arXiv AI
When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability
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arXiv AI
Sep 25
AI-Moderated Interviews for Market Research and Digital Twins Calibration
AI‑moderated interviews are a scalable market‑research method that can match human moderation in depth, cover more themes, and recover more customer needs while keeping budgets constant. Participants, however, feel more emotionally engaged with live humans. Digital twins built from AI‑moderated data predict consumer responses better than demographics‑only personas, but the added richness does not improve quantitative predictions over static interviews, and prediction errors stem from differences in thinking styles and data‑distribution gaps.
arXiv Computation and Language
2d ago
SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation
SYN-DIGITS is a model‑agnostic, post‑hoc calibration framework that uses relationships learned from digital twin simulations to predict human responses, especially in cold‑start scenarios where no target question data is available. It transfers these relationships to improve individual‑level correlation and distributional predictions, even when only marginal reference data is available. Experiments on three datasets and thirteen persona constructions show significant gains—up to 417% improvement in correlation and 57% reduction in distributional error—over uncalibrated baselines.
arXiv AI
Sep 15
Synthetic Data in Marketing Research: How to Evaluate and When to Trust
The paper discusses the use of synthetic data in marketing research, arguing that the key question is not whether synthetic respondents work, but when they do. It categorizes synthetic data into three types—ungrounded LLM responses, segment-level personas, and individual-level digital twins—and maps each to the decisions they can support. The authors also propose a taxonomy of accuracy measures, highlight the forgotten question problem, and introduce an ex‑ante answerability diagnostic based on R² to improve twin-human correlation.
arXiv Machine Learning
Sep 22
Augmented Hypothesis Testing with Persona-Based LLM Simulations
arXiv:2609.24629v1 Announce Type: new Abstract: A/B testing requires large sample sizes, long timelines, and significant costs. When auxiliary predictions of experimental outcomes are available from...
arXiv AI
Jul 21
A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
arXiv:2607. 18164v1 Announce Type: cross Abstract: Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift.
arXiv Computation and Language
Aug 24
Beyond Raw Transcripts: Structured Persona Extraction for LLM-Based Digital Twins
arXiv:2608.20344v1 Announce Type: new Abstract: LLM-based "digital twins" aim to simulate how an individual would behavein new environments or respond to novel questions, given some representation of...