基本B效应：基于大语言模型（LLM）的智能体的使用会降低人们选择的独特性与多样性
The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices
AI总结：
该研究通过实地研究和对照实验发现，使用基于LLM的智能体会降低人们选择的独特性与多样性，且顺序选择、智能体个性化会放大该效应，相关发现对设计维护人类多样性的AI系统具有重要意义。
摘要：
大语言模型（LLM）越来越多地代表人们行事：它们撰写电子邮件、购买杂货、预订餐厅。虽然将人类决策外包给AI可能很方便，但这引发了一个基本问题：将关乎身份定义的选择委托给AI会如何塑造人们的自我？我们通过一项大型实地研究和一项对照实验，研究了智能体LLM对两个与身份相关的结果的影响：人际独特性（一个人的选择相对于他人的独特程度）和个人内多样性（单个个体随时间变化的选择广度）。研究1使用了来自1000名美国用户社交媒体行为的110000个真实选择，将通用智能体和个性化智能体与人类基线进行比较。两种智能体都会将人们的选择转向更受欢迎的选项，降低其偏好的独特性。虽然与通用智能体相比，个性化智能体的使用缓和了这种同质化，但它也通过缩小人们在主题和心理偏好上的探索，更强烈地压缩了人们偏好组合的多样性。研究2在一项模仿常见现实场景（如选择电影）的在线实验中复制了这些模式，该实验使我们能够直接将AI智能体的选择与348名参与者（12097个人类选择）做出的选择进行比较。研究结果还表明，当选择是按顺序做出（而非批量），且智能体依赖于特定领域的用户信息进行个性化时，AI智能体的扁平化效应会被放大。理解AI智能体如何压缩人类体验（以及其中涉及的权衡）对于设计增强人类能动性、维护思想、品味和表达多样性的系统至关重要。
英文摘要：
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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