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Revealing Life Preferences Through LLMs / Omar Abdel Haq, Amitabh Chandra, Tomáš Jagelka, Erzo F.P. Luttmer, Joshua Schwartzstein.
- Format:
- Book
- Author/Creator:
- Abdel Haq, Omar.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w35185.
- NBER working paper series no. w35185
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2026.
- Summary:
- 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.
- Notes:
- May 2026.
- Print version record
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