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

2025-05-23

AI Summary

Existing research is constrained by the scarcity of large-scale, real-world, individual-level behavioral datasets, impeding the modeling and validation of LLM-driven digital twins in AI and social science. To address this, we construct the first multidimensional longitudinal behavioral benchmark dataset comprising 2,058 U.S. participants, featuring over 500 indicators spanning demographics, psychology, economics, personality, cognition, and behavioral experiments. Rigorous methodological safeguards—including four-wave longitudinal tracking, replication of behavioral economics tasks, pricing experiments, and multi-session test–retest assessments—ensure high reliability and validity. This dataset uniquely provides high-fidelity, fully dimensional, individual-level annotations, enabling training and evaluation of digital twins at both individual-prediction and population-simulation granularities. The dataset is publicly released, substantially advancing interdisciplinary research at the intersection of AI and social science.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Lack of large public datasets for digital twin development
• Need for high-quality data to validate behavior prediction models
• Limited resources for cross-disciplinary social science research

Innovation

• LLM-based digital twin simulation
• Large-scale public behavior dataset
• Comprehensive demographic and psychological measures