The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices

2025-09-02

AI Summary

This study investigates how LLM-based agents intervening in identity-relevant decisions affect interpersonal distinctiveness and intrapersonal diversity. Method: Leveraging real-world social media behavioral data from 1,000 U.S. users (110,000 choices), we developed both generic and personalized LLM agents and systematically compared their effects on human choice patterns. Contribution/Results: Both agent types significantly increased preference for popular options, thereby reducing interpersonal distinctiveness. While personalized agents partially mitigated convergence, they more strongly compressed intrapersonal diversity—i.e., reduced cross-topic variability in individual choices—revealing an “efficiency–diversity” trade-off. This work provides the first empirical evidence that AI agents, while enhancing decision efficiency, systematically narrow the human preference space. It offers a novel theoretical framework and reproducible evidence for understanding identity construction and behavioral homogenization in human–AI collaboration.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Application Domains: Humanities & Computational Social Science
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

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 both efficient and effective, it raises a fundamental question: how does delegating identity-defining choices to AI reshape who people become? 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. Using real choices drawn from social-media behavior of 1,000 U.S. users (110,000 choices in total), we compare a generic and personalized agent to a human baseline. Both agents shift people's choices toward more popular options, reducing the distinctiveness of their behaviors and preferences. While the use of personalized agents tempers this homogenization (compared to the generic AI), it also more strongly compresses the diversity of people's preference portfolios by narrowing what they explore across topics and psychological affinities. Understanding how AI agents might flatten human experience, and how using generic versus personalized agents involves distinctiveness-diversity trade-offs, is critical for designing systems that augment rather than constrain human agency, and for safeguarding diversity in thought, taste, and expression.

Problem

• LLM agents reduce distinctiveness of human choices
• AI delegation compresses diversity of personal preferences
• Generic vs personalized agents involve trade-offs homogenization

Innovation

• LLM-based agents reduce choice distinctiveness
• Personalized agents temper homogenization but narrow diversity
• Agents shift choices toward more popular options