  As large language models (LLMs) like GPT become increasingly prevalent, it is
essential that we assess their capabilities beyond language processing. This
paper examines the economic rationality of GPT by instructing it to make
budgetary decisions in four domains: risk, time, social, and food preferences.
We measure economic rationality by assessing the consistency of GPT's decisions
with utility maximization in classic revealed preference theory. We find that
GPT's decisions are largely rational in each domain and demonstrate higher
rationality score than those of human subjects in a parallel experiment and in
the literature. Moreover, the estimated preference parameters of GPT are
slightly different from human subjects and exhibit a lower degree of
heterogeneity. We also find that the rationality scores are robust to the
degree of randomness and demographic settings such as age and gender, but are
sensitive to contexts based on the language frames of the choice situations.
These results suggest the potential of LLMs to make good decisions and the need
to further understand their capabilities, limitations, and underlying
mechanisms.
