A Mega-Study of Digital Twins Reveals Strengths, Weaknesses and Opportunities for Further Improvement

• URL: http://arxiv.org/abs/2509.19088v3
• Date: Fri, 07 Nov 2025 06:14:27 GMT
• Title: A Mega-Study of Digital Twins Reveals Strengths, Weaknesses and Opportunities for Further Improvement
• Authors: Tianyi Peng, George Gui, Daniel J. Merlau, Grace Jiarui Fan, Malek Ben Sliman, Melanie Brucks, Eric J. Johnson, Vicki Morwitz, Abdullah Althenayyan, Silvia Bellezza, Dante Donati, Hortense Fong, Elizabeth Friedman, Ariana Guevara, Mohamed Hussein, Kinshuk Jerath, Bruce Kogut, Akshit Kumar, Kristen Lane, Hannah Li, Patryk Perkowski, Oded Netzer, Olivier Toubia,
• Abstract summary: Digital representations of individuals ("digital twins") promise to transform social science and decision-making.<n>We conducted 19 studies with a representative U.S. panel and their digital twins.<n>Twins reproduced individual responses with 75% accuracy and seemingly low correlation with human answers.
• Score: 3.418816254588274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital representations of individuals ("digital twins") promise to transform social science and decision-making. Yet it remains unclear whether such twins truly mirror the people they emulate. We conducted 19 preregistered studies with a representative U.S. panel and their digital twins, each constructed from rich individual-level data, enabling direct comparisons between human and twin behavior across a wide range of domains and stimuli (including never-seen-before ones). Twins reproduced individual responses with 75% accuracy and seemingly low correlation with human answers (approximately 0.2). However, this apparently high accuracy was no higher than that achieved by generic personas based on demographics only. In contrast, correlation improved when twins incorporated detailed personal information, even outperforming traditional machine learning benchmarks that require additional data. Twins exhibited systematic strengths and weaknesses - performing better in social and personality domains, but worse in political ones - and were more accurate for participants with higher education, higher income, and moderate political views and religious attendance. Together, these findings delineate both the promise and the current limits of digital twins: they capture some relative differences among individuals but not yet the unique judgments of specific people. All data and code are publicly available to support the further development and evaluation of digital twin pipelines.

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