40 citations · 41 across the 6 of their papers we have counts for
8 papers
AI-Moderated Interviews for Market Research and Digital Twins Calibration
Yuting Deng, Jingxuan Liu, Olivier Toubia +1
AI-moderated interviews are emerging as a scalable market-research method for generating consumer insights and building consumer "digital twins." Yet it remains unclear whether the…
ExploraTwin, a Non-Profit Research Platform for Digital Twin Simulations
Naveen Venkat, Yuchen Qiu, Tianyi Peng +2
Digital twin simulations show promise, but current empirical evidence suggests that the approach should be tested before being deployed in any particular context. To lower the fric…
Innovating with Generative AI: A Human Bottleneck Framework
Julian De Freitas, Ayelet Israeli, Gideon Nave +2
We propose a human bottleneck perspective for understanding how generative AI transforms the innovation process. The central premise is that many constraints traditionally plaguing…
Synthetic Contact with AI Reduces Cross-Partisan Animosity
Benjamin Lira, Noah Castelo, Stefano Puntoni +1
Americans' warmth toward members of the opposing political party has fallen sharply over the past three decades -- yet meaningful cross-partisan contact remains scarce, in part bec…
Examining and Addressing Barriers to Diversity in LLM-Generated Ideas
Yuting Deng, Melanie Brucks, Olivier Toubia
Ideas generated by independent samples of humans tend to be more diverse than ideas generated from independent LLM samples, raising concerns that widespread reliance on LLMs could…
Digital Twins as Funhouse Mirrors: Five Key Distortions
Tianyi Peng, George Gui, Melanie Brucks +20
Scientists and practitioners are increasingly moving to deploy digital twins--LLM-based models of real individuals--across social science and policy research. We conduct 19 pre-reg…
Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions
Olivier Toubia, George Z. Gui, Tianyi Peng +3
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 ex…
The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
George Gui, Olivier Toubia
Large Language Models (LLMs) have shown impressive potential to simulate human behavior. We identify a fundamental challenge in using them to simulate experiments: when LLM-simulat…