Academic Validation
At Avatar Insights, we ground our consumer research methodologies in validated academic studies to ensure precision and reliability in our findings. Our Academic Validation section highlights foundational research from thought leaders in behavioral science, language modeling, and consumer psychology. We incorporate the latest insights from esteemed journals and pioneering research institutions, such as Harvard Business School and Cambridge Core, to provide robust, scientifically-backed data for our clients. By bridging academic rigor with market insights, we empower businesses to make informed, data-driven decisions in a rapidly evolving digital landscape. Explore our validation approach to see how we elevate industry standards through continuous learning and scientific integrity.
Research Papers
Out of One, Many: Using Language Models to Simulate Human Samples
Authors: Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, and David Wingate
Published in: Political Analysis
Link: Cambridge Core
Using LLMs for Market Research
Authors: James Brand, Ayelet Israeli, and Donald Ngwe
Published by: Harvard Business School, Working Paper No. 23-062, April 2023 (Revised July 2024)
Link: Harvard Business School
Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines
Authors: Marko Sarstedt, Susanne J. Adler, Lea Rau, and Bernd Schmitt
Published in: Psychology & Marketing, February 10, 2024
Link: Wiley Online Library
Estimating the Personality of White-Box Language Models
Authors: Saketh Reddy Karra, Son The Nguyen, and Theja Tulabandhula
Available on: arXiv
Link: arXiv - Estimating the Personality of White-Box Language Models
Evaluating and Inducing Personality in Pre-trained Language Models
Authors: Guangyuan Jiang, Manjie Xu, Song-Chun Zhu, Wenjuan Han, Chi Zhang, and Yixin Zhu
Presented at: NeurIPS 2023
Link: NeurIPS
Language Models as Agent Models
Author: Jacob Andreas
Published on: arXiv, December 2022
Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
Author: John J. Horton
Published by: National Bureau of Economic Research (NBER), January 2023
Link: NBER Working Paper