Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg
Research Area: Natural Language Processing
Discipline: Social Science, Artificial Intelligence
This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.
Citations: 16
Authors: K Hackenburg, BM Tappin, L Hewitt, E Saunders
Year: 2025
Published in: Science, 2025 - science.org
Institution: London School of Economics and Political Science, Stony Brook University
Research Area: Political Persuasion with Conversational AI, Large Language Models, Factual Accuracy in AI Systems.
Discipline: Political Science, Computational Social Science
This Science paper shows that conversational AI chatbots can systematically influence political opinions at scale, and that techniques like post-training and prompting make them far more persuasive—but that increased persuasion is tied to reduced factual accuracy in what the AI says.
Citations: 12
Authors: L Hewitt, A Ashokkumar, I Ghezae, R Willer
Year: 2024
Published in: Preprint, 2024 - samim.io
Institution: Stanford University, New York University
Research Area: Social Science Experiments, Large Language Model Prediction, Large Language Models
Discipline: Computational Social Science
The study presents a framework using large language models to predict outcomes of social science field experiments, achieving 78% accuracy but facing challenges with experiments on complex social issues.
Methods: Authors used an automated framework powered by large language models to predict outcomes of 276 field experiments drawn from economics literature.
Key Findings: The prediction accuracy of large language models for outcomes of field experiments addressing various human behaviors.
Citations: 68
Sample Size: 276
