# Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions

arXiv: https://arxiv.org/abs/2505.17479 · HTML: https://mlsanity.com/paper/2505.17479/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 7 of 7)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [LLM Generated Persona is a Promise with a Catch](https://mlsanity.com/paper/2503.16527/) (2025)
- [The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective](https://mlsanity.com/paper/2312.15524/) (2023)

## Built on by (most cited first, 22 of 22)

- [TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation](https://mlsanity.com/paper/2510.25536/) (2025)
- [SAGE: A Top-Down Bottom-Up Knowledge-Grounded User Simulator for Multi-turn AGent Evaluation](https://mlsanity.com/paper/2510.11997/) (2025)
- [Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents](https://mlsanity.com/paper/2608.16578/) (2026)
- [When Can Digital Personas Reliably Approximate Human Survey Findings?](https://mlsanity.com/paper/2605.10659/) (2026)
- [TextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AI](https://mlsanity.com/paper/2511.12063/) (2025)
- [PICon: A Multi-Turn Interrogation Framework for Evaluating Persona Agent Consistency](https://mlsanity.com/paper/2603.25620/) (2026)
- [HugAgent: A Human Simulation Benchmark for Individual-Level Reasoning](https://mlsanity.com/paper/2510.15144/) (2025)
- [The Sign Estimator: LLM Alignment in the Face of Choice Heterogeneity](https://mlsanity.com/paper/2510.23965/) (2025)
- [Persona-Based Simulation of Human Opinion at Population Scale](https://mlsanity.com/paper/2603.27056/) (2026)
- [From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes](https://mlsanity.com/paper/2605.16303/) (2026)
- [Graph-Based Alternatives to LLMs for Human Simulation](https://mlsanity.com/paper/2511.02135/) (2025)
- [Large Behavior Model: A Promptable Digital Twin of the Retail Customer](https://mlsanity.com/paper/2607.06993/) (2026)
- [Item-Mean Surrogates: Why Richer Persona Data Fail to Improve LLMs as Human Surrogates](https://mlsanity.com/paper/2608.29455/) (2026)
- [When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels](https://mlsanity.com/paper/2609.13148/) (2026)
- [Assessing the Human-Likeness of LLM-Driven Digital Twins in Simulating Health Care System Trust](https://mlsanity.com/paper/2512.08939/) (2025)
- [Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions](https://mlsanity.com/paper/2602.14279/) (2026)
- [Beyond Recall: Behavioral Specification as an Interpretive Layer for AI Personalization](https://mlsanity.com/paper/2605.28969/) (2026)
- [Posterior Twins: Distributional Behavioral Simulation for Enterprise Decisions](https://mlsanity.com/paper/2606.16415/) (2026)
- [Mind the Gaps: Mixture-of-Minds for Human Simulation](https://mlsanity.com/paper/2608.06115/) (2026)
- [Beyond Raw Transcripts: Structured Persona Extraction for LLM-Based Digital Twins](https://mlsanity.com/paper/2608.20344/) (2026)
- [Artificial Societies Benchmark: A Validation Framework for Synthetic Research](https://mlsanity.com/paper/2609.30030/) (2026)
- [Benchmarking Psychological Dynamics in Generative Agents](https://mlsanity.com/paper/2610.04246/) (2026)

## Abstract (arXiv)

LLM-based digital twin simulation, where large language models are used to emulate individual human behavior, holds great promise for research in AI, social science, and digital experimentation. However, progress in this area has been hindered by the scarcity of real, individual-level datasets that are both large and publicly available. This lack of high-quality ground truth limits both the development and validation of digital twin methodologies. To address this gap, we introduce a large-scale, public dataset designed to capture a rich and holistic view of individual human behavior. We survey a representative sample of N = 2,058 participants (average 2.42 hours per person) in the US across four waves with 500 questions in total, covering a comprehensive battery of demographic, psychological, economic, personality, and cognitive measures, as well as replications of behavioral economics experiments and a pricing survey. The final wave repeats tasks from earlier waves to establish a test-retest accuracy baseline. Initial analyses suggest the data are of high quality and show promise for constructing digital twins that predict human behavior well at the individual and aggregate levels. By making the full dataset publicly available, we aim to establish a valuable testbed for the development and benchmarking of LLM-based persona simulations. Beyond LLM applications, due to its unique breadth and scale the dataset also enables broad social science research, including studies of cross-construct correlations and heterogeneous treatment effects.