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

arXiv: https://arxiv.org/abs/2509.02910 · HTML: https://mlsanity.com/paper/2509.02910/

## Key facts

- Cited by 5 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 12 of its 27 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## 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, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Does Writing with Language Models Reduce Content Diversity?](https://mlsanity.com/paper/2309.05196/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Large Language Models Can Infer Psychological Dispositions of Social Media Users](https://mlsanity.com/paper/2309.08631/) (2023)
- [The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models](https://mlsanity.com/paper/2502.11266/) (2025)
- [Growing a Tail: Increasing Output Diversity in Large Language Models](https://mlsanity.com/paper/2411.02989/) (2024)
- [One world, one opinion? The superstar effect in LLM responses](https://mlsanity.com/paper/2412.10281/) (2024)
- [Hybrid Forecasting of Geopolitical Events](https://mlsanity.com/paper/2412.10981/) (2024)

## Built on by (most cited first, 5 of 5)

- [The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety](https://mlsanity.com/paper/2601.06116/) (2026)
- [Voice-based debate with an AI adversary is associated with increased divergent ideation](https://mlsanity.com/paper/2603.27073/) (2026)
- [LLM Consumer Behavior Theory: Foundations of a Novel Research Field](https://mlsanity.com/paper/2606.18005/) (2026)
- [AI Should Facilitate Democratic Deliberation at Scale](https://mlsanity.com/paper/2609.20059/) (2026)
- [Gacha Decoding: Eliciting Diverse Generations Through Instruction Following](https://mlsanity.com/paper/2610.01382/) (2026)

## Abstract (arXiv)

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.