Revealing Life Preferences Through LLMs

Omar Abdel Haq (), Amitabh Chandra (), Tomáš Jagelka (), Erzo F.P. Luttmer () and Joshua Schwartzstein ()
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Omar Abdel Haq: Harvard Business School
Amitabh Chandra: Harvard Business School & Harvard Kennedy School
Tomáš Jagelka: University of Bonn, Dartmouth College, & CREST-Ensae
Erzo F.P. Luttmer: Dartmouth College
Joshua Schwartzstein: Harvard Business School

No 410, ECONtribute Discussion Papers Series from University of Bonn and University of Cologne, Germany

Abstract: Large Language Models (LLMs) are trained on a prodigious corpus of human writing and may reveal human preferences over characteristics of life courses, such as income, longevity, and working conditions. We present OpenAI's GPT-5.4 and a broadly representative sample of Americans with pairs of life stories and ask them to choose the life they would prefer for themselves. A person's choice is better predicted by the LLM's choice than by another person’s choice over the same stories, and LLM valuations of several life attributes are similar to those derived from human responses. Our results suggest that LLM responses offer a scalable and cost-effective complement to existing methods for studying human preferences.

Keywords: Life preferences; LLMs; LLM revelation conjecture; life stories; essential life attributes; life attribute valuations (search for similar items in EconPapers)
JEL-codes: D90 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2026-05
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https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_410_2026.pdf First version, 2026 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:ajk:ajkdps:410

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