  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.
