. While the use of personalized agents tempers this homogenization (compared to generic agents), it also more strongly compresses the diversity of people's preference portfolios by narrowing their exploration across topics and psychological affinities. Study 2 replicates these patterns in an online experiment which mimics common real-world scenarios (e.g., choosing movies) and allows us to directly compare the AI agent's choices to those made by 348 participants (12,097 human choices). The findings also suggest that the flattening effects of AI agents are amplified when choices are made sequentially (vs. batch), and when agents rely on domain-specific user information for personalization. Understanding how AI agents compress human experience (and the trade-offs involved) is critical for designing systems that augment human agency and safeguard diversity in thought, taste, and expression.
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