# Generative Agent Simulations of 1,000 People

*Source* · `src-park-2024-1000-people` · status: **n/a**

| field | value |
|---|---|
| kind | Preprint |
| venue | arXiv |
| date | 2024-11-15 |
| url | https://arxiv.org/abs/2411.10109 |
| arxiv | 2411.10109 |
| accessed | 2026-10-24 |

## Authors

- Joon Sung Park
- Carolyn Q. Zou
- Jonne Kamphorst
- Niles Egan
- Aaron Shaw
- Benjamin Mako Hill
- Carrie Cai
- Meredith Ringel Morris
- Percy Liang
- Robb Willer
- Michael S. Bernstein

## Notes

Agents from two-hour interviews with 1,052 U.S. participants. The arXiv abstract as fetched (later version, retitled LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals) gives normalized GSS accuracy of 83% interview-only, 82% survey-only, 86% combined, 74% demographics-only; the Stanford HAI brief reports 85%. Reduced accuracy disparities across racial and ideological groups.

## Backlinks (computed)

**cited by:** [LLM models of individual human choice](../technique/human-behavior-simulation-models.md)

## Citation

Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst, Niles Egan, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Percy Liang, Robb Willer, Michael S. Bernstein (2024-11-15). *Generative Agent Simulations of 1,000 People*. arXiv https://arxiv.org/abs/2411.10109

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Provenance: created in run `2026-10-24-techniques-llm-agents`.