Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions
This dataset addresses a bottleneck for researchers in AI and social science by providing a public testbed for digital twin development and validation, though it is incremental as it fills a data gap rather than proposing a new method.
The authors tackled the scarcity of large, public datasets for LLM-based digital twin simulation by introducing Twin-2K-500, a dataset of 2,058 participants with 500 questions, enabling high-quality ground truth for predicting human behavior at individual and aggregate levels.
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.