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Simulating human well-being with large language models: Systematic validation and misestimation across 64,000

Pat Pataranutaporn¹, Nattavudh Powdthavee², Chayapatr Archiwaranguprok¹

¹Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139-4307.

Proceedings of the National Academy of Sciences of the United States of America

|November 26, 2025

Summary

Large language models (LLMs) cannot accurately predict subjective well-being globally. They show biases, especially in underrepresented regions, and fail to capture cultural depth, making them unsuitable substitutes for human self-reports.

Keywords:

artificial intelligence global inequality large language models life satisfaction subjective well-being

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Area of Science:

Computational Social Science
Artificial Intelligence Ethics

Background:

Subjective well-being is crucial for economic, medical, and policy decisions.
Accurate measurement of well-being is essential for informed decision-making.

Purpose of the Study:

To evaluate the validity of large language models (LLMs) in predicting global subjective well-being.
To benchmark LLM performance against traditional statistical models and human self-reports.

Main Methods:

Utilized natural-language profiles of 64,000 individuals across 64 countries.
Compared predictions from four leading LLMs with self-reported well-being and regression models.
Conducted a preregistered experiment to assess LLM reliance on linguistic associations versus conceptual understanding.

Main Results:

LLMs produced plausible patterns but systematically underperformed compared to regression models.
LLM errors were largest in underrepresented countries, reflecting digital and economic inequalities.
LLMs demonstrated reliance on surface-level linguistic associations, leading to predictable distortions in unfamiliar contexts.

Conclusions:

LLMs can simulate broad correlates of life satisfaction but lack experiential and cultural depth.
LLMs are not suitable substitutes for human self-reports of well-being.
Using LLMs for well-being assessment risks reinforcing inequality and undermining human agency.