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Learning to be Homo Economicus: Can an LLM Learn Preferences from Choice

Author

Listed:
  • Jeongbin Kim
  • Matthew Kovach
  • Kyu-Min Lee
  • Euncheol Shin
  • Hector Tzavellas

Abstract

This paper explores the use of Large Language Models (LLMs) as decision aids, with a focus on their ability to learn preferences and provide personalized recommendations. To establish a baseline, we replicate standard economic experiments on choice under risk (Choi et al., 2007) with GPT, one of the most prominent LLMs, prompted to respond as (i) a human decision maker or (ii) a recommendation system for customers. With these baselines established, GPT is provided with a sample set of choices and prompted to make recommendations based on the provided data. From the data generated by GPT, we identify its (revealed) preferences and explore its ability to learn from data. Our analysis yields three results. First, GPT's choices are consistent with (expected) utility maximization theory. Second, GPT can align its recommendations with people's risk aversion, by recommending less risky portfolios to more risk-averse decision makers, highlighting GPT's potential as a personalized decision aid. Third, however, GPT demonstrates limited alignment when it comes to disappointment aversion.

Suggested Citation

  • Jeongbin Kim & Matthew Kovach & Kyu-Min Lee & Euncheol Shin & Hector Tzavellas, 2024. "Learning to be Homo Economicus: Can an LLM Learn Preferences from Choice," Papers 2401.07345, arXiv.org.
    • Handle: RePEc:arx:papers:2401.07345
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    References listed on IDEAS

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    1. Federico Echenique & Taisuke Imai & Kota Saito, 2023. "Approximate Expected Utility Rationalization," Journal of the European Economic Association, European Economic Association, vol. 21(5), pages 1821-1864.
    2. Pawe{l} Niszczota & Sami Abbas, 2023. "GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice," Papers 2309.00649, arXiv.org, revised Sep 2024.
    3. Chambers,Christopher P. & Echenique,Federico, 2016. "Revealed Preference Theory," Cambridge Books, Cambridge University Press, number 9781107087804, September.
    4. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2024.
    5. Taylor Webb & Keith J. Holyoak & Hongjing Lu, 2023. "Emergent analogical reasoning in large language models," Nature Human Behaviour, Nature, vol. 7(9), pages 1526-1541, September.
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