Revealing Life Preferences Through LLMs

Omar Abdel Haq (), Amitabh Chandra (), Jagelka, Tomáš (), Erzo Luttmer () and Joshua Schwartzstein ()
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Omar Abdel Haq: Harvard Business School
Amitabh Chandra: Harvard Business School and Harvard Kennedy School
Jagelka, Tomáš: University of Bonn
Erzo Luttmer: Dartmouth College
Joshua Schwartzstein: Harvard Business School

No 18634, IZA Discussion Papers from IZA Network @ LISER

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: generative AI; preference estimation methods; choice experiments; survey validation (search for similar items in EconPapers)
JEL-codes: D0 H0 I0 (search for similar items in EconPapers)
Date: 2026-05
New Economics Papers: this item is included in nep-ain, nep-cbe and nep-dcm
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